Unraveling the role of the secretor antigen in human rotavirus attachment to histo-blood group antigens

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Abstract

Rotavirus is the leading agent causing acute gastroenteritis in young children, with the P[8] genotype accounting for more than 80% of infections in humans. The molecular bases for binding of the VP8* domain from P[8] VP4 spike protein to its cellular receptor, the secretory H type-1 antigen (Fuc-α1,2-Gal-β1,3-GlcNAc; H1), and to its precursor lacto- N-biose (Gal-β1,3-GlcNAc; LNB) have been determined. The resolution of P[8] VP8* crystal structures in complex with H1 antigen and LNB and site-directed mutagenesis experiments revealed that both glycans bind to the P[8] VP8* protein through a binding pocket shared with other members of the P[II] genogroup (i.e.: P[4], P[6] and P[19]). Our results show that the L-fucose moiety from H1 only displays indirect contacts with P[8] VP8*. However, the induced conformational changes in the LNB moiety increase the ligand affinity by two-fold, as measured by surface plasmon resonance (SPR), providing a molecular explanation for the different susceptibility to rotavirus infection between secretor and non-secretor individuals. The unexpected interaction of P[8] VP8* with LNB, a building block of type-1 human milk oligosaccharides, resulted in inhibition of rotavirus infection, highlighting the role and possible application of this disaccharide as an antiviral. While key amino acids in the H1/LNB binding pocket were highly conserved in members of the P[II] genogroup, differences were found in ligand affinities among distinct P[8] genetic lineages. The variation in affinities were explained by subtle structural differences induced by amino acid changes in the vicinity of the binding pocket, providing a fine-tuning mechanism for glycan binding in P[8] rotavirus.

Author summary

The interaction of viruses with host glycans has become an important topic in the study of enteric virus infectivity. This interaction modulates several aspects of the viral cycle, including viral attachment, which in most cases depends on the host glycobiology dictated by the secretor and Lewis genotypes. The capacity to synthesize secretory type-I antigen H (fucose-α1,2-galactose-β1,3-N-acetylglucosamine; H1) at the mucosae, dictated by the presence of one or two functional copies of the fucosyl-transferase FUT2 gene (secretor status), has been clearly linked to infectivity in important enteric viruses such as the noroviruses. However, a big controversy existed about the contribution of H1 antigen to infection in the leading cause of viral gastroenteritis in young children (rotavirus). It has not been until recently that epidemiological data evidenced a diminished incidence of rotavirus in non-secretor individuals unable to produce H1. In the present manuscript we offer the evidence that P[8] RV bind H1 via a binding site common for the P[II] RV genogroup and that the H1 precursor lacto- N-biose (galactose-β1,3- N-acetylglucosamine; LNB) is also bound to this pocket with diminished affinity. The P[8] VP8* structures show a marginal role for the L-fucose moiety from H1 in protein interaction. However, its presence provides conformational changes in the LNB moiety that increase the affinity of VP8* for the H1 ligand and would account for a stronger RV binding to mucosa in individuals expressing H1 (secretors). We thus offer a mechanistic explanation for the different incidence of P[8] RV infection in different secretor phenotypes.

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Features and development of Coot

P Emsley, B Lohkamp, W. Scott … (2010)

1. Introduction Macromolecular model building using X-ray data is an interactive task involving the iterative application of various optimization algorithms with evaluation of the model and interpretation of the electron density by the scientist. Coot is an interactive three-dimensional molecular-modelling program particularly designed for the building and validation of protein structures by facilitating the steps of the process. In recent years, initial construction of the protein chain has often been carried out using automatic model-building tools such as ARP/wARP (Langer et al., 2008 ▶), SOLVE/RESOLVE (Wang et al., 2004 ▶) and more recently Buccaneer (Cowtan, 2006 ▶). In consequence, relatively more time and emphasis is placed on model validation than has previously been the case (Dauter, 2006 ▶). The refinement and validation steps become increasingly important and also more time-consuming with lower resolution data. Coot aims to provide access to as many of the tools required in the iterative refinement and validation of a macromolecular structure as possible in order to facilitate those aspects of the process which cannot be performed automatically. A primary design goal has been to make the software easy to learn in order to provide a low barrier for scientists who are beginning to work with X-ray data. While this goal has not been met for every feature, it has played a major role in many of the design decisions that have shaped the software. The principal tasks of the software are the visualization of macromolecular structures and data, the building of models into electron density and the validation of existing models; these will be considered in the next three sections. The remaining sections of the paper will deal with more technical aspects of the software, including interactions with external software, scripting and testing. 2. Program design The program is constructed from a range of existing software libraries and a purpose-written Coot library which provides a range of tools specific to model building and visualization. The OpenGL and other graphics libraries, such as the X Window System and GTK+, provide the graphical user-interface functionality, the GNU Scientific Library (GSL) provides mathematical tools such as function minimizers and the Clipper (Cowtan, 2003 ▶) and MMDB (Krissinel et al., 2004 ▶) libraries provide crystallographic tools and data types. On top of these tools are the Coot libraries, which are used to manipulate models and maps and to represent them graphically. Much of this functionality may be accessed from the scripting layer (see §8), which allows programmatic access to all of the underlying functionality. Finally, the graphical user interface is built on top of the scripting layer, although in some cases it is more convenient for the graphical user interface to access the underlying classes directly (Fig. 1 ▶). 3. Visualization Coot provides tools for the display of three-dimensional data falling into three classes. (i) Atomic models (generally displayed as vectors connecting bonded atoms). (ii) Electron-density maps (generally contoured using a wire-frame lattice). (iii) Generic graphical objects (including the unit-cell box, noncrystallographic rotation axes and similar). A user interface and a set of controls allow the user to interact with the graphical display, for example in moving or rotating the viewpoint, selecting the data to be displayed and the mode in which those data are presented. The primary objective in the user interface as it stands today has been to make the application easy to learn. Current design of user interfaces emphasizes a number of characteristics for a high-quality graphical user interface (GUI). Such characteristics include learnability, productivity, forgiveness (if a user makes a mistake, it should be easy to recover) and aesthetics (the application should look nice and provide a pleasurable experience). When designing the user interface for Coot, we aim to respect these issues; however, this may not always be achieved and the GUI often undergoes redesign. Ideally, a user who has a basic familiarity with crystallographic data but who has never used Coot before should be able to start the software, display their data and perform some basic manipulations without any instruction. In order for the software to be easy to learn, it is necessary that the core functionality of the software be discoverable, i.e. the user should be able to find out how to perform common tasks without consulting the documentation. This may be achieved in any of three ways. (i) The behaviour is intuitive, i.e. the behaviour of user-interface elements can be either anticipated or determined by a few experiments. An example of this is the rotation of the view, which is accomplished by simply dragging with the mouse. (ii) The behaviour is familiar and consistent, i.e. user-interface elements behave in a similar way to other common software. An example of this is the use of a ‘File’ menu containing ‘Open…’ options, leading to a conventional file-selection dialogue. (iii) The interface is explorable, i.e. if a user needs an additional functionality they can find it rapidly by inspecting the interface. An example of this is the use of organized menus which provide access to the bulk of the program functionality. Furthermore, tooltips are provided for most menus and buttons and informative widgets explain their function. 3.1. User interface The main Coot user interface window is shown in Fig. 2 ▶ and consists of the following elements. (i) In the centre of the main window is the three-dimensional canvas, on which the atomic models, maps and other graphical objects are displayed. By default this area has a black background, although this can be changed if desired. (ii) At the top of the window is a menu bar. This includes the following menus: ‘File’, ‘Edit’, ‘Calculate’, ‘Draw’, ‘Measures’, ‘Validate’, ‘HID’, ‘About’ and ‘Extensions’. The ‘File’, ‘Edit’ and ‘About’ menus fulfill their normal roles. ‘Calculate’ provides access to model-manipulation tools. ‘Draw’ implements display options. ‘Measures’ presents access to geometrical information. ‘Validate’ provides access to validation tools. ‘HID’ allows the human-interface behaviour to be customized. ‘Extensions’ provides access to a range of optional functionalities which may be customized and extended by advanced users. Additional menus can be added by the use of the scripting interface. (iii) Between the menu bar and the canvas is a toolbar which provides two very frequently used controls: ‘Reset view’ switches between views of the molecules and ‘Display Manager’ opens an additional window which allows individual maps and molecules to be displayed in different ways. This toolbar is customizable, i.e. additional buttons can be added. (iv) On the right-hand side of the window is a toolbar of icons which allow the modification of atomic models. By default these are displayed as icons, although tooltips are provided and text can also be displayed. (v) Below the canvas is a status bar in which brief text messages are displayed concerning the status of current operations. The user interface is implemented using the GTK+2 widget stack, although with some work this could be changed in the future. 3.2. Controls User input to the program is primarily via mouse and keyboard, although it is also possible to use some dial devices such as the ‘Powermate’ dial. The mouse may be used to select menu options and toolbar buttons in the normal way. In addition, the mouse and the keyboard may be used to manipulate the view in the three-dimensional canvas using the controls shown in Fig. 3 ▶. In a large program there is often tension between software being easy to learn and being easy to use. A program which is easy to use provides extensive shortcuts to allow common tasks to be performed with the minimum user input. Keyboard shortcuts, customizations and macro languages are common examples and are often employed by expert users of all types of software. Coot now provides tools for all of these. Much of the functionality of the package is now accessible from both the Python (http://www.python.org) and the Scheme (Kelsey et al., 1998 ▶) scripting languages, which may be used to construct more powerful tools using combinations of existing functions. One example is a function often used after molecular replacement which will step through every residue in a protein, replace any missing atoms, find the best-fitting side-chain rotamer and perform real-space refinement. This function is in turn bound to a menu item, although it would also be possible to bind it to a key on the keyboard. 3.3. Lighting model The lighting model used in Coot is a departure from the approach adopted in most molecular-graphics software. It is difficult to illustrate a three-dimensional shape in a two-dimensional representation of an object. The traditional approach is to use so-called ‘depth-cueing’: objects closer to the user appear more brightly lit and more distant objects are more like the background colour (usually darker). In the Coot model, however, the most brightly lit features are just forward of the centre of rotation. This innovation was accidental, but has been retained because it seemed to provide a more natural image and has generated positive feedback from users once they become accustomed to the new behaviour. It is now possible to offer an explanation for this result. Depth-cueing is an algorithm which adjusts the colours of graphical objects according to their distance from the viewer. Depth-cueing is used in several ways. When rendering outdoor scenes, it is used to wash out the colours of distant features to simulate the effect of light scattering in the intervening air. When rendering darkened scenes, the same effect can be used to darken distant objects in order to create the effect that the viewer is carrying a light source which illuminates nearer objects more brightly than distant ones. Note that both of these usages assume a ‘first-person’ view: the observer is placed within the three-dimensional environment. This is also borne out in the controls for manipulating the view: when the view is rotated, the whole environment usually rotates about the observer. However, fitting three-dimensional atomic models to X-ray data is a different situation. It is not useful to place the observer inside the model and rotate the model around them, not least because the scientist is usually more interested in looking at the molecule or electron density from the outside. As a result, it is normal to rotate the view not about the observer but rather about the centre of the feature being studied. Since the central feature is of most interest, it helps the visualization if it is the brightest entity. To properly light the model in this way is relatively slow, so in Coot an approximation is used and the plane perpendicular to the viewer that contains the central feature is most brightly lit. 3.4. Atomic model Coot displays the atoms of the atomic models as points on the three-dimensional canvas. If the points are within bonding distance then a line symbolizing a bond is drawn between the atomic points; otherwise the atoms are displayed as crosses. By default the atoms are coloured by element, with carbon yellow, oxygen red, nitrogen blue, sulfur green and hydrogen white. Bonds have two colours, with one half corresponding to each connecting atom. Additional atomic models are distinguished by different colour coding. The colour wheel is rotated and the element colours are adjusted accordingly. However, there is an option to fix the colours for noncarbon elements and the colour-wheel position can be adjusted for each molecule individually. Furthermore, Coot allows the user to colour the atomic model by molecule, chain, secondary structure, B factor and occupancy. Besides showing atomic models, Coot can also display Cα (or backbone) chains only. Again the model can be coloured in different modes, by chain, secondary structure or with rainbow colours from the N-terminus to the C-terminus. Currently, Coot offers some additional atomic representations in the form of different bond-width or ball-and-stick representation for selected residues. Information about individual atoms can be visualized in the form of labels. These show the atom name, residue number, residue name and chain identifier. Labels are shown upon Shift + left mouse click or double left mouse click on an atom (the atom closest to the rotation/screen centre can be labelled using the keyboard shortcut ‘l’). This operation not only shows the label beside the atom in the three-dimensional canvas, but also gives more detailed information about the atom, including occupancy, B factor and coordinates, in the status bar. Symmetry-equivalent atoms of the atomic model can be displayed in Coot within a certain radius either as whole chains or as atoms within this radius. Different options for colouring and displaying atoms or Cα backbone are provided. The symmetry-equivalent models can be labelled as described above. Additionally, the label will provide information about the symmetry operator used to generate the selected model. Navigation around the atomic models is primarily achieved with a GUI (‘Go To Atom…’). This allows the view to be centred on a particular atom by selection of a model, chain ID, residue number and atom name. Buttons to move to the next or previous residue are provided and are also available via keyboard shortcuts (space bar and Shift space bar, respectively). Furthermore, each chain is displayed as an expandable tree of its residues, with atoms that can be selected for centring. Additionally, a mouse can be used for navigation, so a middle mouse click centres on the clicked atom. A keyboard shortcut for the view to be centred on a Cα atom of a specific residue is provided by the use of Ctrl-g followed by input of the chain identifier and residue number (terminated by Enter). All atomic models, in contrast to other display objects, are accessible by clicking a mouse button on an atom centre. This allows, for example, re-centring, selection and labelling of the model. 3.5. Electron density Electron-density maps are displayed using a three-dimensional mesh to visualize the surface of electron-density regions higher than a chosen electron-density value using a ‘marching-cubes’-type algorithm (Lorensen & Cline, 1987 ▶). The spacing of the mesh is dictated by the spacing of the grid on which the electron density is sampled. Since electron-density maps are most often described in terms of structure factors, the sampling can be modified by the user at the point where the electron density is read into the program. The contour level may be varied interactively using the scroll wheel on the mouse (if available) or alternatively by using the keyboard (‘+’ and ‘-’). In most cases this avoids the need for multiple contour levels to be displayed at once, although additional contour levels can be displayed if desired. The colour of the electron-density map may be selected by the user. By default, the first map read into the program is contoured in blue, with subsequent maps taking successive colour hues around a colour wheel. Difference maps are by default contoured at two levels, one positive and one negative (coloured green and red, respectively). The electron density is contoured in a box about the current screen centre and is interactively re-contoured whenever the view centre is changed. By default, this box covers a volume extending at least 10 Å in each direction from the current screen centre. This is an appropriate scale for manipulating individual units of a peptide or nucleotide chain and provides good interactive performance, even on older computers. Larger volumes may be contoured on faster machines. A ‘dynamic volume’ option allows the volume contoured to be varied with the current zoom level, so that the contoured region always fills the screen. A ‘dynamic sampling’ option allows the map to be contoured on a subsampled grid (e.g. every second or fourth point along each axis). This is useful when using a solvent mask to visualize the packing of the molecules in the crystal. 3.6. Display objects There are a variety of non-interactive display objects which can also be superimposed on the atomic model and electron density. These include the boundaries of the unit cell, an electron-density ridge trace (or skeleton), surfaces, three-dimensional text annotations and dots (used in the MolProbity interface). These cannot be selected, but aid in the visualization of features of the electron density and other entities. 3.7. File formats Coot recognizes a variety of file formats from which the atomic model and electron density may be read. The differences in the information stored in these various formats mean that some choices have to be made by the user. This is achieved by providing several options for reading electron density and, where necessary, by requesting additional information from the user. The file formats which may be used for atomic models and for electron density will be considered in turn. In addition to obtaining data from the local storage, it is also possible to obtain atomic models directly from the Protein Data Bank (Bernstein et al., 1977 ▶) by entering the PDB code of a deposited structure. Similarly, in the case of structures for which experimental data have been deposited, the model and phased reflections may both be obtained from the Electron Density Server (Kleywegt et al., 2004 ▶). 3.7.1. Atomic models Atomic models are read into Coot by selecting the ‘Open Coordinates…’ option from the File menu. This provides a standard file selector which may be used to select the desired file. Coot recognizes atomic models stored in the following three formats. (i) Protein Data Bank (PDB) format (with file extension .pdb or .ent; compressed files of this format with extension .gz can also be read). The latest releases provide compatibility with version 3 of the PDB format. (ii) Macromolecular crystallographic information file (mmCIF; Westbrook et al., 2005 ▶) format (extension .cif). (iii) SHELX result files produced by the SHELXL refinement software (extension .res or .ins). In each case, the unit-cell and space-group information are read from the file (in the case of SHELXL output the space group is inferred from the symmetry operators). The atomic model is read, including atom name, alternate conformation code, monomer name, sequence number and insertion code, chain name, coordinates, occupancy and isotropic/anisotropic atomic displacement parameters. PDB and mmCIF files are handled using the MMDB library (Krissinel et al., 2004 ▶), which is also used for internal model manipulations. 3.7.2. Electron density The electron-density representation is a significant element of the design of the software. Coot employs a ‘crystal space’ representation of the electron density, in which the electron density is practically infinite in extent, in accordance with the lattice repeat and cell symmetry of the crystal. Thus, no matter where the viewpoint is located in space density can always be represented. This design decision is achieved by use of the Clipper libraries (Cowtan, 2003 ▶). The alternative approach is to just display electron density in a bounded box described by the input electron-density map. This approach is simpler and may be more appropriate in some specific cases (e.g. when displaying density from cryo-EM experiments or some types of NCS maps). However, it has the limitation that no density is available for symmetry-related molecules and if the initial map has been calculated with the wrong extent then it must be recalculated in order to view the desired regions. This distinction is important in that it affects how electron-density data should be prepared for use in Coot. Files prepared for O or PyMOL may not be suitable for use in Coot. In order to read a map file into Coot, it should cover an asymmetric unit or unit cell. In contrast, map files prepared for O (Jones et al., 1991 ▶) or PyMOL (DeLano, 2002 ▶) usually cover a bounded box surrounding the molecule. While it is possible to derive any bounded box from the asymmetric unit, it is not always possible to go the other way; therefore, using map files prepared for other software may lead to unexpected results in some cases, the most common being an incorrect calculation of the standard deviation of the map. If one uses more advanced techniques that involve masking, the electron-density map must have the same symmetry as the associated model molecule. Electron density may be read into Coot either in the form of structure factors (with optional weights) and phases or alternatively in the form of an electron-density map. There are a number of reasons why the preferred approach is to read reflection data rather than a map. (i) Coot can always obtain a complete asymmetric unit of data, avoiding the problems described above. (ii) Structure-factor files are generally smaller than electron-density maps. (iii) Some structure-factor files, and in particular MTZ files, provide multiple sets of data in a single file. Thus, it is possible to read a single file and obtain, for example, both best and difference maps. The overhead in calculating an electron-density map by FFT is insignificant for modern computers. 3.7.3. Reading electron density from a reflection-data file Two options are provided for reading electron density from a reflection-data file. These are ‘Auto Open MTZ…’ and ‘Open MTZ, mmcif, fcf or phs…’ from the ‘File’ menu. (i) ‘Auto Open MTZ…’ will open an MTZ file containing coefficients for the best and difference map, automatically select the FWT/PHWT and the DELFWT/DELPHWT pairs of labels and display both electron-density maps. Currently, suitable files are generated by the following software: Phaser (Storoni et al., 2004 ▶), REFMAC (Murshudov et al., 1997 ▶), phenix.refine (Adams et al., 2002 ▶), DM (Zhang et al., 1997 ▶), Parrot (Cowtan, 2010 ▶), Pirate (Cowtan, 2000 ▶) and BUSTER (Blanc et al., 2004 ▶). (ii) ‘Open MTZ, mmcif, fcf or phs…’ will open a reflection-data file in any of the specified formats. Note that XtalView .phs files do not contain space-group and cell information: in these cases a PDB file must be read first to obtain the relevant information or the information has to be entered manually. MTZ files may contain many sets of map coefficients and so it is necessary to select which map coefficients to use. In this case the user is provided with an additional window which allows the map coefficients to be selected. The standard data names for some common crystallographic software are provided in Table 1 ▶. SHELX .fcf files are converted to mmCIF format and the space group is then inferred from the symmetry operators. 4. Model building Initial building of protein structures from experimental phasing is usually accomplished by automated methods such as ARP/wARP, RESOLVE (Wang et al., 2004 ▶) and Buccaneer (Cowtan, 2006 ▶). However, most of these methods rely on a resolution of better than 2.5 Å and yield more complete models the better the resolution. The main focus in Coot, therefore, is the completion of initial models generated by either molecular replacement or automated model building as well as building of lower resolution structures. However, the features described below are provided for cases where an initial model is not available. 4.1. Tools for general model building 4.1.1. Cα baton mode Baton building, which was introduced by Kleywegt & Jones (1994 ▶), allows a protein main chain to be built by using a 3.8 Å ‘baton’ to position successive Cα atoms at the correct spacing. In Coot, this facility is coupled with an electron-density ridge-trace skeleton (Greer, 1974 ▶). Firstly, a skeleton is calculated which follows the ridges of the electron density. The user then selects baton-building mode, which places an initial baton with one end at the current screen centre. Candidate positions for the next α-carbon are highlighted as crosses selected from those points on the skeleton which lie at the correct distance from the start point. The user can cycle through a list of candidate positions using the ‘Try Another’ button or alternatively rotate the baton freely by use of the mouse. Additionally, the length of the baton can be changed to accommodate moderate shifts in the α-carbon positions. Once a new position is accepted, the baton moves so that its base is on the new α-carbon. In this way, a chain may be traced manually at a rate of between one and ten residues per minute. 4.1.2. Cα zone→main chain Having placed the Cα atoms, the rest of the main-chain atoms may be generated automatically. This tool uses a set of 62 high-resolution structures as the basis for a library of main-chain fragments. Hexapeptide and pentapeptide fragments are chosen to match the Cα positions of each successive pentapeptide of the Cα trace in turn, following the method of Esnouf (1997 ▶), which is similar to that of Jones & Thirup (1986 ▶). The fragments with the best fit to the candidate Cα positions are merged to provide a full trace. After this step, one typically performs a real-space refinement of the subsequent main-chain model. 4.1.3. Find secondary structure Protein secondary-structure elements, including α-helices and β-strands, can be located by their repeating electron-density features, which lead to high and low electron-density values in characteristic positions relative to the consecutive Cα atoms. The ‘Find Secondary Structure’ tool performs a six-dimensional rotation and translation search to find the likely positions of helical and strand elements within the electron density. This search has been highly optimized in order to achieve interactive performance for moderately sized structures and as a result is less exhaustive than the corresponding tools employed in automated model-building packages: however, it can provide a very rapid indication of map quality and a starting point for model building. 4.1.4. Place helix here At low resolution it is sometimes possible to identify secondary-structure features in the electron density when the Cα positions are not obvious. In this case, Coot can fit an α-helix automatically. This process involves several stages. (i) A local optimization is performed on the starting position to maximize the integral of the electron density over a 5 Å sphere. This tends to move the starting point close to the helix axis. (ii) A search is performed to obtain the direction of the helix by integrating the electron density in a cylinder of radius 2.5 Å and length 12 Å. A two-dimensional orientation search is performed to optimize the orientation of the cylinder. This gives the direction of the helix. (iii) A theoretical α-helical model (including C, Cα, N and O atoms) is placed in the density in accordance with the position and direction already found. Different rotations of the model around the helix axis must be considered. Each of the resulting models is scored by the sum of the density at the atomic centres. At this stage the direction of the helix is unknown and so both directions are tested. (iv) Next, a choice is made between the best-fitting models for each helix direction by comparing the electron density at the Cβ positions. In case neither orientation gives a significant better fit for the Cβ atoms, both helices are presented to the user. (v) Finally, attempts are made to extend the helix from the N- and C-termini using ideal ϕ, ψ values. 4.1.5. Place strand here A similar method is used for placing β-strand fragments in electron density. However, there are three differences compared with helix placement: firstly the initial step is omitted, secondly the length of the fragment (number of residues) needs to be provided by the user and finally the placed fragments are obtained from a database. The first step (optimizing the starting position) is unreliable for strands owing to the smaller radius of the cylinder, i.e. main chain, combined with larger density deviations originating from the side chains. Hence, it is omitted and the user must provide a starting position in this case. The integration cylinder used in determining the orientation of the strand has a radius of 1 Å and a length of 20 Å. The ϕ, ψ torsion angles in β-strands in protein deviate from the ideal values, resulting in curved and twisted strands. Such strands cannot be well modelled using ideal values of ϕ and ψ; therefore, candidate strand fragments corresponding to the requested length are taken from a strand ‘database’ (top100 or top500; Word, Lovell, LaBean et al., 1999 ▶) and used in the search. 4.1.6. Ideal DNA/RNA Coot has a function to generate idealized atomic structures of single or double-stranded A-form or B-form RNA or DNA given a nucleotide sequence. The function is menu-driven and can produce any desired helical nucleic acid coordinates in PDB format with canonical Watson–Crick base pairing from a given input sequence with the click of a single button. Because most DNA and RNA structures are comprised of at least local regions of regular near-ideal helical structural elements, the ability to generate nucleic acid helical models on the fly is of particular value for molecular replacement. Recently, a collection of short ideal A-form RNA helical fragments generated within Coot were used to solve a structurally complex ligase ribozyme by molecular replacement (Robertson & Scott, 2008 ▶). Using Coot together with the powerful molecular-replacement program Phaser (Storoni et al., 2004 ▶) not only permitted this novel RNA structure to be solved without resort to heavy-atom methods, but several other RNA and RNA/protein complexes were also subsequently determined using this approach (Robertson & Scott, 2007 ▶). Since Coot and Phaser can be scripted using embedded Python components, an automated and integrated phasing system is amenable for development within the current software framework. 4.1.7. Find ligands The automatic fitting of ligands into electron-density maps is a frequently used technique that is particularly useful for pharmaceutical crystallographers (see, for example, Williams et al., 2005 ▶). The mechanism in Coot addresses a number of ligand-fitting scenarios and is a modified form of a previously described algorithm (Oldfield, 2001 ▶). It is common practice in ‘fragment screening’ to soak different ligands into the same crystal (Blundell et al., 2002 ▶). Using Coot one can either specify a region in space or search a whole asymmetric unit for either a single or a number of different ligand types. In the ‘whole-map’ scenario, candidate ligand sites are found by cluster analysis of a residual map. The candidate ligands are fitted in turn to each site (with the candidate orientations being generated by matching the eigenvectors of the ligand to that of the cluster). Each candidate ligand is fitted and scored against the electron density. The best-fitting orientation of the ligand candidates is chosen. Ligands often contain a number of rotatable bonds. To account for this flexibility, Coot samples torsion angles around these rotatable bonds. Here, each rotatable bond is sampled from an independent probability distribution. The number of conformers is under user control and it is recommended that ligands with a higher number of rotatable bonds should be allowed more conformer candidates. Above a certain number of rotatable bonds it is more efficient to use a ‘core + fragment by fragment’ approach (see, for example, Terwilliger et al., 2006 ▶). 4.2. Rebuilding and refinement The rebuilding and refinement tools are the primary means of model manipulation in Coot and are all grouped together in the ‘Model/Fit/Refine’ toolset. These tools may be accessed either through a toolbar (which is usually docked on the right-hand side of the main window) or through a separate ‘Model/Fit/Refine’ window containing buttons for each of the toolbar functions. The core of the rebuilding and refinement tools is the real-space refinement (RSR) engine, which handles the refinement of the atomic model against an electron-density map and the regularization of the atomic model against geometric restraints. Refinement may be invoked both interactively, when executed by the user, and non-interactively as part of some of the automated fitting tools. The refinement and regularization tools are supplemented by a range of additional tools aimed at assisting the fitting of protein chains. These features are discussed below. 4.3. Tools for moving existing atoms 4.3.1. Real-space refine zone The real-space refine tool is the most frequently used tool for the refinement and rebuilding of atomic models and is also incorporated as a final stage in a number of other tools, e.g. ‘Add Terminal Residue…’. In interactive mode, the user selects the RSR button and then two atoms bounding a range of monomers (amino acids or otherwise). Alternatively, a single atom can be selected followed by the ‘A’ key to refine a monomer and its neighbours. All atoms in the selected range of monomers will be refined, including any flanking residues. Atoms of the flanking residues are marked as ‘fixed’ but are required to be added to the refinement so that the geometry (e.g. peptide bonds, angles and planes) between fixed and moving parts is also optimized. The selected atoms are refined against a target consisting of two terms: the first being the atomic number (Z) weighted sum of the electron-density values over all the atomic centres and the second being the stereochemical restraints. The progress of the refinement is shown with a new set of atoms displayed in white/pale colours. When convergence is reached the user is shown a dialogue box with a set of χ2 scores and coloured ‘traffic lights’ indicating the current geometry scores in each of the geometrical criteria (Fig. 4 ▶). Additionally, a warning is issued if the refined range contains any new cis-peptide bonds. At this stage the user may adjust the model by selecting an atom with the mouse and dragging it, whereby the other atoms will move with the dragged atom. Alternatively, a single atom may be dragged by holding the Ctrl key. As soon as the atoms are released, the selected atoms will refine from the dragged position. Optionally, before the start of refinement atoms may be selected to be fixed during the refinement (in addition to the atoms of the flanking residues). 4.3.2. Sphere refinement One of the problems with the refinement mode described above is that it only considers a linear range of residues. This can cause difficulties, with some side chains being inappropriately refined into the electron density of neighbouring residues, particularly at lower resolutions. Additionally, a linear residue selection precludes the refinement of entities such as disulfide bonds. Therefore, a new residue-selection mechanism was introduced to address these issues: the so-called ‘Sphere Refinement’. This mode selects residues that have atoms within a given radius of a specified position (typically within 4 Å of the centre of the screen). The selected residues are matched to the dictionary and any user-defined links (typically from the mon_lib_list.cif in the REFMAC dictionary), e.g. disulfide bonds, glycosidic linkages and formylated lysines. If such links are found and the (supposedly) bonded atoms are within 3 Å of each other then these extra link restraints are added into the refinement. 4.3.3. Ramachandran restraints At lower resolution it is sometimes difficult to obtain an acceptable fit of the model to the density and at the same time achieve a Ramachandran plot of high quality (most residues in favourable regions and less than 1% outliers). If a Ramachandran score is added to the target function then the Ramachandran plot can be improved. The analytical form for torsion gradients (∂θ/∂x 1 and so on) for each of the x, y, z positions of the four atoms contributing to the torsion angle has been reported previously (Emsley & Cowtan, 2004 ▶) (in the case of Ramachandran restraints, the θ torsions will be ϕ and ψ). The extension of the torsion gradients for use as Ramachandran restraints is performed in the following manner. Firstly, two-dimensional log Ramachandran plots R are generated as tables (one for each of the residue types Pro, Gly and non-Pro or Gly). Where the Ramachandran probability becomes zero the log probability becomes infinite and so it is replaced by values which become increasingly negative with distance from the nearest nonzero value. This provides a weak gradient in the disallowed regions towards the nearest allowed region. The log Ramachandran plot provides the following values and derivatives: The derivative of R with respect to the coordinates is required for the addition into the target geometry and is generated as (and so on for each of the x, y, z positions of the atoms in the torsion). Adding a Ramachandran score to the geometry target function is not without consequences. The Ramachandran plot has for a long time been used as a validation criterion, therefore if it is used in geometry optimization it becomes less informative as a validation metric. Kleywegt & Jones (1996 ▶) included the Ramachandran plot in the restraints during refinement using X-PLOR (Brünger, 1992 ▶) and reported that the number of Ramachandran outliers was reduced by about a third using moderate force constants. However, increasing the force constants by over two orders of magnitude only marginally decreased the number of outliers. As a result, Kleywegt and Jones note that the Ramachandran plot retains significant value as a validation tool even when it is also used as a restraint. Using the Ramachandran restraints as implemented in Coot with the default weights, the number of outliers can be reduced from around 10% to 5% (typical values). 4.3.4. Regularize zone The ‘Regularize Zone’ option functions in the same way as ‘Real-Space Refine Zone’ except that in this case the model is refined with respect to stereochemical restraints but without reference to any electron density. 4.3.5. Rigid-body fit zone The ‘Rigid-Body Fit Zone’ option also follows a similar interface convention to the other refinement options. A range of atoms are selected and the orientation of the selected group is refined to best fit the density. In this case the density is the only contributor to the target function, since the geometry of the fragment is not altered. No constraints are placed on the bonding atoms. If atoms are dragged after refinement, no further refinement is performed on the fragment. 4.3.6. Rotate/translate zone Using this tool, the selected residue selection can be translated and rotated either by dragging it around the screen or through the use of user-interface sliders. No reference to the map is made. The rotation centre can be specified to be either the last atom selected or the centre of mass of the fragment rotated. Additionally, a selection of the whole chain or molecule can be transformed. 4.3.7. Rotamer tools Four tools are available for the fitting of amino-acid side chains. For a side chain whose amino-acid type is already correctly assigned, the best rotamer may be chosen to fit the density either automatically or manually. If the automatic option is chosen then the side-chain rotamer from the MolProbity library (Lovell et al., 2000 ▶) which gives rise to the highest electron-density values at the atomic centres is selected and rigid-body refined (this includes the main-chain atoms of the residues). Otherwise, the user is presented with a list of rotamers for that side-chain type sorted by frequency in the database. The user can then scroll through the list of rotamers using either the keyboard or user-interface buttons to select the desired rotamer. Rotamers are named according to the MolProbity system. Briefly, the χ angles are given letters according to the torsion angle: ‘t’ for approximately 180°, ‘p’ for approximately 60° and ‘m’ for approximately −60° (Lovell et al., 2000 ▶). The other two options (‘Mutate & Auto Fit’ and ‘Simple Mutate’) allow the amino-acid type to be assigned or changed. The ‘Mutate & Auto Fit Rotamer’ option allows an amino-acid type to be selected from a list and then immediately performs the autofit rotamer operation as above. The ‘Simple Mutate’ option changes the amino-acid type and builds the side-chain atoms in the most frequently occurring rotamer without further refinement. 4.3.8. Torsion editing (‘Edit Chi Angles’, ‘Edit Backbone Torsions’, ‘Torsion General’) Coot has different tools for editing the main-chain and side-chain (or ligand) torsion angles. The main-chain torsion angles, namely ϕ and ψ, can be edited using ‘Edit Backbone Torsion…’. With two sliders, the peptide and carbonyl torsion angles can be adjusted. A separate window showing the Ramachandran plot with the two residues forming the altered peptide bond is displayed with the position of the residues updated as the angles change. Side-chain (or ligand) torsion angles must be defined prior to editing. Either the user manually defines the four atoms forming the torsion angle (‘Torsion General’) or the torsion angles are determined automatically and the user selects the one to edit. In the latter case the bond around which the selected torsion angle is edited is visually marked. Using the mouse, the angle can then be rotated freely. 4.3.9. Other protein tools (‘Flip peptide’, ‘Side Chain 180° Flip’, ‘Cis→Trans’) There are three other tools to perform common corrections to protein models. ‘Flip peptide’ rotates the planar atoms in a peptide group through 180° about the vector joining the bounding Cα atoms (Jones et al., 1991 ▶). ‘Side Chain 180° Flip’ rotates the last torsion of a side chain through 180° (e.g. to swap the OD1 and ND2 side-chain atoms of Asn). ‘Cis→Trans’ shifts the torsion of the peptide bond through 180°, thereby changing the peptide bond from trans to cis and vice versa. 4.4. Tools for adding atoms to the model 4.4.1. Find waters The water-finding mechanism in Coot uses the same cluster analysis as is used in ligand fitting. However, only those clusters below a certain volume (by default 4.2 Å3) are considered as candidate sites for water molecules. The centre of each cluster is computed and a distance check is then made to the potential hydrogen-bond donors or receptors in the protein molecule (or other waters). The distance criteria for acceptable hydrogen-bond length are under user control. Additionally, a test for acceptable sphericity of the electron density is performed. 4.4.2. Add terminal residue The MolProbity ϕ, ψ distribution is used to generate a set of randomly selected ϕ, ψ pairs. To build additional residues at the N- and C-termini of protein chains, the MolProbity ϕ, ψ distribution is used to generate a set of positions of the N, Cα, O and C atoms of the next two residues. The conformation of these new atoms is then scored against the electron-density map and recorded. This procedure is carried out a number of times (by default 100). The best-fitting conformation is offered as a candidate to the user (only the nearest of the two residues is kept). 4.4.3. Add alternate conformation Alternate conformations are generated by splitting the residue into two sets of conformations (A and B). By default all atoms of the residue are split, or alternatively only the Cα and side-chain atoms are divided. If the residue chosen is a standard protein residue then the rotamer-selection dialogue described above is also shown, along with a slider to specify the occupancy of the new conformation. 4.4.4. Place atom at pointer This is a simple interface to place a typed atom at the position of the centre of the screen. It can place additional water or solvent molecules in unmodelled electron-density peaks and is used in conjunction with the ‘Find blobs’ tool, which allows the largest unmodelled peaks to be visited in turn. 4.5. Tools for handling noncrystallographic symmetry (NCS) Noncrystallographic symmetry (NCS) can be exploited during the building of an atomic model and also in the analysis of an existing model. Coot provides five tools to help with the building and visualization of NCS-related molecules. (i) NCS ghost molecules. In order to visualize the similarities and differences between NCS-related molecules, a ‘ghost’ copy of any or all NCS-related chains may be superimposed over a specific chain in the model. The ‘ghost’ copies are displayed in thin lines and coloured differently, as well as uniformly, in order to distinguish them from the original. The superposition may be performed automatically by secondary-structure matching (Krissinel & Henrick, 2004 ▶) or by least-squares superposition. An example of an NCS ghost molecule is shown in Fig. 5 ▶. (ii) NCS maps. The electron density of NCS-related molecules can be superimposed in order to allow differences in the electron density to be visualized. This is achieved by transforming the coordinates of the three-dimensional contour mesh, rather then the electron density itself, in order to provide good interactive performance. The operators are usually determined with reference to an existing atomic model which obeys the same NCS relationships. An example of an NCS map is shown in Fig. 6 ▶. (iii) NCS-averaged maps. In addition to viewing NCS-related copies of the electron density, the average density of the related regions may be computed and viewed. In noisy maps this can provide a clearer starting point for model building. (iv) NCS rebuilding. When building an atomic model of a molecule with NCS, it is often more convenient to work on one chain and then replicate the changes made in every NCS-related copy of that chain (at least in the early stages of model building). This can be achieved by selecting two related chains and replacing the second chain in its entirety, or in a specific residue range, with an NCS-transformed copy of the first chain. (v) NCS ‘jumping’. The view centre jumps to the next NCS-related peer chain and at the same time the NCS operators are taken into account so that the relative view remains the same. This provides a means for rapid visual comparison of NCS-related entities. 5. Validation Coot incorporates a range of validation tools from the comparison of a model against electron density to comprehensive geometrical checks for protein structures and additional tools specific to nucleotides. It also provides convenient interfaces to external validation tools: most notably the MolProbity suite (Davis et al., 2007 ▶), but also to the REFMAC refinement software (Murshudov et al., 1997 ▶) and dictionary (Vagin et al., 2004 ▶). Many of the internal validation tools provide a uniform interface in the form of colour-coded bar charts, for example the ‘Density Fit Analysis’ chart (Fig. 7 ▶). This window contains one bar chart for each chain in the structure. Each chart contains one bar for each residue in the chain. The height and colour of the bar indicate the model quality of the residue, with small green bars indicating a good or expected/conventional conformation and large red bars indicating poor-quality or ‘unconventional’ residues. The chart is active, i.e. on moving the pointer over the bar tooltips provide relevant statistics and clicking on a bar changes the view in the main graphics window to centre on the selected residue. In this way, a rapid overview of model quality is obtained and problem areas can be investigated. In order to obtain a good structure for submission, the user may simply cycle though the validation options, correcting any problems found. The available validation tools are described in more detail in the following sections. 5.1. Ramachandran plot The Ramachandran plot tool (Fig. 8 ▶) launches a new window in which the Ramachandran plot for the active molecule is displayed. A data point appears in this plot for each residue in the protein, with different symbols distinguishing Gly and Pro residues. The background of the plot shows frequency data for Ramachandran angles using the Richardsons’ data (Lovell et al., 2003 ▶). The plot is interactive: clicking on a data point moves the view in the three-dimensional canvas to centre on the corresponding residue. Similarly, selecting an atom in the model highlights the corresponding data point. Moving the mouse over a data point corresponding to a Gly or Pro residue causes the Ramachandran frequency data for that residue type to be displayed. 5.2. Kleywegt plot The Kleywegt plot (Kleywegt, 1996 ▶; Fig. 9 ▶) is a variation of the Ramachandran plot that is used to highlight NCS differences between two chains. The Ramachandran plot for two chains of the protein is displayed, with the data points of NCS-related residues in the two chains linked by a line for the top 50 (default) most different ϕ, ψ angles. Long lines in the corresponding figure correspond to significant differences in backbone conformation between the NCS-related chains. 5.3. Incorrect chiral volumes Dictionary definitions of monomers can contain descriptions of chiral centres. The chiral centres are described as ‘positive’, ‘negative’ or ‘both’. Coot can compare the residues in the protein structure to the dictionary and identify outliers. 5.4. Unmodelled blobs The ‘Unmodelled Blobs’ tool finds candidate ligand-binding sites (as described above) without trying to fit a specific ligand. 5.5. Difference-map peaks Difference maps can be searched for positive and negative peaks. The peak list is then sorted on peak height and filtered by proximity to higher peaks (i.e. only peaks that are not close to previous peaks are identified). 5.6. Check/delete waters Waters can be validated using several criteria, including distance from hydrogen-bond donors or acceptors, temperature factor or electron-density level. Waters that do not pass these criteria are identified and presented as a list or automatically deleted. 5.7. Check waters by difference map variance This tool is used to identify waters that have been placed in density that should be assigned to other atoms or molecules. The difference map at each water position is analysed by generating 20 points on each sphere at radii of 0.5, 1.0 and 1.5 Å and the electron-density level at each of these points is found by cubic interpolation. The mean and variance of the density levels is calculated for each set of points. If, for example, a water was misplaced into the density for a glycerol then (given an isotropic density model for the water molecule) the difference map will be anisotropic because there will be unaccounted-for positive density along the bonds to the other atoms in the glycerol. There may also be some negative density in a perpendicular direction as the refinement program tries to compensate for the additional electron density. The variances are summed and compared with a reference value (by default 0.12 e2 Å−6). Note that it only makes sense to run this test on a difference map generated by reciprocal-space refinement (for example, from REFMAC or phenix.refine) that included temperature-factor refinement. 5.8. Geometry analysis The geometry (bonds, angles, planes) for each residue in the selected molecule is compared with dictionary values (typically provided by the mmCIF REFMAC dictionary). Torsion-angle deviations are not analysed (as there are other validation tools for these; see §5.9). The statistic displayed in the geometry graph is the average Z value for each of the geometry terms for that residue (peptide-geometry distortion is shared between neighbouring residues). The tooltip on the geometry graph describes the geometry features giving rise to the highest Z value. 5.9. Peptide ω analysis This is a validation tool for the analysis of peptide ω torsion angles. It produces a graph marking the deviation from 180° of the peptide ω angle. The deviation is assigned to the residue that contains the C and O atoms of the peptide link, thus peptide ω angles of 90° are very poor. Optionally, ω angles of 0° can be considered ideal (for the case of intentional cis-peptide bonds). 5.10. Temperature-factor variance analysis The variance of the temperature factors for the atoms of each residue is plotted. This is occasionally useful to highlight misbuilt regions. In a badly fitting residue, reciprocal-space refinement will tend to expand the temperatures factors of atoms in low or negative density, resulting in a high variance. However, residues with long side chains (e.g. Arg or Lys) often naturally have substantial variance, even though the atoms are correctly placed, which causes ‘noise’ in this graph. This shortcoming will be addressed in future developments. H atoms are ignored in temperature-factor variance analysis. 5.11. Gln and Asn B-factor outliers This is another tool that analyses the results of reciprocal-space refinement. A measure z is computed that is half of the difference of the temperature factor between the NE2 and OE1 atoms (in the case of Gln) divided by the standard deviation of the temperature factors of the remaining atoms in the residue. Our analysis of high-resolution structures has shown that when z is greater than +2.25 there is a more than 90% chance that OE1 and NE2 need to be flipped (P. Emsley, unpublished results). 5.12. Rotamer analysis The rotamer statistics are generated from an analysis of the nearest conformation in the MolProbity rotamer probability distribution (Lovell et al., 2000 ▶) and displayed as a bar chart. The height of the bar in the graph is inversely proportional to the rotamer probability. 5.13. Density-fit analysis The bars in the density-fit graphs are inversely proportional to the average Z-weighted electron density at the atom centres and to the grid sampling of the map (i.e. maps with coarser grid sampling will have lower bars than a more finely gridded map, all other things being equal). Accounting for the grid sampling allows lower resolution maps to have an informative density-fit graph without many or most residues being marked as worrisome owing to their atoms being in generally low levels of density. 5.14. Probe clashes ‘Probe Clashes’ is a graphical representation of the output of the MolProbity tools Reduce (Word, Lovell, Richardson et al., 1999 ▶), which adds H atoms to a model (and thereby provides a means of analyzing potential side-chain flips), and Probe (Word, Lovell, LaBean et al., 1999 ▶), which analyses atomic packing. ‘Contact dots’ are generated by Probe and these are displayed in Coot and coloured by the type of interaction. 5.15. NCS differences The graph of noncrystallographic symmetry differences shows the r.m.s. deviation of atoms in residues after the transformation of the selected chain to the reference chain has been applied. This is useful to highlight residues that have unusually large differences in atom positions (the largest differences are typically found in the side-chain atoms). 6. Model analysis 6.1. Geometric measurements Geometric measurements can be performed on the model and displayed in a three-dimensional view using options from the ‘Measures’ menu. These measurements include bond lengths, bond angles and torsion angles, which may be selected by clicking successively on the atoms concerned. It is also possible to measure the distance of an atom to a least-squares plane defined by a set of three or more other atoms. The ‘Environment Distances’ option allows all neighbours within a certain distance of any atom of a chosen residue to be displayed. Distances between polar neighbours are coloured differently to all others. This is particularly useful in the initial analysis of hydrogen bonding. 6.2. Superpositions It is often useful to compare several related molecules which are similar in terms of sequence or fold. In order to do this the molecules must be placed in the same position and orientation in space so that the differences may be clearly seen. Two tools are provided for this purpose. (i) SSM superposition (Krissinel & Henrick, 2004 ▶). Secondary Structure Matching (SSM) is a tool for superposing proteins whose fold is related by fitting the secondary-structure elements of one protein to those of the other. This approach is automatic and does not rely on any sequence identity between the two proteins. The superposition may include a complete structure or just a single chain. (ii) LSQ superposition. Least-squares (LSQ) superposition involves finding the rotation and translation which minimizes the distances between corresponding atoms in the two models and therefore depends on having a predefined correspondence between the atoms of the two structures. This approach is very fast but requires that a residue range from one structure be specified and matched to a corresponding residue range in the other structure. 7. Interaction with other programs In addition to the built-in tools, e.g. for refinement and validation, Coot provides interfaces to external programs. For refinement, interfaces to REFMAC and SHELXL are provided. Validation can be accomplished by interaction with the programs Probe and Reduce from the MolProbity suite. Furthermore, interfaces for the production of publication-quality figures are provided by communication with the (molecular) graphics programs CCP4mg, POV-Ray and Raster3D. 7.1. REFMAC Coot provides a dialogue similar to that used in CCP4i for running REFMAC (Murshudov et al., 2004 ▶). REFMAC is a program from the CCP4 suite for maximum-likelihood-based macromolecular refinement. Once a round of interactive model building has finished, the user can choose to use REFMAC to refine the current model. Reflections for the refinement are either used from the MTZ file from which the currently displayed map was calculated or can be acquired from a selected MTZ file. Most REFMAC parameters are set as defaults; however, some can be specified in the GUI, such as the number of refinement cycles, twin refinement and the use of NCS. Once REFMAC has terminated, the newly generated (refined) model and MTZ file from which maps are generated are automatically read in (and displayed). If REFMAC detected geometrical outliers at the end of the refinement, an interactive dialogue will be presented with two buttons for each residue containing an outlier: one to centre the view on the residue and the other to carry out real-space refinement. 7.2. SHELXL For high-resolution refinement, SHELXL can be used directly from Coot. A new SHELXL.ins file can be generated from a SHELXL.res file including any manipulations or additions to the model. Additional parameters may be added to the file or it can be edited in a GUI. Once refinement in SHELXL is finished, the refined coordinate file is read in and displayed. The resulting reflections file (.fcf) is converted into an mmCIF file, after which it is read in and the electron density is displayed. An interactive dialogue of geometric outliers (disagreeable restraints and other problems discovered by SHELXL) can be displayed by parsing the .lst output file from SHELXL. 7.3. MolProbity Coot interacts with programs and data from the MolProbity suite in a number of ways, some of which have already been described. In addition, MolProbity can provide Coot with a list of possible structural problems that need to be addressed in the form of a ‘to-do chart’ in either Python or Scheme format; this can be read into Coot (‘Calculate’→‘Scripting…’). 7.4. CCP4mg Coot can write CCP4mg picture-definition files (Potterton et al., 2004 ▶). These files are human-readable and editable and define the scene displayed by CCP4mg. Currently, the view and all displayed coordinate models and maps are described in the Coot-generated definition file. Hence, the displayed scene in Coot when saving the file is identical to that in CCP4mg after reading the picture-definition file. For convenience, a button is provided which will automatically produce the picture-definition file and open it in CCP4mg. 7.5. Raster3D/POV-Ray Raster3D (Merritt & Bacon, 1997 ▶) and POV-Ray (Persistence of Vision Pty Ltd, 2004 ▶) are commonly used programs for the production of publication-quality figures in macromolecular crystallography. Coot writes input files for both of these programs to display the current view. These can then be rendered and ray-traced by the external programs either externally or directly within Coot using ‘default’ parameters. The resulting images display molecular models in ball-and-stick representation and electron densities as wire frames. 8. Scripting Most internal functions in Coot are accessible via a SWIG (Simplified Wrapper and Interface Generator) interface to the scripting languages Python (http://www.python.org) and Guile (a Scheme interpreter; Kelsey et al., 1998 ▶; http://www.gnu.org/software/guile/guile.html). Via the same interface, some of Coot’s graphics widgets are available to the scripting layer (e.g. the main menu bar and the main toolbar). The availability of two scripting interfaces allows greater flexibility for the user as well as facilitating the interaction of Coot with other applications. In addition to the availability of Coot’s internal functions, the scripting interface is enriched by a number of provided scripts (usually available in both scripting languages). Some of these scripts use GUIs, either through use of the Coot graphics widgets or via the GTK+2 extensions of the scripting languages. A number of available scripts and functions are made available in an extra ‘Extensions’ menu. Scripting not only provides the user with the possibility of running internal Coot functions and scripts but also that of reading and writing their own scripts and customizing the menus. 9. Building and testing When Coot was made available to the public, three initial considerations were that it should be cross-platform, robust and easy to install. These considerations continue to be a challenge. To assist in meeting them, an automated scheduled build-and-test system has been developed, thus enabling almost constant deployment of the pre-release software. The subversion version-control system (http://svnbook.red-bean.com/) is used to manage source-code revisions. An ‘integration machine’ checks out the latest source code several times per hour, compiles the software and makes a source-code tar file. Less frequently, a heterogeneous array of build machines copies the source tar file and compiles it for the host architecture. After a successful build, the software is run against a test suite and only if the tests are passed is the software bundled and made available for download from the web site. All the build and test logs are made available on the Coot web site. Fortunately, users of the pre-release code seem to report problems without undue exasperation. It is the aim of the developers to respond rapidly to such reports. 9.1. Computer operating-system compatibility Coot is released under the GNU General Public License (GPL) and depends upon many other GPL and open-source software components. Coot’s GUI and graphical display are based on rather standard infrastructure, including the X11 windowing system, OpenGL and associated software such as the cross-platform GTK+2 stack derived from the GIMP project. In addition, Coot depends upon open-source crystallographic software components including the Clipper libraries (Cowtan, 2003 ▶), the MMDB library (Krissinel et al., 2004 ▶), the SSM library (Krissinel & Henrick, 2004 ▶) and the CCP4 libraries. In principle, Coot and its dependencies can be installed on any modern GNU/Linux or Unix platform without fanfare. A Windows-based version of Coot is also available. 9.2. Coot on GNU/Linux Compiling and installing Coot on the GNU/Linux operating system is probably the most straightforward option. GNU/Linux is in essence a free software/open-source collaborative implementation of the Unix operating system that is compatible with most computer hardware. Coot’s infrastructural dependencies, such as GTK+2 and other GNU libraries, as well as all of its crystallographic software dependencies, were selected with portability in mind. Most of the required dependencies are either installed with the GNOME desktop or are readily available for installation via the package-management systems specific to each distribution. It is possible that in future Coot (along with all its dependencies) will be made available via the official package-distribution systems for several of the major GNU/Linux distributions. When an end-user chooses to install the Coot package, all of Coot’s required dependencies will be installed along with it in a simple and painless procedure. An official Coot package currently exists in the Gentoo distribution (maintained by Donnie Berkholz), a Fedora package (maintained by Tim Fenn) is under development at the time of writing and unofficial Debian and rpm Coot packages are also available. Binary Coot releases for the most popular GNU/Linux platforms are available from the Coot website: http://www.ysbl.york.ac.uk/~emsley/software/binaries/. Additional information on installing Coot on GNU/Linux, either as a pre-compiled binary or from source code, is available on the Coot wiki: http://strucbio.biologie.uni-konstanz.de/ccp4wiki/index.php/COOT. 9.3. Coot on Apple’s Mac OS X With the release of Apple’s Mac OS X, a Unix-based operating system, it became possible to use most if not all of the standard crystallographic software on Apple computers. OS X does not natively use the X11 windowing system, but rather a proprietary windowing technology called Quartz. This system has some benefits over X11, but does not support X11-based Unix software. However, the X11 windowing system can be run within OS X (in rootless mode) and as of OS X version 10.5 this has become a default option and operates in a reasonably seamless manner. Unlike GNU/Linux, Apple does not provide the X11-based dependencies (GTK+2, GNOME libraries) and many of the other open-source components required to install and run Coot. However, third-party package-management systems have appeared to fill this gap, having made it their mission to port essentially all of the most important software that is freely available to users of other Unix-based systems to OS X. The two most popular package-management systems are Fink and MacPorts. Of these, Fink makes available a larger collection of software that is of use to scientists, including a substantial collection of crystallographic software. For that reason, Fink has been adopted as the preferred option for installing Coot on Mac OS X. Fink uses many of the same software tools as the Debian GNU/Linux package-management system and provides a convenient front-end. In practice, this requires the end user to do three things in preparation for installing Coot under OS X. (i) Install Apple’s X-code Developer tools. This is a free gigabyte-sized download available from Apple. (ii) Install the very latest version of X11. This is crucial, as many bug fixes are required to run Coot. (iii) Install the third-party package-management system Fink and enable the ‘unstable’ software tree to obtain access to the latest software. Coot may then be installed through Fink with the command fink install coot. 9.4. Coot on Microsoft Windows Since Microsoft Windows operating systems are the most widely used computer platform, a Coot version which runs on Microsoft Windows has been made available (WinCoot). All of Coot’s dependencies compile readily on Windows systems (although some require small adjustments) or are available as GPL/open-source binary downloads. The availability of GTK+2 (dynamically linked) libraries (DLLs) for Windows makes it possible to compile Coot without the requirement of the X11 windowing system, which would depend on an emulation layer (e.g. Cygwin). Some minor adjustments to Coot itself were necessary owing to differences in operating-system architecture, e.g. the filesystem (Lohkamp et al., 2005 ▶). Currently WinCoot, by default, only uses Python as a scripting language since the Guile GTK+2 extension module is not seen as robust enough on Windows. WinCoot binaries are, as for GNU/Linux systems, automatically built and tested on a regular basis. The program is executed using a batch script and has been shown to work on Windows 98, NT, 2000, XP and Vista. WinCoot binaries (stable as well as pre-releases) are available as a self-extracting file from http://www.ysbl.york.ac.uk/~lohkamp/coot/. 10. Discussion Coot tries to combine modern methods in macromolecular model building and validation with concerns about a modern GUI application such as ease of use, productivity, aesthetics and forgiveness. This is an ongoing process and although improvements can still be made, we believe that Coot has an easy-to-learn intuitive GUI combined with a high level of crystallographic awareness, providing useful tools for the novice and experienced alike. However, Coot has a number of limitations: NCS-averaged maps are poorly implemented, being meaningful only over a limited part of the unit cell (or crystal). There is also a mismatch in symmetry when using maps from cryo-EM data (Coot incorrectly applies crystal symmetry to EM maps). Coot is not at all easy to compile, having many dependencies: this is a problem for developers and advanced users. 10.1. Future Coot is under constant development. New features and bug fixes are added on an almost daily basis. It is anticipated that further tools will be added for validation, nucleotide and carbohydrate model building, as well as for refinement. Interactive model building will be enhanced by communication with the CCP4 database, use of annotations and an interactive notebook and by adding annotation representation into the validation graphs. The embedded scripting languages provide the potential for sophisticated communication with model-building tools such as Buccaneer, ARP/wARP and PHENIX; in future this may be extended to include density modification as well. In the longer term tools to handle EM maps are planned, including the possibility of building and refining models. The appropriate data structures are already implemented in the Clipper libraries but are not yet available in Coot. The integration of validation tools will be expanded, especially with respect to MolProbity, and an interface to the WHAT_CHECK validation program (Hooft et al., 1996 ▶) will be added. WHAT_CHECK provides machine-readable output and this can be read by Coot to provide both an interactive description and navigation as well as (requiring more work) a mode to automatically fix up problematic geometry. Note added in proof: Ian Tickle has noted a potential problem with the calculation of χ2 values resulting from real-space refinement. Coot will be reworked to instead represent the r.m.s. deviation from ideality of each of the geometrical terms.

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Phaser crystallographic software

Airlie J. McCoy, Ralf W. Grosse-Kunstleve, Paul D. Adams … (2007)

1. Introduction Improved crystallographic methods rely on both improved automation and improved algorithms. The software handling one part of structure solution must be automatically linked to software handling parts upstream and downstream of it in the structure solution pathway with (ideally) no user input, and the algorithms implemented in the software must be of high quality, so that the branching or termination of the structure solution pathway is minimized or eliminated. Automation allows all the choices in structure solution to be explored where the patience and job-tracking abilities of users would be exhausted, while good algorithms give solutions for poorer models, poorer data or unfavourable crystal symmetry. Both forms of improvement are essential for the success of high-throughput structural genomics (Burley et al., 1999 ▶). Macromolecular phasing by either of the two main methods, molecular replacement (MR) and experimental phasing, which includes the technique of single-wavelength anomalous dispersion (SAD), are key parts of the structure solution pathway that have potential for improvement in both automation and the underlying algorithms. MR and SAD are good phasing methods for the development of structure solution pipelines because they only involve the collection of a single data set from a single crystal and have the advantage of minimizing the effects of radiation damage. Phaser aims to facilitate automation of these methods through ease of scripting, and to facilitate the development of improved algorithms for these methods through the use of maximum likelihood and multivariate statistics. Other software shares some of these features. For molecular replacement, AMoRe (Navaza, 1994 ▶) and MOLREP (Vagin & Teplyakov, 1997 ▶) both implement automation strategies, though they lack likelihood-based scoring functions. Likelihood-based experimental phasing can be carried out using Sharp (La Fortelle & Bricogne, 1997 ▶). 2. Algorithms The novel algorithms in Phaser are based on maximum likelihood probability theory and multivariate statistics rather than the traditional least-squares and Patterson methods. Phaser has novel maximum likelihood phasing algorithms for the rotation functions and translation functions in MR and the SAD function in experimental phasing, but also implements other non-likelihood algorithms that are critical to success in certain cases. Summaries of the algorithms implemented in Phaser are given below. For completeness and for consistency of notation, some equations given elsewhere are repeated here. 2.1. Maximum likelihood Maximum likelihood is a branch of statistical inference that asserts that the best model on the evidence of the data is the one that explains what has in fact been observed with the highest probability (Fisher, 1922 ▶). The model is a set of parameters, including the variances describing the error estimates for the parameters. The introduction of maximum likelihood estimators into the methods of refinement, experimental phasing and, with Phaser, MR has substantially increased success rates for structure solution over the methods that they replaced. A set of thought experiments with dice (McCoy, 2004 ▶) demonstrates that likelihood agrees with our intuition and illustrates the key concepts required for understanding likelihood as it is applied to crystallography. The likelihood of the model given the data is defined as the probability of the data given the model. Where the data have independent probability distributions, the joint probability of the data given the model is the product of the individual distributions. In crystallography, the data are the individual reflection intensities. These are not strictly independent, and indeed the statistical relationships resulting from positivity and atomicity underlie direct methods for small-molecule structures (reviewed by Giacovazzo, 1998 ▶). For macromolecular structures, these direct-methods relationships are weaker than effects exploited by density modification methods (reviewed by Kleywegt & Read, 1997 ▶); the presence of solvent means that the molecular transform is over-sampled, and if there is noncrystallographic symmetry then other correlations are also present. However, the assumption of independence is necessary to make the problem tractable and works well in practice. To avoid the numerical problems of working with the product of potentially hundreds of thousands of small probabilities (one for each reflection), the log of the likelihood is used. This has a maximum at the same set of parameters as the original function. Maximum likelihood also has the property that if the data are mathematically transformed to another function of the parameters, then the likelihood optimum will occur at the same set of parameters as the untransformed data. Hence, it is possible to work with either the structure-factor intensities or the structure-factor amplitudes. In the maximum likelihood functions in Phaser, the structure-factor amplitudes (Fs), or normalized structure-factor amplitudes (Es, which are Fs normalized so that the mean-square values are 1) are used. The crystallographic phase problem means that the phase of the structure factor is not measured in the experiment. However, it is easiest to derive the probability distributions in terms of the phased structure factors and then to eliminate the unknown phase by integration, a process known as integrating out a nuisance variable (the nuisance variable being the introduced phase of the observed structure factor, or equivalently the phase difference between the observed structure factor and its expected value). The central limit theorem applies to structure factors, which are sums of many small atomic contributions, so the probability distribution for an acentric reflection, F O, given the expected value of F O (〈F O〉) is a two-dimensional Gaussian with variance Σ centred on 〈F O〉. (Note that here and in the following, bold font is used to represent complex or signed structure factors, and italics to represent their amplitudes.) In applications to molecular replacement and structure refinement, 〈F O〉 is the structure factor calculated from the model (F C) multiplied by a fraction D (where 0 R, H = 0. The atoms are taken to be of equal mass. The eigenvalues λ and eigenvectors U of H can then be calculated. The eigenvalues are directly proportional to the squares of the vibrational frequencies of the normal modes, the lowest eigenvalues thus giving the lowest normal modes. Six of the eigenvalues will be zero, corresponding to the six degrees of freedom for a rotation and translation of the entire structure. For all but the smallest proteins, eigenvalue decomposition of the all-atom Hessian is not computationally feasible with current computer technology. Various methods have been developed to reduce the size of the eigenvalue problem. Bahar et al. (1997 ▶) and Hinsen (1998 ▶) have shown that it is possible to find the lowest frequency normal modes of proteins in the elastic network model by considering amino acid Cα atoms only. However, this merely postpones the computational problem until the proteins are an order of magnitude larger. The problem is solved for any size protein with the rotation–translation block (RTB) approach (Durand et al., 1994 ▶; Tama et al., 2000 ▶), where the protein is divided into blocks of atoms and the rotation and translation modes for each block used project the full Hessian into a lower dimension. The projection matrix is a block-diagonal matrix of dimensions 3N × 3N. Each of the NB block matrices P nb has dimensions 3N nb × 6, where N nb is the number of atoms in the block nb, For atom j in block nb displaced from the centre of mass, of the block, the 3 × 6 matrix P nb,j is The first three columns of the matrix contain the infinitesimal translation eigenvectors of the block and last three columns contain the infinitesimal rotation eigenvectors of the block. The orthogonal basis Q of P nb is then found by QR decomposition: where Q nb is a 3N nb × 6 orthogonal matrix and R nb is a 6 × 6 upper triangle matrix. H can then be projected into the subspace spanned by the translation/rotation basis vectors of the blocks: where The eigenvalues λP and eigenvectors U P of the projected Hessian are then found. The RTB method is able to restrict the size of the eigenvalue problem for any size of protein with the inclusion of an appropriately large N nb for each block. In the implementation of the RTB method in Phaser, N nb for each block is set for each protein such that the total size of the eigenvalue problem is restricted to a matrix H P of maximum dimensions 750 × 750. This enables the eigenvalue problem to be solved in a matter of minutes with current computing technology. The eigenvectors of the translation/rotation subspace can then be expanded back to the atomic space (dimensions of U are N × N): As for the decomposition of the full Hessian H, the eigenvalues are directly proportional to the squares of the vibrational frequencies of the normal modes, the lowest eigenvalues thus giving the lowest normal modes. Although the eigenvalues and eigenvectors generated from decomposition of the full Hessian and using the RTB approach will diverge with increasing frequency, the RTB approach is able to model with good accuracy the lowest frequency normal modes, which are the modes of interest for looking at conformational difference in proteins. The all-atom, Cα only and RTB normal-mode analysis methods are implemented in Phaser. After normal-mode analysis, n normal modes can be used to generate 2 n − 1 (nonzero) combinations of normal modes. Phaser allows the user to specify the r.m.s. deviation between model and target desired by the perturbation, and the fraction dq of the displacement vector for each mode combination corresponding to each model combination is then used to generate the models. Large r.m.s. deviations will cause the geometry of the model to become distorted. Phaser reports when the model becomes so distorted that there are Cα clashes in the structure. 2.4. Packing function The packing of potential solutions in the asymmetric unit is not inherently part of the translation function. It is therefore possible that an arrangement of models has a high log-likelihood gain, although the models may overlap and therefore be physically unreasonable. The packing of the solutions is checked using a clash test using a subset of the atoms in the structure: the ‘trace’ atoms. For proteins, the trace atoms are the Cα positions, spaced at 3.8 Å. For nucleic acid, the phosphate and C atoms in the ribose-phosphate backbone and the N atoms of the bases are selected as trace atoms. These atoms are also spaced at about 3.8 Å, so that the density of trace atoms in nucleic acid is similar to that of proteins, which makes the number of protein–protein, protein–nucleic acid and nucleic acid–nucleic acid clashes comparable where there is a mixed protein–nucleic acid structure. For the clash test, the number of trace atoms from another model within a given distance (default 3 Å) is counted. The clash test includes symmetry-related copies of the model under consideration, other components in the asymmetric unit and their symmetry-related copies. If the search model has a low sequence identity with the target, or has large flexible loops that could adopt an alternative conformation, the number of clashes may be expected to be nonzero. By default the best packing solutions are carried forward, although a specific number of allowed clashes may also be given as the cut-off for acceptance. However, it is better to edit models before use so that structurally nonconserved surface loops are excluded, as they will only contribute noise to the rotation and translation functions. Where an ensemble of structures is used as the model, the highest homology model is taken as the template for the packing search. Before this model is used, the trace atom positions are edited to take account of large conformational differences between the models in the ensemble. Equivalent trace atom positions are compared and if the coordinates deviate by more than 3 Å then the template trace atom is deleted. Thus, use of an ensemble not only improves signal to noise in the maximum likelihood search functions, it also improves the discrimination of possible solutions by the packing function. 2.5. Minimizer Minimization is used in Phaser to optimize the parameters against the appropriate log-likelihood function in the anisotropy correction, in MR (refines the position and orientation of a rigid-body model) and in SAD phasing. The same minimizer code is used for all three applications and has been designed to be easily extensible to other applications. The minimizer for the anisotropy correction uses Newton’s method, while MR and SAD use the standard Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. Both minimization methods in Phaser include a line search. The line search algorithm is a basic iterative method for finding the local minimum of a target function f. Starting at parameters x , the algorithm finds the minimum (within a convergence tolerance) of by varying γ, where γ is the step distance along a descent direction d . Newton’s method and the BFGS algorithm differ in the determination of the descent direction d that is passed to the line search, and thus the speed of convergence. Within one cycle of the line search (where there is no change in d ) the trial step distances γ are chosen using the golden section method. The golden ratio (51/2/2 + 1/2) divides a line so that the ratio of the larger part to the total is the same as the ratio of the smaller to larger. The method makes no assumptions about the function’s behaviour; in particular, it does not assume that the function is quadratic within the bracketed section. If this assumption were made, the line search could proceed via parabolic interpolation. Newton’s method uses the Hessian matrix H of second derivatives and the gradient g at the initial set of parameters x 0 to find the values of the parameters at the minimum x min. If the function is quadratic in x then Newton’s method will find the minimum in one step, but if not, iteration is required. The method requires the inversion of the Hessian matrix, which, for large matrices, consumes a large amount of computational time and memory resources. The eigenvalues of the Hessian need to be positive for the function to be at a minimum, rather than a maximum or saddle point, since the method converges to any point where the gradient vector is zero. When used with the anisotropy correction, the full Hessian matrix is calculated analytically. The BFGS algorithm is one of the most powerful minimization methods when calculation of the full Hessian using analytic or finite difference methods is very computationally intensive. At every step, the gradient search vector is analysed to build up an approximate Hessian matrix H, in order to make the resulting search vector direction d better than the original gradient vector direction. In the ‘pure’ form of the BFGS algorithm, the method is started with matrix H equal to the identity matrix. The off-diagonal elements of the Hessian, the mixed second derivatives (i.e. ∂2LL/∂p i ∂p j ) are thus initially zero. As the BFGS cycle proceeds, the off-diagonal elements become nonzero using information derived from the gradient. However, in Phaser, the matrix H is not the identity but rather is seeded with diagonal elements equal to the second derivatives of the parameters (p i ) with respect to the log-likelihood target function (LL) (i.e. ∂2LL/∂p i 2, or curvatures), the values found in the ‘true’ Hessian. For the SAD refinement the diagonal elements are calculated analytically, but for the MR refinement the diagonal elements are calculated by finite difference methods. Seeding the Hessian with the diagonal elements dramatically accelerates convergence when the parameters are on different scales; when an identity matrix is used, the parameters on a larger scale can fail to shift significantly because their gradients tend to be smaller, even though the necessary shifts tend to be larger. In the inverse Hessian, small curvatures for parameters on a large scale translate into large scale factors applied to the corresponding gradient terms. If any of these curvature terms are negative (as may happen when the parameters are far from their optimal values), the matrix is not positive definite. Such a situation is corrected by using problem-specific information on the expected relative scale of the parameters from the ‘large-shift’ variable, as discussed below in §2.5.1. In addition to the basic minimization algorithms, the minimizer incorporates the ability to bound, constrain, restrain and reparameterize variables, as discussed in detail below. Bounds must be applied to prevent parameters becoming nonphysical, constraints effectively reduce the number of parameters, restraints are applied to include prior probability information, and reparameterization of variables makes the parameter space more quadratic and improves the performance of the minimizer. 2.5.1. Problem-specific parameter scaling information When a function is defined for minimization in Phaser, information must be provided on the relative scales of the parameters of that function, through a ‘large-shifts’ variable. As its name implies, the variable defines the size of a parameter shift that would be considered ‘large’ for each parameter. The ratios of these large-shift values thus specify prior knowledge about the relative scales of the different parameters for each problem. Suitable large-shift values are found by a combination of physical insight (e.g. the size of a coordinate shift considered to be large will be proportional to d min for the data set) and numerical simulations, studying the behaviour of the likelihood function as parameters are varied systematically in a variety of test cases. The large-shifts information is used in two ways. Firstly, it is used to prevent the line search from taking an excessively large step, which can happen if the estimated curvature for a parameter happens to be too small and can lead to the refinement becoming numerically unstable. If the initial step for a line search would change any parameter by more than its large-shift value, the initial step is scaled down. Secondly, it is used to provide relative scale information to correct negative curvature values. Parameters with positive curvatures are used to define the average relationship between the large-shift values and the curvatures, which can then be used to compute appropriate curvature values for the parameters with negative curvatures. This stabilizes the refinement until it is sufficiently close to the minimum that all curvatures become positive. 2.5.2. Reparameterization Second-order minimization algorithms in effect assume that, at least in the region around the minimum, the function can be approximated as a quadratic. Where this assumption holds, the minimizer will converge faster. It is therefore advantageous to use functions of the parameters being minimized so that the target function is more quadratic in the new parameter space than in the original parameter space (Edwards, 1992 ▶). For example, atomic B factors tend to converge slowly to their refined values because the B factor appears in the exponential term in the structure-factor equation. Although any function of the parameters can be used for this purpose, we have found that taking the logarithm of a parameter is often the most effective reparameterization operation (not only for the B factors). The offset x offset is chosen so that the value of x′ does not become undefined for allowed values of x, and to optimize the quadratic nature of the function in x′. For instance, atomic B factors are reparameterized using an offset of 5 Å2, which allows the B factors to approach zero and also has the physical interpretation of accounting roughly for the width of the distribution of electrons for a stationary atom. 2.5.3. Bounds Bounds on the minimization are applied by setting upper and/or lower limits for each variable where required (e.g. occupancy minimum set to zero). If a parameter reaches a limit during a line search, that line search is terminated. In subsequent line searches, the gradient of that parameter is set to zero whenever the search direction would otherwise move the parameter outside of its bounds. Multiplying the gradient by the step size thus does not alter the value of the parameter at its limit. The parameter will remain at its limit unless calculation of the gradient in subsequent cycles of minimization indicates that the parameter should move away from the boundary and into the allowed range of values. 2.5.4. Constraints Space-group-dependent constraints apply to the anisotropic tensor applied to ΣN in the anisotropic diffraction correction. Atoms on special positions also have constraints on the values of their anisotropic tensor. The anisotropic displacement ellipsoid must remain invariant under the application of each symmetry operator of the space group or site-symmetry group, respectively (Giacovazzo, 1992 ▶; Grosse-Kunstleve & Adams, 2002 ▶). These constraints reduce the number of parameters by either fixing some values of the anisotropic B factors to zero or setting some sets of B factors to be equal. The derivatives in the gradient and Hessian must also be constrained to reflect the constraints in the parameters. 2.5.5. Restraints Bayes’ theorem describes how the probability of the model given the data is related to the likelihood and gives a justification for the use of restraints on the parameters of the model. If the probability of the data is taken as a constant, then P(model) is called the prior probability. When the logarithm of the above equation is taken, Prior probability is therefore introduced into the log-likelihood target function by the addition of terms. If parameters of the model are assumed to have independent Gaussian probability distributions, then the Bayesian view of likelihood will lead to the addition of least-squares terms and hence least-squares restraints on those parameters, such as the least-squares restraints applied to bond lengths and bond angles in typical macromolecular structure refinement programs. In Phaser, least-squares terms are added to restrain the B factors of atoms to the Wilson B factor in SAD refinement, and to restrain the anisotropic B factors to being more isotropic (the ‘sphericity’ restraint). A similar sphericity restraint is used in SHELXL (Sheldrick, 1995 ▶) and in REFMAC5 (Murshudov et al., 1999 ▶). 3. Automation Phaser is designed as a large set of library routines grouped together and made available to users as a series of applications, called modes. The routine-groupings in the modes have been selected mainly on historical grounds; they represent traditional steps in the structure solution pipeline. There are 13 such modes in total: ‘anisotropy correction’, ‘cell content analysis’, ‘normal-mode analysis’, ‘ensembling’, ‘fast rotation function’, ‘brute rotation function’, ‘fast translation function’, ‘brute translation function’, ‘log-likelihood gain’, ‘rigid-body refinement’, ‘single-wavelength anomalous dispersion’, ‘automated molecular replacement’ and ‘automated experimental phasing’. The ‘automated molecular replacement’ and ‘automated experimental phasing’ modes are particularly powerful and aim to automate fully structure solution by MR and SAD, respectively. Aspects of the decision making within the modes are under user input control. For example, the ‘fast rotation function’ mode performs the ensembling calculation, then a fast rotation function calculation and then rescores the top solutions from the fast search with a brute rotation function. There are three possible fast rotation function algorithms and two possible brute rotation functions to choose from. There are four possible criteria for selecting the peaks in the fast rotation function for rescoring with the brute rotation function, and for selecting the results from the rescoring for output. Alternatively, the rescoring of the fast rotation function with the brute rotation function can be turned off to produce results from the fast rotation function only. Other modes generally have fewer routines but are designed along the same principles (details are given in the documentation). 3.1. Automated molecular replacement Most structures that can be solved by MR with Phaser can be solved using the ‘automated molecular replacement’ mode. The flow diagram for this mode is shown in Fig. 1 ▶. The search strategy automates four search processes: those for multiple components in the asymmetric unit, for ambiguity in the hand of the space group and/or other space groups in the same point group, for permutations in the search order for components (when there are multiple components), and for finding the best model when there is more than one possible model for a component. 3.1.1. Multiple components of asymmetric unit Where there are many models to be placed in the asymmetric unit, the signal from the placement of the first model may be buried in noise and the correct placement of this first model only found in the context of all models being placed in the asymmetric unit. One way of tackling this problem has been to use stochastic methods to search the multi-dimensional space (Chang & Lewis, 1997 ▶; Kissinger et al., 1999 ▶; Glykos & Kokkinidis, 2000 ▶). However, we have chosen to use a tree-search-with-pruning approach, where a list of possible placements of the first (and subsequent) models is kept until the placement of the final model. This tree-search-with-pruning search strategy can generate very branched searches that would be challenging for users to negotiate by running separate jobs, but becomes trivial with suitable automation. The search strategy exploits the strength of the maximum likelihood target functions in using prior information in the search for subsequent components in the asymmetric unit. The tree-search-with-pruning strategy is heavily dependent on the criteria used for selecting the peaks that survive to the next round. Four selection criteria are available in Phaser: selection by percentage difference between the top and mean log-likelihood of the search, selection by Z score, selection by number of peaks, and selection of all peaks. The default is selection by percentage, with the default percentage set at 75%. This selection method has the advantage that, if there is one clear peak standing well above the noise, it alone will be passed to the next round, while if there is no clear signal, all peaks high in the list will be passed as potential solutions to the next round. If structure solution fails, it may be possible to rescue the solution by reducing the percentage cut-off used for selection from 75% to, for example, 65%, so that if the correct peak was just missing the default cut-off, it is now included in the list passed to the next round. The tree-search-with-pruning search strategy is sub-optimal where there are multiple copies of the same search model in the asymmetric unit. In this case the search generates many branches, each of which has a subset of the complete solution, and so there is a combinatorial explosion in the search. The tree search would only converge onto one branch (solution) with the placement of the last component on each of the branches, but in practice the run time often becomes excessive and the job is terminated before this point can be reached. When searching for multiple copies of the same component in the asymmetric unit, several copies should be added at each search step (rather than branching at each search step), but this search strategy must currently be performed semi-manually as described elsewhere (McCoy, 2007 ▶). 3.1.2. Alternative space groups The space group of a structure can often be ambiguous after data collection. Ambiguities of space group within the one point group may arise from theoretical considerations (if the space group has an enantiomorph) or on experimental grounds (the data along one or more axes were not collected and the systematic absences along these axes cannot be determined). Changing the space group of a structure to another in the same point group can be performed without re-indexing, merging or scaling the data. Determination of the space group within a point group is therefore an integral part of structure solution by MR. The translation function will yield the highest log-likelihood gain for a correctly packed solution in the correct space group. Phaser allows the user to make a selection of space groups within the same point group for the first translation function calculation in a search for multiple components in the asymmetric unit. If the signal from the placement of the first component is not significantly above noise, the correct space group may not be chosen by this protocol, and the search for all components in the asymmetric unit should be completed separately in all alternative space groups. 3.1.3. Alternative models As the database of known structures expands, the number of potential MR models is also rapidly increasing. Each available model can be used as a separate search model, or combined with other aligned structures in an ‘ensemble’ model. There are also various ways of editing structures before use as MR models (Schwarzenbacher et al., 2004 ▶). The number of MR trials that can be performed thus increases combinatorially with the number of potential models, which makes job tracking difficult for the user. In addition, most users stop performing MR trials as soon as any solution is found, rather than continuing the search until the MR solution with the greatest log-likelihood gain is found, and so they fail to optimize the starting point for subsequent steps in the structure solution pipeline. The use of alternative models to represent a structure component is also useful where there are multiple copies of one type of component in the asymmetric unit and the different copies have different conformations due to packing differences. The best solution will then have the different copies modelled by different search models; if the conformation change is severe enough, it may not be possible to solve the structure without modelling the differences. A set of alternative search models may be generated using previously observed conformational differences among similar structures, or, for example, by normal-mode analysis (see §2.3). Phaser automates searches over multiple models for a component, where each potential model is tested in turn before the one with the greatest log-likelihood gain is found. The loop over alternative models for a component is only implemented in the rotation functions, as the solutions passed from the rotation function to the translation function step explicitly specify which model to use as well as the orientation for the translation function in question. 3.1.4. Search order permutation When searching for multiple components in the asymmetric unit, the order of the search can be a factor in success. The models with the biggest component of the total structure factor will be the easiest to find: when weaker scattering components are the subject of the initial search, the solution may be buried in noise and not significant enough to survive the selection criteria in the tree-search-with-pruning search strategy. Once the strongest scattering components are located, then the search for weaker scattering components (in the background of the strong scattering components) is more likely to be a success. Having a high component of the total structure factor correlates with the model representing a high fraction of the total contents of the asymmetric unit, low r.m.s. deviation between model and target atoms, and low B factors for the target to which the model corresponds. Although the first of these (high completeness) can be determined in advance from the fraction of the total molecular weight represented by the model, the second can only be estimated from the Chothia & Lesk (1986 ▶) formula and the third is unknown in advance. If structure solution fails with the search performed in the order of the molecular weights, then other permutations of search order should be tried. In Phaser, this possibility is automated on request: the entire search strategy (except for the initial anisotropic data correction) is performed for all unique permutations of search orders. 3.2. Automated experimental phasing SAD is the simplest type of experimental phasing method to automate, as it involves only one crystal and one data set. SAD is now becoming the experimental phasing method of choice, overtaking multiple-wavelength anomalous dispersion because only a single data set needs to be collected. This can help minimize radiation damage to the crystal, which has a major adverse effect on the success of multi-wavelength experiments. The ‘automated experimental phasing’ mode in Phaser takes an atomic substructure determined by Patterson, direct or dual-space methods (Karle & Hauptman, 1956 ▶; Rossmann, 1961 ▶; Mukherjee et al., 1989 ▶; Miller et al., 1994 ▶; Sheldrick & Gould, 1995 ▶; Sheldrick et al., 2001 ▶; Grosse-Kunstleve & Adams, 2003 ▶) and refines the positions, occupancies, B factors and values of the atoms to optimize the SAD function, then uses log-likelihood gradient maps to complete the atomic substructure. The flow diagram for this mode is shown in Fig. 2 ▶. The search strategy automates two search processes: those for ambiguity in the hand of the space group and for completing atomic substructure from log-likelihood gradient maps. A feature of using the SAD function for phasing is that the substructure need not only consist of anomalous scatterers; indeed it can consist of only real scatterers, since the real scattering of the partial structure is used as part of the phasing function. This allows structures to be completed from initial real scattering models. 3.2.1. Enantiomorphic space groups Since the SAD phasing mode of Phaser takes as input an atomic substructure model, the space group of the solution has already been determined to within the enantiomorph of the correct space group. Changing the enantiomorph of a SAD refinement involves changing the enantiomorph of the heavy atoms, or in some cases the space group (e.g. the enantiomorphic space group of P41 is P43). In some rare cases (Fdd2, I41, I4122, I41 md, I41 cd, I 2d, F4132; Koch & Fischer, 1989 ▶) the origin of the heavy-atom sites is changed [e.g. the enantiomorphic space group of I41 is I41 with the origin shifted to ( , 0, 0)]. If there is only one type of anomalous scatterer, the refinement need not be repeated in both hands: only the phasing needs to be carried out in the second hand to be considered. However, if there is more than one type of anomalous scatterer, then the refinement and substructure completion needs to be repeated, as it will not be enantiomorphically symmetric in the other hand. To facilitate this, Phaser runs the refinement and substructure completion in both hands [as does other experimental phasing software, e.g. Solve (Terwilliger & Berendzen, 1999 ▶) and autosharp (Vonrhein et al., 2006 ▶)]. The correct space group can then be found by inspection of the electron density maps; the density will only be interpretable in the correct space group. In cases with significant contributions from at least two types of anomalous scatterer in the substructure, the correct space group can also be identified by the log-likelihood gain. 3.2.2. Completing the substructure Peaks in log-likelihood gradient maps indicate the coordinates at which new atoms should be added to improve the log-likelihood gain. In the initial maps, the peaks are likely to indicate the positions of the strongest anomalous scatterers that are missing from the model. As the phasing improves, weaker anomalous scatterers, such as intrinsic sulfurs, will appear in the log-likelihood gradient maps, and finally, if the phasing is exceptional and the resolution high, non-anomalous scatterers will appear, since the SAD function includes a contribution from the real scattering. After refinement, atoms are excluded from the substructure if their occupancy drops below a tenth of the highest occupancy amongst those atoms of the same atom type (and therefore ). Excluded sites are flagged rather than permanently deleted, so that if a peak later appears in the log-likelihood gradient map at this position, the atom can be reinstated and prevented from being deleted again, in order to prevent oscillations in the addition of new sites between cycles and therefore lack of convergence of the substructure completion algorithm. New atoms are added automatically after a peak and hole search of the log-likelihood gradient maps. The cut-off for the consideration of a peak as a potential new atom is that its Z score be higher than 6 (by default) and also higher than the depth of the largest hole in the map, i.e. the largest hole is taken as an additional indication of the noise level of the map. The proximity of each potential new site to previous atoms is then calculated. If a peak is more than a cut-off distance (κ Å) of a previous site, the peak is added as a new atom with the average occupancy and B factor from the current set of sites. If the peak is within κ Å of an isotropic atom already present, the old atom is made anisotropic. Holes in the log-likelihood gradient map within κ Å of an isotropic atom also cause the atom’s B factor to be switched to anisotropic. However, if the peak or hole is within κ Å of an anisotropic atom already present, the peak or hole is ignored. If a peak is within κ Å of a previously excluded site, the excluded site is reinstated and flagged as not for deletion in order to prevent oscillations, as described above. At the end of the cycle of atom addition and isotropic to anisotropic atomic B-factor switching, new sites within 2κ Å of an old atom that is now anisotropic are then removed, since the peak may be absorbed by refining the anisotropic B factor; if not, it will be accepted as a new site in the next cycle of log-likelihood gradient completion. The distance κ may be input directly by the user, but by default it is the ‘optical resolution’ of the structure (κ = 0.715d min), but not less than 1 Å and no more than 10 Å. If the structure contains more than one significant anomalous scatterer, then log-likelihood gradient maps are calculated from each atom type, the maps compared and the atom type associated with each significant peak assigned from the map with the most significant peak at that location. 3.2.3. Initial real scattering model One of the reasons for including MR and SAD phasing within one software package is the ability to use MR solutions with the SAD phasing target to improve the phases. Since the SAD phasing target contains a contribution from the real scatterers, it is possible to use a partial MR model with no anomalous scattering as the initial atomic substructure used for SAD phasing. This approach is useful where there is a poor MR solution combined with a poor anomalous signal in the data. If the poor MR solution means that the structure cannot be phased from this model alone, and the poor anomalous signal means that the anomalous scatterers cannot be located in the data alone, then using the MR solution as the starting model for SAD phasing may provide enough phase information to locate the anomalous scatterers. The combined phase information will be stronger than from either source alone. To facilitate this method of structure solution, Phaser allows the user to input a partial structure model that will be interpreted in terms of its real scattering only and, following phasing with this substructure, to complete the anomalous scattering model from log-likelihood gradient maps as described above. 3.3. Input and output The fastest and most efficient way, in terms of development time, to link software together is using a scripting language, while using a compiled language is most efficient for intensive computation. Following the lead of the PHENIX project (Adams et al., 2002 ▶, 2004 ▶), Phaser uses Python (http://python.org) as the scripting language, C++ as the compiled language, and the Boost.Python library (http://boost.org/libs/python/) for linking C++ and Python. Other packages, notably X-PLOR (Brünger, 1993 ▶) and CNS (Brünger et al., 1998 ▶), have defined their own scripting languages, but the choice of Python ensures that the scripting language is maintained by an active community. Phaser functionality has mostly been made available to Python at the ‘mode’ level. However, some low-level SAD refinement routines in Phaser have been made available to Python directly, so that they can be easily incorporated into phenix.refine. A long tradition of CCP4 keyword-style input in established macromolecular crystallography software (almost exclusively written in Fortran) means that, for many users, this has been the familiar method of calling crystallographic software and is preferred to a Python interface. The challenge for the development of Phaser was to find a way of satisfying both keyword-style input and Python scripting with minimal increase in development time. Taking advantage of the C++ class structure allowed both to be implemented with very little additional code. Each keyword is managed by its own class. The input to each mode of Phaser is controlled by Input objects, which are derived from the set of keyword classes appropriate to the mode. The keyword classes are in turn derived from a CCP4base class containing the functionality for the keyword-style input. Each keyword class has a parse routine that calls the CCP4base class functions to parse the keyword input, stores the input parameters as local variables and then passes these parameters to a keyword class set function. The keyword class set functions check the validity and consistency of the input, throw errors where appropriate and finally set the keyword class’s member parameters. Alternatively, the keyword class set functions can be called directly from Python. These keyword classes are a standalone part of the Phaser code and have already been used in other software developments (Pointless; Evans, 2006 ▶). An Output object controls all text output from Phaser sent to standard output and to text files. Switches on the Output object give different output styles: CCP4-style for compatibility with CCP4 distribution, PHENIX-style for compatibility with the PHENIX interface, CIMR-style for development, XML-style output for developers of automation scripts and a ‘silent running’ option to be used when running Phaser from Python. In addition to the text output, where possible Phaser writes results to files in standard format; coordinates to ‘pdb’ files and reflection data (e.g. map coefficients) to ‘mtz’ files. Switches on the Output object control the writing of these files. 3.3.1. CCP4-style output CCP4-style output is a text log file sent to standard output. While this form of output is easily comprehensible to users, it is far from ideal as an output style for automation scripts. However, it is the only output style available from much of the established software that developers wish to use in their automation scripts, and it is common to use Unix tools such as ‘grep’ to extract key information. For this reason, the log files of Phaser have been designed to help developers who prefer to use this style of output. Phaser prints four levels of log file, summary, log, verbose and debug, as specified by user input. The important output information is in all four levels of file, but it is most efficient to work with the summary output. Phaser prints ‘SUCCESS’ and ‘FAILURE’ at the end of the log file to demarcate the exit state of the program, and also prints the names of any of the other output files produced by the program to the summary output, amongst other features. 3.3.2. XML output XML is becoming commonly used as a way of communicating between steps in an automation pipeline, because XML output can be added very simply by the program author and relatively simply by others with access to the source code. For this reason, Phaser also outputs an XML file when requested. The XML file encapsulates the mark-up within 〈phaser〉 tags. As there is no standard set of XML tags for crystallographic results, Phaser’s XML tags are mostly specific to Phaser but were arrived at after consultation with other developers of XML output for crystallographic software. 3.3.3. Python interface The most elegant and efficient way to run Phaser as part of an automation script is to call the functionality directly from Python. Using Phaser through the Python interface is similar to using Phaser through the keyword interface. Each mode of operation of Phaser described above is controlled by an Input object and its parameter set functions, which have been made available to Python with the Boost.Python library. Phaser is then run with a call to the ‘run-job’ function, which takes the Input object as a parameter. The ‘run-job’ function returns a Result object on completion, which can then be queried using its get functions. The Python Result object can be stored as a ‘pickled’ class structure directly to disk. Text is not sent to standard out in the CCP4 logfile way but may be redirected to another output stream. All Input and Result objects are fully documented. 4. Future developments Phaser will continue to be developed as a platform for implementing novel phasing algorithms and bringing the most effective approaches to the crystallographic community. Much work remains to be done formulating maximum likelihood functions with respect to noncrystallographic symmetry, to account for correlations in the data and to consider non-isomorphism, all with the aim of achieving the best possible initial electron density map. After a generation in which Fortran dominated crystallographic software code, C++ and Python have become the new standard. Several developments, including Phaser, PHENIX (Adams et al., 2002 ▶, 2004 ▶), Clipper (Cowtan, 2002 ▶) and mmdb (Krissinel et al., 2004 ▶), simultaneously chose C++ as the compiled language at their inception at the turn of the millennium. At about the same time, Python was chosen as a scripting language by PHENIX, ccp4mg (Potterton et al., 2002 ▶, 2004 ▶) and PyMol (DeLano, 2002 ▶), amongst others. Since then, other major software developments have also started or converted to C++ and Python, for example PyWarp (Cohen et al., 2004 ▶), MrBump (Keegan & Winn, 2007 ▶) and Pointless (Evans, 2006 ▶). The choice of C++ for software development was driven by the availability of free compilers, an ISO standard (International Standardization Organization et al., 1998 ▶), sophisticated dynamic memory management and the inherent strengths of using an object-oriented language. Python was equally attractive because of the strong community support, its object-oriented design, and the ability to link C++ and Python through the Boost.Python library or the SWIG library (http://www.swig.org/). Now that a ‘critical mass’ of developers has taken to using the new languages, C++ and Python are likely to remain the standard for crystallographic software for the current generation of crystallographic software developers. Phaser source code has been distributed directly by the authors (see http://www-structmed.cimr.cam.ac.uk/phaser for details) and through the PHENIX and CCP4 (Collaborative Computing Project, Number 4, 1994 ▶) software suites. The source code is released for several reasons, including that we believe source code is the most complete form of publication for the algorithms in Phaser. It is hoped that generous licensing conditions and source distribution will encourage the use of Phaser by other developers of crystallographic software and those writing crystallographic automation scripts. There are no licensing restrictions on the use of Phaser in macromolecular crystallography pipelines by other developers, and the license conditions even allow developers to alter the source code (although not to redistribute it). We welcome suggestions for improvements to be incorporated into new versions. Compilation of Phaser requires the computational crystallography toolbox (cctbx; Grosse-Kunstleve & Adams, 2003 ▶), which includes a distribution of the cmtz library (Winn et al., 2002 ▶). The Boost libraries (http://boost.org/) are required for access to the functionality from Python. Phaser runs under a wide range of operating systems including Linux, Irix, OSF1/Tru64, MacOS-X and Windows, and precompiled executables are available for these platforms when only keyword-style access (and not Python access) is required. Graphical user interfaces to Phaser are available for both the PHENIX and the CCP4 suites. User support is available through PHENIX, CCP4 and from the authors (email cimr-phaser@lists.cam.ac.uk).

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Overview of the CCP4 suite and current developments

Martyn D. Winn, Charles C. Ballard, Kevin D Cowtan … (2011)

1. Introduction CCP4 (Collaborative Computational Project, Number 4, 1994 ▶) exists to produce and support a world-leading integrated suite of programs that allows researchers to determine macromolecular structures by X-ray crystallography and other biophysical techniques. CCP4 aims to develop and support the development of cutting-edge approaches to the experimental determination and analysis of protein structure and to integrate these approaches into the CCP4 software suite. CCP4 is a community-based resource that supports the widest possible researcher community, embracing academic, not-for-profit and for-profit research. CCP4 aims to play a key role in the education and training of scientists in experimental structural biology. It encourages the wide dissemination of new ideas, techniques and practice. In this article, we give an overview of the CCP4 project, past, present and future. We begin with a historical perspective on the growth of the software suite, followed by a summary of the current functionality in the suite. We then discuss ongoing plans for the next generation of the suite which is in development. In this account we focus on the suite as a whole, while other articles in this issue delve deeper into individual programs. We intend that this article could serve as a general literature citation for the use of the CCP4 software suite in structure determination, although we also encourage the citation of individual programs, many of the relevant references for which are included here. While we focus here on the CCP4 software suite, we would emphasize that comparable functionality is available in other software packages such as SHARP/autoSHARP (Vonrhein et al., 2007 ▶), SHELX (Sheldrick, 2008 ▶), ARP/wARP (Langer et al., 2008 ▶), PHENIX (Adams et al., 2010 ▶) and many others. 2. Evolution of the CCP4 software suite The CCP4 software suite is a collection of programs implementing specific algorithms concerned with macromolecular structure solution from X-ray diffraction data. Significantly, it is a collection of autonomous and independently developed programs. While some have been commissioned by the academic committees overseeing the CCP4 project, the majority originate from the community to address a perceived gap in current functionality or to implement newly developed algorithms. The result is a collection of around 200 programs, ranging from large programs which are effectively packages in themselves to small ‘jiffy’ programs. Over the years the suite has grown continuously, with each major release featuring significant new software (see Table 1 ▶). Unsurprisingly, there is overlap of functionality, with several programs performing a particular task, albeit often using different approaches. The question then is how to combine these programs into a software suite, both in terms of ensuring communication between the different programs and in helping both naïve and experienced users to navigate through the suite. Early on in the history of CCP4, there was an agreement for all programs to use the same file formats for data files. Formats were specified for diffraction data (the LCF format, later replaced by the MTZ format) and for electron-density maps (the CCP4 map format), while for atomic coordinates the PDB format was adopted. A software library was developed to facilitate reading and writing of these data formats and thereby ensure standardization of the formats. Originally supporting only Fortran programs, the library was re-written to support both Fortran and C/C++ as well as scripting languages (Winn et al., 2002 ▶). The CCP4 set of libraries has since expanded to cover a wider range of crystallographic tasks, in particular with the addition of the Clipper library (Cowtan, 2003 ▶), the MMDB library (Krissinel et al., 2004 ▶) and the CCTBX library (Grosse-Kunstleve et al., 2002 ▶) from the PHENIX project (Adams et al., 2010 ▶). Crystallographic tasks were performed by writing or adapting scripts (e.g. Unix shell or VMS scripts) to link together a number of programs (Fig. 1a ▶) and the suite can still be run in this way. The programs communicate solely via the data files which are passed between them. The user sets program options based on the program documentation and the expected results from earlier steps. A major change was introduced in 2000 with the release of the graphical user interface ccp4i (Fig. 1b ▶; Potterton et al., 2003 ▶). Task interfaces help the user to prepare run scripts. Details of how to run specific programs are largely hidden, as are the jiffy programs used to perform minor functions such as format conversion. Some limited intelligence in the interface code allows program options to be customized according to properties of the data and/or the desired objective. ccp4i interfaces are now available for all of the commonly used CCP4 programs as well as for several non-CCP4 programs (e.g. ARP/wARP; Langer et al., 2008 ▶). The ccp4i interface also introduced for the first time tools for helping the user to organize data. Jobs that have been run were recorded in a ‘database’ (in reality a directory of files) with tools to access and interpret the files saved there. Jobs are further organized into projects, representing different structure solutions. There are now plans to update the CCP4 GUI (see §4), but the impact of the original ccp4i on the suite should not be underestimated. In the last few years, two other modes of accessing the CCP4 suite have emerged. On the one hand, the latest version of the suite contains four complementary automation pipelines, namely xia2 (Winter, 2010 ▶), CRANK (Ness et al., 2004 ▶), MrBUMP (Keegan & Winn, 2007 ▶) and BALBES (Long et al., 2008 ▶). These pipelines attempt to perform large sections of the full structure solution (e.g. phasing) without user intervention. This is achieved partly through the use of a large number of trials, trying different protocols and performing parameter scanning. Such an approach can be very powerful, using cheap computer power to make many more attempts than a user would manually. Automation pipelines have been realised in the last few years because of the maturity of the underlying programs and the availability of sufficient computer power to support multiple trials. On the other hand, graphical programs for interactive use have become more powerful. Rather than simply reviewing the results of previously run programs and performing interactive model editing, Coot (Emsley et al., 2010 ▶) can launch separate refinement and validation programs (Fig. 1c ▶). Similarly, iMOSFLM can be used to interface the data-processing programs POINTLESS and SCALA. In some ways this is a completely different scenario to the automation pipelines. User interaction is paramount, with crystallography programs acting as tools to be invoked. The user can become familiar with the data and structure and use this to make intelligent decisions. Such an approach has also become possible because of the maturity of the invoked programs and the availability of sufficient computer power to run the programs interactively. 3. Overview of current functionality In this section, we give an overview of the current functionality of the CCP4 software suite (corresponding to release series 6.1 at the time of writing). We summarize the automation pipelines and individual programs included in the suite; many more details can be found in the accompanying articles in this issue. We present the functionality in the traditional manner, starting at data processing and ending at validation. However, it is becoming increasingly apparent that these neat categories are breaking down. 3.1. Data processing The earliest starting point for entry into the CCP4 suite is a set of X-ray diffraction images. The data-reduction program MOSFLM (Leslie, 2006 ▶) will take a set of diffraction images, identify spots on each image, index the diffraction pattern and thus identify the Bragg peaks, and integrate the spots. The output is a list of integrated intensities and their standard uncertainties labelled by the h, k, l indices. Associated information includes the batch number of the image from which the intensity was obtained, whether the peak was full or partial and the symmetry operation that relates the particular observation to the chosen asymmetric unit. MOSFLM continues to be improved, with support added recently for Pilatus detectors, addition of automatic backstop masking etc. The most visible change is the replacement of the old X-windows-based interface with the Tcl-based iMOSFLM interface (Fig. 2 ▶), which guides the user in a stepwise manner through the stages of data processing. POINTLESS is a relatively new program whose primary purpose is to identify the Laue group of a crystal from an unmerged data set (Evans, 2006 ▶). The program will also attempt to identify the space group from an analysis of systematic absences. A secondary purpose is to test the choice of indexing and re-index a data set if necessary. Given a choice of space group, the program SCALA (Evans, 2006 ▶) will refine the parameters of a scaling function for an unmerged data set, apply scales to each observation of a reflection and merge all observations of a reflection to give an average intensity. It will also provide an improved estimate of the standard uncertainty of each intensity. The new program CTRUNCATE (which replaces the older TRUNCATE; Stein, unpublished program) can then convert the intensities to structure-factor amplitudes, although downstream programs increasingly use the mean intensities directly. Perhaps more importantly, CTRUNCATE will analyse a data set for signs of twinning, translational noncrystallographic symmetry (NCS), anisotropy and other notable features, since it is best to identify problems before attempting phasing. The program SFCHECK (Vaguine et al., 1999 ▶) will also provide an analysis of a data set, including testing for twinning and translational NCS, estimating the optical resolution and the anisotropy, and plotting the radial and angular completeness. The previous steps of data processing are automated by the xia2 pipeline (Winter, 2010 ▶). From a directory of images, xia2 will identify the type of experiment (multi-wedge, multi-pass, multi-wavelength) and process accordingly. The pipeline will determine the point group, space group and correct indexing. Multiple processing pipelines using alternative underlying programs are supported. At the end, the user should have a set of merged structure-factor amplitudes suitable for input to phasing. 3.2. Experimental phasing CCP4 includes the CRANK pipeline (Ness et al., 2004 ▶), which covers experimental phasing and beyond, and interfaces with several CCP4 and non-CCP4 programs. Heavy-atom substructure detection is performed by AFRO/CRUNCH2 (de Graaff et al., 2001 ▶) or by SHELXC/D (Sheldrick, 2008 ▶) and initial phasing is carried out by BP3 (Pannu et al., 2003 ▶; Pannu & Read, 2004 ▶) or SHELXE (Sheldrick, 2008 ▶). Phase improvement is carried out by SOLOMON (Abrahams & Leslie, 1996 ▶), DM (Cowtan et al., 2001 ▶) or Pirate (Cowtan, 2000 ▶) and automated model building by Buccaneer (Cowtan, 2006 ▶; Cowtan, 2008 ▶) or ARP/wARP (Langer et al., 2008 ▶). CRANK thus supports a range of underlying software handling the communication of data and allowing the user to trial different combinations. CCP4 includes a number of additional individual programs, each of which has its own particular strength. The long-standing CCP4 program MLPHARE for phasing still works in straightforward cases and is fast to use. ACORN (Jia-xing et al., 2005 ▶; Dodson & Woolfson, 2009 ▶) uses ab initio methods for the determination of phases starting from a small fragment which could be a single heavy atom. The use of ab initio methods usually requires atomic resolution data, since it assumes atomicity of the electron density. However, a variant of the so-called free-lunch algorithm (Jia-xing et al., 2005 ▶) allows the temporary generation of phases to atomic resolution which the ACORN method can utilize. The OASIS program (Wu et al., 2009 ▶) also uses ab initio methods to break the phase ambiguity in SAD/SIR phasing. Phaser (McCoy et al., 2007 ▶) can obtain phase estimates starting from known heavy-atom positions and SAD data. Log-likelihood gradient (LLG) maps are used to automatically find additional sites for anomalous scatterers and to detect anisotropy in existing anomalous scatterers. Phaser can also use a partial model, for example from a molecular-replacement solution that is hard to refine, as a source of phase information to help locate weak anomalous scatterers and thus improved phases. The latter reflects the view of experimental phasing and molecular replacement as just two sources of phase information rather than two separate techniques. 3.3. Molecular replacement CCP4 includes two pipelines for molecular replacement (MR): MrBUMP (Keegan & Winn, 2007 ▶) and BALBES (Long et al., 2008 ▶). Both start from processed data and a target sequence and aim to deliver a molecular-replacement solution consisting of positioned and partially refined models. BALBES uses its own database of protein molecules and domains taken from the PDB and customized for MR, while MrBUMP uses public databases and a set of widely available bioinformatics tools to generate possible search models. BALBES is based around the MR program MOLREP (Vagin & Teplyakov, 1997 ▶, 2010 ▶), while MrBUMP can also use the program Phaser (McCoy et al., 2007 ▶). Both MOLREP and Phaser are also available as stand-alone programs in CCP4. As well as providing rotation and translation functions, whereby a search model is positioned in the unit cell to give an initial estimate of the phases, these programs provide additional functionality, including a significant contribution to automated decision-making. For instance, a single run of Phaser can search for several copies each of several components in the structure of a complex, testing different possible search orders and trying different possible choices of space group. The search model for MR may be an ensemble of structures, a set of models from an NMR structure or an electron-density map. Phases for the target may be available, so that the search model is to be fitted into electron density, or there may be density available from an electron-microscopy experiment. The MR step can be followed by rigid-body refinement and the packing of the MR solution can be checked. Much of this functionality is common to Phaser and MOLREP, but there are a number of differences in implementation, so that both may prove useful in certain circumstances. A crucial component of MR is the selection and preparation of search models. The program CHAINSAW (Stein, 2008 ▶) takes as input a sequence alignment which relates residues in the search model to residues in the target protein and uses this information to edit the search model appropriately. The output model is labelled according to the target sequence. MOLREP (Lebedev et al., 2008 ▶) can take as input the target sequence and performs its own alignment to the search model in order to edit the search model. 3.4. Phase improvement and automated model building Having obtained initial phases from experimental phasing, the next step is phase improvement (density modification) to give a map that can be built into. When phases come from molecular replacement, phase improvement may also be useful to reduce model bias. For a long time, the main CCP4 phase-improvement programs were DM (Cowtan et al., 2001 ▶) and SOLOMON (Abrahams & Leslie, 1996 ▶), which covered the standard techniques of solvent flattening/flipping, histogram matching and NCS averaging. More recently, statistically based methods have been incorporated into the program Pirate (Cowtan, 2000 ▶). Pirate can give better results, but has been found to be inconveniently slow. The latest program Parrot (Cowtan, 2010 ▶) achieves similar improvements but is also fast and automated. Given an electron-density map, automated model building is provided in CCP4 by Buccaneer (Cowtan, 2006 ▶, 2008 ▶). This finds candidate Cα positions, builds these into chain fragments, joins the fragments together and docks a sequence. NCS can be used to rebuild and complete related chains. Since version 1.4, there is support for model (re)building after molecular replacement and for supplying known structural elements such as heavy atoms. The CCP4 suite includes an interface for alternating cycles of model building with Buccaneer with cycles of model refinement with REFMAC5. The supplementary program Sloop (Cowtan, unpublished program) builds missing loops using fragments taken from the Richardson’s Top500 library of structures (Lovell et al., 2003 ▶) to fill gaps in the chain. The chance of finding a good fit falls with increasing size of the gap, but the method may work for loops of up to eight residues in length. RAPPER (Furnham et al., 2006 ▶) provides a conformational search algorithm for protein modelling, which can produce an ensemble of models satisfying a wide variety of restraint information. In the context of CCP4, restraints on the modelling are provided by the electron density and/or the locations of the Cα atoms. The ccp4i interface includes modes for loop building or for building the entire structure. 3.5. Refinement and model completion The aim of macromolecular crystallography is to produce a model of the macromolecule of interest which explains the diffraction images as accurately and completely as possible. Both the form of the model and the parameters of the model need to be defined. Refinement is the process of optimizing the values of the model parameters and in CCP4 is performed by the program REFMAC5 (Murshudov et al., 1997 ▶). REFMAC5 will refine atomic coordinates and atomic isotropic or anisotropic displacement parameters (Murshudov et al., 1999 ▶), as well as group parameters for rigid-body refinement and TLS refinement (Winn et al., 2001 ▶, 2003 ▶). It will also refine scaling parameters and a mask-based bulk-solvent correction. When good-quality experimental phases are available, these can be included as additional data (Pannu et al., 1998 ▶). More recently, it has become possible to refine directly against anomalous data for the cases of SAD (Skubák et al., 2004 ▶) and SIRAS (Skubák et al., 2009 ▶) without the need for estimated phases and phase probabilities. REFMAC5 will also now refine against twinned data (Lebedev et al., 2006 ▶), automatically recognising the twin laws and estimating the corresponding twin fractions. The nonprotein contents of the crystal are often of most interest, such as bound ligands, cofactors, metal sites etc. Correct refinement at moderate or low resolution requires a knowledge of the ideal geometry together with associated uncertainties. In REFMAC5 this is handled through a dictionary of possible ligands (Vagin et al., 2004 ▶), with details held in mmCIF format. Dictionary files can be created through the tools SKETCHER and JLIGAND. Refinement goes hand-in-hand with rounds of model building which add/subtract parts of the model and apply large structural changes that are beyond the reach of refinement. In addition to the automated procedures of Buccaneer and RAPPER described above, there are many model-building tools in Coot (Emsley et al., 2010 ▶). A ccp4i interface to the popular ARP/wARP model-building package (Langer et al., 2008 ▶) has also been available for many years. 3.6. Validation, deposition and publication Validation is the process of ensuring that all aspects of the model are supported by the diffraction data, as well as conforming with known features of protein chemistry. Although validation has traditionally been viewed as something that is performed at the end of structure determination, just before deposition, it is now appreciated that validation is an integral part of the process of structure solution, which should be carried out continually. CCP4 includes a wide variety of validation tools, all of which should be run to gain a complete picture of model quality. Coot (Emsley et al., 2010 ▶) has a dedicated drop-down menu of validation tools which can and should be applied as the model is being built. Coot can also extract warnings about particular links or outliers from a REFMAC5 log file. Warnings associated with specific atoms or residues are linked directly to the model as viewed in Coot. The ccp4i ‘Validation and Deposition’ module contains further validation tools. As mentioned above, SFCHECK (Vaguine et al., 1999 ▶) provides a number of measures of data quality, but if a model is provided it will also assess the agreement of the model with the data. Sequins (Cowtan, unpublished program) validates the assigned sequence against electron density (generated from experimental phases or from phases calculated from a side-chain omit process) and warns of misplaced side chains or register errors. RAMPAGE (which is part of the RAPPER package; Furnham et al., 2006 ▶) provides Ramachandran plots based on updated ϕ–ψ propensities. PROCHECK is also included, although the Ramachandran plots are no longer generated, having been superseded by RAMPAGE. R500 (Henrick, unpublished program) checks the stereochemistry in a given PDB file against expected values and lists outliers in REMARK 500 records. The quaternary structure of the protein can be analysed with PISA (Krissinel & Henrick, 2007 ▶). This considers all possible interfaces in the crystal structure, estimates the free energy of dissociation, taking into account solvation and entropy effects, and predicts which interfaces are likely to be of biological significance. The CCP4 molecular-graphics program CCP4mg (Potterton et al., 2002 ▶, 2004 ▶) provides a simple means of generating publication-quality images and movies. As well as displaying coordinates in a wide variety of styles, CCP4mg can display molecular surfaces, electron density, arbitrary vectors and labels. The latest versions are built on the Qt toolkit, giving an enhanced look and feel (Fig. 3 ▶). Structures and views can be transferred between CCP4mg and Coot. 3.7. Jiffies and utilities In addition to the main functionality described above, the CCP4 suite contains a large number of utilities for performing format conversions and various analyses. Reflection data processed in other software packages can be imported with the utilities COMBAT, POINTLESS, SCALEPACK2MTZ, DTREK2SCALA and DTREK2MTZ, while data can be exchanged with other structure-solution packages with CONVERT2MTZ, F2MTZ, CIF2MTZ, MTZ2VARIOUS and MTZ2CIF. There are several useful utilities based on the Clipper library (Cowtan, 2003 ▶), such as CPHASEMATCH, which will compare two phase sets and look for changes in origin or hand. There are also many useful utilities for analysing coordinate files. New programs based on the MMDB library (Krissinel et al., 2004 ▶) include NCONT for listing atom contacts and PDB_MERGE for combining two PDB files. 4. Future plans At the heart of the CCP4 suite are the set of algorithms encoded in individual programs. As always, we include new programs in each major release of the suite and will continue to do so. Since the source of novel software is usually independent developers, the additions to the suite are not centrally planned. Nevertheless, some current themes are clearly recognisable, such as automated model building, in particular for low-resolution data. CCP4 also aims to enhance its functionality related to the maintenance and use of data on small molecules (ligands). Firstly, a considerably larger library of chemical compounds will be provided with the suite. Extended search functions will be provided to allow the efficient retrieval of known compounds or their close analogues. Secondly, existing functions for generating restraint data for new ligands will be enhanced by the inclusion of relevant software such as PRODRG (Schüttelkopf & van Aalten, 2004 ▶) into the suite, as well as by the development of new methods for structure reconstruction on the basis of partial similarity to structures in the library. Functionality will be available through a graphical front-end application, JLIGAND. In addition to the core programs, the infrastructure of CCP4 continues to evolve to support the latest working practices. The current CCP4 GUI, ccp4i, was a major innovation and has served us well for over ten years (Potterton et al., 2003 ▶). While it continues to provide a useful interface to the CCP4 suite, there are increasing demands from automation pipelines and users alike. In particular, there is a requirement to provide help on what to try next, advice which can be useful to both scientists and automated software. This depends on a robust assessment of the experimental data and the results of previous processing, which in turn requires good data management. We aim to address these issues through the development of a next-generation CCP4 interface. There will also be changes in the way that CCP4 is delivered to the end user. We have all become used to automated updates to the software we use (e.g. Windows Update, Synaptic for Debian-based Linux or application-specific updates such as for Firefox). Some CCP4 programs do alert the users to the availability of newer versions and CCP4mg (Potterton et al., 2002 ▶, 2004 ▶) will update the version on request. A CCP4-wide update mechanism is more difficult given the heterogeneous nature of the suite, but efforts in this direction are under way. A specific example of a remotely maintained crystallography platform is given by the US-based SBGrid Consortium. The CCP4 suite is downloaded to a user’s machine or a local server before being run. This is in contrast to many biology software tools, which are web-based. Reasons for running CCP4 locally include the wallclock time of jobs, the detailed control required and the size of data files. Nevertheless, there is increasing usage of web servers for crystallographic tasks. A server at York (http://www.ysbl.york.ac.uk/YSBLPrograms/index.jsp) runs a number of CCP4 programs, including BALBES and Buccaneer, while CCP4 programs are included in a number of other services, for example the ARP/wARP server at Hamburg (http://cluster.embl-hamburg.de/ARPwARP/remote-http.html). Plans are under way to make more CCP4 functionality available via the web. Finally, the coming years will see increasing integration of crystallography with other techniques, both experimental and theoretical. CCP4 aims to contribute towards efforts, such as the European infrastructure project INSTRUCT, to ease the transfer of data to and from these other domains.

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Contributors

Roberto Gozalbo-Rovira:

ORCID: http://orcid.org/0000-0003-3427-3800

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – original draft

J. Rafael Ciges-Tomas:

ORCID: http://orcid.org/0000-0003-2647-3052

Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draft

Susana Vila-Vicent: Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draft

Javier Buesa:

ORCID: http://orcid.org/0000-0002-3328-3428

Role: ConceptualizationRole: SupervisionRole: Writing – review & editing

Cristina Santiso-Bellón: Role: Data curationRole: InvestigationRole: Methodology

Vicente Monedero: Role: ConceptualizationRole: Writing – review & editing

María J. Yebra: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – review & editing

Alberto Marina: Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: Writing – review & editing

Jesús Rodríguez-Díaz:

ORCID: http://orcid.org/0000-0002-9698-7684

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing

Félix A. Rey: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Pathog

Journal ID (iso-abbrev): PLoS Pathog

Journal ID (publisher-id): plos

Journal ID (pmc): plospath

Title: PLoS Pathogens

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1553-7366

ISSN (Electronic): 1553-7374

Publication date (Electronic): 21 June 2019

Publication date Collection: June 2019

Volume: 15

Issue: 6

Electronic Location Identifier: e1007865

Affiliations

[1 ] Departament of Microbiology, Faculty of Medicine, University of Valencia, Valencia, Spain

[2 ] Department of Genomic and Proteomic, Instituto de Biomedicina de Valencia (IBV-CSIC) and CIBER de Enfermedades Raras (CIBERER), Valencia, Spain

[3 ] Department of Food Biotechnology, Institute of Agrochemistry and Food Technology (IATA-CSIC), Paterna, Spain

Institut Pasteur, FRANCE

Author notes

The authors have declared that no competing interests exist.

* E-mail: amarina@ 123456ibv.csic.es (AM); jesus.rodriguez@ 123456uv.es (JR)

Author information

Roberto Gozalbo-Rovira http://orcid.org/0000-0003-3427-3800

J. Rafael Ciges-Tomas http://orcid.org/0000-0003-2647-3052

Javier Buesa http://orcid.org/0000-0002-3328-3428

Jesús Rodríguez-Díaz http://orcid.org/0000-0002-9698-7684

Article

Publisher ID: PPATHOGENS-D-18-01908

DOI: 10.1371/journal.ppat.1007865

PMC ID: 6609034

PubMed ID: 31226167

SO-VID: 5dfb8fa6-5923-4171-82a5-5eda1f1b8ba8

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 2 October 2018

Date accepted : 23 May 2019

Page count

Figures: 7, Tables: 2, Pages: 25

Funding

Funded by: funder-id http://dx.doi.org/10.13039/501100010198, Ministerio de Economía, Industria y Competitividad, Gobierno de España;

Award ID: AGL2014-52996-C2-2-R

Award Recipient :

ORCID: http://orcid.org/0000-0002-9698-7684

Jesús Rodríguez-Díaz

Funded by: funder-id http://dx.doi.org/10.13039/501100010198, Ministerio de Economía, Industria y Competitividad, Gobierno de España;

Award ID: RYC-2013-12442

Award Recipient :

ORCID: http://orcid.org/0000-0002-9698-7684

Jesús Rodríguez-Díaz

Funded by: funder-id http://dx.doi.org/10.13039/501100010198, Ministerio de Economía, Industria y Competitividad, Gobierno de España;

Award ID: AGL2014-52996-C2-1-R

Award Recipient : María J. Yebra

Funded by: funder-id http://dx.doi.org/10.13039/501100010198, Ministerio de Economía, Industria y Competitividad, Gobierno de España;

Award ID: BIO2016-78571-P

Award Recipient : Alberto Marina

Funded by: funder-id http://dx.doi.org/10.13039/501100003359, Generalitat Valenciana;

Award ID: Prometeo II/2014/029

Award Recipient : Alberto Marina

Funded by: funder-id http://dx.doi.org/10.13039/501100003359, Generalitat Valenciana;

Award ID: APOST/2017/037

Award Recipient :

ORCID: http://orcid.org/0000-0003-3427-3800

Roberto Gozalbo-Rovira

Funded by: funder-id http://dx.doi.org/10.13039/501100003176, Ministerio de Educación, Cultura y Deporte;

Award ID: FPU13/02880

Award Recipient :

ORCID: http://orcid.org/0000-0003-2647-3052

J. Rafael Ciges-Tomas

Funded by: funder-id http://dx.doi.org/10.13039/501100003359, Generalitat Valenciana;

Award ID: ACIF/2016/437

Award Recipient : Susana Vila-Vicent

This work was supported by Spanish Government (Ministerio de Economia y Competitividad) grants AGL2014-52996-C2-2-R and RYC-2013-12442 to JRD, AGL2014-52996-C2-1-R to MJY, BIO2016-78571-P to AM and by Valencian Government grant Prometeo II/2014/029 to AM. RGR is the recipient of a postdoctoral grant from the Valencian Government APOST/2017/037, JRCT is the recipient of fellowship FPU13/02880 from Ministerio de Educación, Cultura y Deporte, SVV is recipient of a predoctoral fellowship from Valencian Government ACIF/2016/437. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Custom metadata

PLOS Publication Stage vor-update-to-uncorrected-proof

Publication Update 2019-07-03

Data Availability All relevant data are within the paper and its Supporting Information files. The following structures have been deposited in the Protein Data Base (PDB) with the accession numbers 6H9W, 6H9Z, 6H9Y and 6HA0.

ScienceOpen disciplines: Infectious disease & Microbiology

Data availability:

ScienceOpen disciplines: Infectious disease & Microbiology

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