Evasion of cGAS and TRIM5 defines pandemic HIV

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Abstract

Of the 13 known independent zoonoses of simian immunodeficiency viruses to humans, only one, leading to human immunodeficiency virus (HIV) type 1(M) has become pandemic, causing over 80 million human infections. To understand the specific features associated with pandemic human-to-human HIV spread, we compared replication of HIV-1(M) with non-pandemic HIV-(O) and HIV-2 strains in myeloid cell models. We found that non-pandemic HIV lineages replicate less well than HIV-1(M) owing to activation of cGAS and TRIM5-mediated antiviral responses. We applied phylogenetic and X-ray crystallography structural analyses to identify differences between pandemic and non-pandemic HIV capsids. We found that genetic reversal of two specific amino acid adaptations in HIV-1(M) enables activation of TRIM5, cGAS and innate immune responses. We propose a model in which the parental lineage of pandemic HIV-1(M) evolved a capsid that prevents cGAS and TRIM5 triggering, thereby allowing silent replication in myeloid cells. We hypothesize that this capsid adaptation promotes human-to-human spread through avoidance of innate immune response activation.

Abstract

Innate immune evasion is key to evolution of the pandemic lineage of HIV.

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RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

Alexandros Stamatakis (2014)

Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.

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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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PHENIX: a comprehensive Python-based system for macromolecular structure solution

Paul D. Adams, Pavel Afonine, Gábor Bunkóczi … (2010)

1. Foundations 1.1. PHENIX architecture The PHENIX (Adams et al., 2002 ▶) architecture is designed from the ground up as a hybrid system of tightly integrated interpreted (‘scripted’) and compiled software modules. A mix of scripted and compiled components is invariably found in all major successful crystallographic packages, but often the scripting is added as an afterthought in an ad hoc fashion using tools that predate the object-oriented programming era. While such ad hoc systems are quickly established, they tend to become a severe maintenance burden as they grow. In addition, users are often forced into many time-consuming routine tasks such as manually converting file formats. In PHENIX, the scripting layer is the heart of the system. With only a few exceptions, all major functionality is implemented as modules that are exclusively accessed via the scripting interfaces. The object-oriented Python scripting language (Lutz & Ascher, 1999 ▶) is used for this purpose. In about two decades, a large developer/user community has produced millions of lines of highly uniform, interoperable, mature and openly available sources covering all aspects of programming ranging from simple file handling to highly sophisticated network communication and fully featured cross-platform graphical interfaces. Embedding crystallographic methods into this environment enables an unprecedented degree of automation, stability and portability. By design, the object-oriented programming model fosters shared collaborative development by multiple groups. It is routine practice to hierarchically recombine modules written by different groups into ever more complex procedures that appear uniform from the outside. A more detailed overview of the key software technology leading to all these advances, presented in the context of crystallography, can be found in Grosse-Kunstleve et al. (2002 ▶). In addition to the advantages outlined in the previous paragraph, the scripting language is generally most efficient for the rapid development of new algorithms. However, runtime performance considerations often dictate that numerically intensive calculations are eventually implemented in a compiled language. The first choice of a compiled language is of course to reuse the same language environment as used for the scripting language itself, which is a C/C++ environment. Not only is this the mainstream software environment on all major platforms used today, but with probably hundreds of millions of lines of C/C++ sources in existence it is an environment that is virtually guaranteed to thrive in the long term. An in-depth discussion of the combined use of Python and C++ can be found in Grosse-Kunstleve et al. (2002 ▶) and Abrahams & Grosse-Kunstleve (2003 ▶). This model is used throughout the PHENIX system. 1.2. Graphical user interface A new graphical user interface (GUI) for PHENIX was introduced in version 1.4. It uses the open-source wxPython toolkit, which provides a ‘native’ look on each operating system. Development has focused on providing interfaces around the existing command-line programs with minimal modification, using the same underlying configuration system (libtbx.phil) as used by most PHENIX programs as a template to automatically generate controls. Because these programs are implemented primarily as Python modules, complex data including models, reflections and other viewable data may be exchanged with the GUI without resorting to parsing log files. The current PHENIX release (version 1.5) includes GUIs for phenix.refine (Afonine et al., 2005 ▶), phenix.xtriage (Zwart et al., 2005 ▶), the AutoSol (Terwilliger et al., 2009 ▶), AutoBuild (Terwilliger, Grosse-Kunstleve, Afonine, Moriarty, Adams et al., 2008 ▶) and LigandFit (Terwilliger et al., 2006 ▶) wizards, the restraints editor REEL, all of the validation tools and several utilities for creating and manipulating maps and reflection files. More recent builds of PHENIX contain a new GUI for the AutoMR wizard and future releases will include a new interface for Phaser (McCoy et al., 2007 ▶). Intrinsically graphical data is visualized with embedded graphs (using the free matplotlib Python library) or a simple OpenGL viewer. This simplifies the most complex parameters, such as atom selections in phenix.refine, which can be visualized or picked interactively with the built-in viewer. The GUI also serves as a platform for additional automation and user customization. Similarly to the CCP4 interface (CCP4i; Potterton et al., 2003 ▶), PHENIX manages data and task history for separate user-defined projects. Default parameters and input files can be specified for each project; for instance, the generation of ligand restraints from the phenix.refine GUI gives the user the option of automatically loading these restraints in future runs. The popularity of Python as a scientific programming language has led to its use in many other structural-biology applications, especially molecular-graphics software. The PHENIX GUI includes extension modules for the modeling programs Coot (Emsley & Cowtan, 2004 ▶) and PyMOL (DeLano, 2002 ▶), both of which are controlled remotely from PHENIX using the XML-RPC protocol. This allows the interfaces to integrate seamlessly; any model or map in PHENIX can be automatically opened in Coot with a single click. In programs that iteratively rebuild or refine structures, such as AutoBuild and phenix.refine, the current model and maps will be continually updated in Coot and/or PyMOL as soon as they are available. In the validation utilities, clicking on any atom or residue flagged for poor statistics will recentre the graphics windows on that atom. Remote control of the PHENIX GUI is also simple using the same protocol and simple extensions to the Coot interface provide direct launching of phenix.refine with a model pre-loaded. 2. Analysis of experimental data PHENIX has a range of tools for the analysis, validation and manipulation of X-ray diffraction data. A comprehensive tool for analyzing X-ray diffraction data is phenix.xtriage (Zwart et al., 2005 ▶), which carries out tests ranging from space-group determination and detection of twinning to detection of anomalous signal. These tests provide the user and the various wizards with a set of statistics that characterize a data set. For analysis of twinning, phenix.xtriage consolidates a number of statistics to provide a balanced verdict of possible symmetry and twin-related issues with the data. Phenix.xtriage provides the user with feedback on the overall characteristics of the data. Routine usage of phenix.xtriage during or immediately after data collection has resulted in the timely discovery of twinning or other issues (Flynn et al., 2007 ▶; Kostelecky et al., 2009 ▶). Detection of these idiosyncrasies in the data typically reduces the overall effort in a successful structure determination. A likelihood-based estimation of the overall anisotropic scale factor is performed using the likelihood formalism described by Popov & Bourenkov (2003 ▶). Database-derived standard Wilson plots for proteins and nucleic acids are used to detect anomalies in the mean intensity. These anomalies may arise from ice rings or other issues (Morris et al., 2004 ▶). Data strength and low-resolution completeness are also analysed. The presence of anomalous signal is detected by analysis of the measurability, a quantity expressing the fraction of statistically significant Bijvoet differences in a data set (Zwart, 2005 ▶). The native Patterson function is used to detect the presence of pseudo-translational symmetry. A database-derived empirical distribution of maximum peak heights is used to assign significance to detected peaks in the Patterson function. A comprehensive automated twinning analysis is performed. Twin laws are derived from first principles to facilitate the identification of pseudo-merodehral cases. Amplitude and intensity ratios, 〈|E 2 − 1|〉 values, the L-statistic (Padilla & Yeates, 2003 ▶) and N(Z) plots are derived from data cut to the resolution limit suggested by the data-strength analysis. The removal of shells of data with relatively high noise content greatly improves the automated interpretation of these statistics. A Britton plot, H-test and a likelihood-derived approach are used to estimate twin fractions when twin laws are present. If a model has been supplied, an R versus R (Lebedev et al., 2006 ▶) analysis is carried out. This type of analysis is of particular use when dealing with pseudo-symmetry, space-group problems and twinning (Zwart et al., 2008 ▶). To test for inconsistent indexing between different data sets, a set of reindexing laws is derived from first principles given the unit cells and space groups of the sample and reference data sets. A correlation analysis suggests the most likely choice of reindexing of the data. Analysis of the metric symmetry of the unit cell provides a number of likely point groups. A likelihood-inspired method is used to suggest the most likely point group of the data. Subsequent analysis of systematic absences in a likelihood framework ranks subsequent space-group possibilities (details to be published). 3. Substructure determination, phasing and molecular replacement After ensuring that the diffraction data are sound and understood, the next critical necessity for solving a structure is the determination of phases using one of several strategies (Adams, Afonine et al., 2009 ▶). 3.1. Substructure determination The substructure-determination procedure implemented as phenix.hyss (Hybrid Substructure Search; Grosse-Kunstleve & Adams, 2003 ▶) combines the multi-trial dual-space recycling approaches pioneered by Shake-and-Bake (Miller et al., 1994 ▶) and later SHELXD (Sheldrick, 2008 ▶) with the use of the fast translation function (Navaza & Vernoslova, 1995 ▶; Grosse-Kunstleve & Brunger, 1999 ▶). The fast translation function is the basis for a systematic search in the Patterson function (performed in reciprocal space), in contrast to the stochastic alternative of SHELXD (performed in direct space). Phenix.hyss is the only substructure-determination program to fully integrate automatic comparison of the substructures found in multiple trials via a Euclidean Model Matching procedure (part of the cctbx open-source libraries). This allows phenix.hyss to detect if the same solution was found multiple times and to terminate automatically if this is the case. Extensive tests with a variety of SAD data sets (Grosse-Kunstleve & Adams, 2003 ▶) have led to a parameterization of the procedure that balances runtime considerations and the likelihood that repeated solutions present the correct substructure. In many cases the procedure finishes in seconds if the substructure is detectable from the input data. 3.2. Phasing Phaser, available in PHENIX as phenix.phaser, applies the principle of maximum likelihood to solving crystal structures by molecular replacement, by single-wavelength anomalous diffraction (SAD) or by a combination of both. The likelihood targets take proper account of the effects of different sources of error (and, in the case of SAD phasing, their correlations) and allow different sources of information to be combined. In solving a molecular-replacement problem with a number of different components, the information gained from a partial solution increases the signal in the search for subsequent components. Because the likelihood scores for different models can be directly compared, decisions among models can readily be made as part of automation strategies (discussed below). 3.3. Noncrystallographic symmetry (NCS) Noncrystallographic symmetry is an important feature of many macromolecular crystals that can be used to greatly improve electron-density maps. PHENIX has tools for the identification of NCS and for using NCS and multiple crystal forms of a macromolecule in phase improvement. Phenix.find_ncs and phenix.simple_ncs_from_pdb are tools for the identification of noncrystallographic symmetry in a structure using information from a heavy-atom substructure or an atomic model. Phenix.simple_ncs_from_pdb will identify NCS and generate transformations from the chains in a model in a PDB file. Phenix.find_ncs will identify NCS from either a heavy-atom substructure (Terwilliger, 2002a ▶) or the chains in a PDB file and will then compare this NCS with the density in a map to verify that the NCS is actually present. Phenix.multi_crystal_average is a method for combining information from several crystal forms of a structure. It is especially well suited to cases where each crystal form has its own NCS, adjusting phases for each crystal form so that all the NCS copies in all crystals are as similar as possible. NCS restraints should normally be applied in density modification and model building in all cases except where there is clear evidence that NCS is not present. In density modification within PHENIX the presence of NCS is identified from the heavy-atom sites or from an atomic model if available. The local correlation of density in NCS-related locations is then used automatically to set variable restraints on NCS symmetry in the map. In refinement, NCS symmetry is applied through coordinate restraints, targeting the positions of each NCS copy relative to those of the other NCS-related chains. The default NCS restraints in PHENIX are very tight, with targets of 0.05 Å r.m.s. At resolutions lower than about 2.5 Å these tight restraints on NCS should usually be applied. At higher resolutions it may be appropriate to use looser restraints or to remove them altogether. Additionally, if there are segments of the chains that clearly do not obey the NCS relationships they should be excluded from the NCS restraints. Normally this is performed automatically, but it can also be specified explicitly. 4. Model building, ligand fitting and nucleic acids Key steps in the analysis of a macromolecular crystal structure are building an initial core model, identification and fitting of ligands into the electron-density map and building an atomic model for loop regions that are less well defined than the majority of the structure. PHENIX has tools for rapid model building of secondary structure and main-chain tracing (phenix.find_helices_strands) and for the fitting of flexible ligands (phenix.ligandfit) as well as for fitting a set of ligands to a map (phenix.find_all_ligands) and for the identification of ligands in a map (phenix.ligand_identification). PHENIX additionally has a tool for the fitting of missing loops (phenix.fit_loops). Validation tools are provided so that the models produced can be validated at each step along the way. 4.1. Model building Phenix.find_helices_strands will rapidly build a secondary-structure-only model into a map or very rapidly trace the polypeptide backbone of a model into a map. To build secondary structure in a map, phenix.find_helices_strands identifies α-helical regions and β-strand segments, models idealized helices and strands into the corresponding density, allowing for bending of the helices and strands, and assembles these into a composite model. To very rapidly trace the main chain in a map, phenix.find_helices_strands finds points along ridgelines of high density where Cα atoms might be located, identifies pairs and then triplets of these Cα atoms that have density between the atoms and plausible geometry, constructs all possible connections of these Cα atoms into nonamers and then identifies all the longest possible chains that can be made by joining the nonamers. This process can build a Cα model at a rate of about 20 residues per second, yielding a backbone model that can readily be interpreted visually or automatically to evaluate the quality of the map that it is based on. Phenix.fit_loops will fit missing loops in an atomic model. It uses RESOLVE model building (Terwilliger, 2003a ▶,b ▶,c ▶) to extend the chain from either end where a loop is missing and to connect the chains into a loop with the expected number of residues. 4.2. Ligand fitting Phenix.ligandfit is a tool for fitting a flexible ligand into an electron-density map (Terwilliger et al., 2006 ▶). The key approaches used are breaking the ligand into its component rigid-body parts, finding where each of these can be placed into density, tracing the remainder of the ligand based on the positions of these core rigid-body parts and recombining the best parts of multiple fits while scoring based on the fit to the density. Phenix.find_all_ligands is a tool for finding all the instances of each of several ligands in an electron-density map. Phenix.find_all_ligands finds the largest contiguous region of unused density in a map and uses phenix.ligandfit to fit each supplied ligand into that density. It then chooses the ligand that has the highest real-space correlation to the density (Terwilliger, Adams et al., 2007 ▶). It then repeats this process until no ligands can be satisfactorily fitted into any remaining density in the map. Phenix.ligand_identification is a tool for identifying which ligands are compatible with unknown electron density in a map (Terwilliger, Adams et al., 2007 ▶). It can search using the 200 most common ligands from the PDB or from a user-supplied list of ligands. Phenix.ligand_identification uses phenix.ligandfit to fit each ligand to the map and identifies the best-fitting ligand using the real-space correlation and surface complementarity of the ligand and the atoms in the structure surrounding the ligand-binding site. 4.3. RNA and DNA In common with most macromolecular crystallographic tools, PHENIX was originally developed with protein structures primarily in mind. Now that nucleic acids, and especially RNA, are increasingly important in large biological structures, the system is being modified in places where subtle differences in procedure are needed rather than just the relevant libraries. Model building in phenix.autobuild now has a preliminary set of nucleic acid procedures that take advantage of the relatively well determined phosphate and base positions, as well as the preponderance of double helix, and that make use of the RNA backbone conformers recently defined by the RNA Ontology Consortium (Richardson et al., 2008 ▶). Nucleic acid structures benefit significantly from torsion-angle refinement, which has recently been added to the options in phenix.refine. A principal problem in RNA models is getting the ribose pucker correct, although it is known to consist almost entirely of either C3′-endo (which is commoner and that found in the A-form helix) or C2′-endo (Altona & Sundaralingam, 1972 ▶). MolProbity uses the perpendicular distance from the 3′ phosphate to the line of the C1′—N1/9 glycosidic bond as a reliable diagnostic of ribose pucker (Davis et al., 2007 ▶; Chen et al., 2010 ▶). This same test has now been built into phenix.refine to allow the use of pucker-specific target parameters for bond lengths, angles and torsions (Gelbin et al., 1996 ▶) rather than the uneasy compromise values (Parkinson et al., 1996 ▶) used in most pucker-agnostic refinement. Currently, if an incorrect pucker is diagnosed it must usually be fixed by user rebuilding, for instance in Coot (Emsley & Cowtan, 2004 ▶) or in RNABC (Wang et al., 2008 ▶). A rebuilding functionality will probably be incorporated into PHENIX soon, but in the meantime the refinement will now correctly maintain the geometry of a C2′-endo pucker once it has been built and identified using conformation-specific residue names. 4.4. Maps, models and avoiding bias Phenix.refine (and the graphical tool phenix.create_maps) can produce various types of maps, including anomalous difference, maximum-likelihood weighted (p*mF obs − q*DF model)exp(iαmodel) and regular (p*F obs − q*F model)exp(iαmodel), where p and q are any user-defined numbers, filled and kick maps. The coefficients m and D of likelihood-weighted maps (Read, 1986 ▶) are computed using test-set reflections as described in Lunin & Skovoroda (1995 ▶) and Urzhumtsev et al. (1996 ▶). Data incompleteness, especially systematic incompleteness, can cause map distortions (Lunin, 1988 ▶; Tronrud, 1997 ▶). An approach to remedying this problem is to replace (‘fill’) missing observations with nonzero values. One can use DF model (similarly to REFMAC; Murshudov et al., 1997 ▶) to replace the missing F obs or use 〈F obs〉, where the F obs are averaged across a resolution bin around the missing F obs value. Based on a limited number of tests, both ‘filling’ schemes produce similar results, reiterating the importance of phases. However, it is important to keep in mind that by replacing missing F obs there is a risk of introducing bias and obviously the more incomplete the data is the larger the risk. At present it is advisable to use both maps simultaneously: filled and not filled. An average kick map (AK map; Gunčar et al., 2000 ▶; Turk, 2007 ▶; Pražnikar et al., 2009 ▶) is the result of the following procedure. A large ensemble of structures is created where the coordinates of each structure from the ensemble are all randomly shaken. A map is then computed for each structure. Finally, all maps are averaged to generate one AK map. An AK map is expected to have less bias and less noise and to enhance the existing signal and can potentially clarify some initially bad densities. A computationally intensive but powerful method of creating a very low-bias map is to carry out iterative model building and refinement while omitting one region of the map from all calculations of structure factors (Terwilliger, Grosse-Kunstleve, Afonine, Moriarty, Adams et al., 2008 ▶). The phenix.autobuild iterative-build OMIT map procedure carries this out automatically for either a single OMIT region or for overlapping OMIT regions to create a composite iterative-build OMIT map. 5. Model, and model-to-data, validation The result of crystallographic structure determination is the atomic model. There are three principal components in assessing model quality: the covalent model geometry, the model stereochemistry and the quality of fit between the model and experimental data in both real space and in reciprocal space. All three provide overall measures, and the first two plus the real-space aspect of the third also provide checks for local outliers, which give the best leverage for user intervention to actively improve model accuracy (Arendall et al., 2005 ▶). (Validation of the experimental data was described in §2 above.) PHENIX includes many individual tools for specific aspects of validation, plus several systems that combine those results into overall summaries. Validation is provided both for user evaluation of the progress and results of a structure solution and also to help inform the automated choices made by other parts of the system. Most aspects of the MolProbity model-validation tools (Davis et al., 2007 ▶; Chen et al., 2010 ▶) have been adapted or rewritten for integrated use within PHENIX and are presented to the user by the new GUI (§1.2). H atoms are added by phenix.reduce, with optimization of entire local hydrogen-bond networks, consideration of the first layer of crystallographic waters and optional correction of side-chain amide or histidine 180° ‘flips’ (Word, Lovell, Richardson et al., 1999 ▶). All-atom contacts (Word, Lovell, LaBean et al., 1999 ▶) are calculated by phenix.probe, which provides the atomic overlap information needed for the validation of serious all-atom steric clashes and can also be visualized in Coot. For the PHENIX GUI, the set of MolProbity-based tools provides both overall model statistics, such as clashscore and percentage of outliers, and detailed lists of the Ramachandran (Lovell et al., 2003 ▶), rotamer (Lovell et al., 2000 ▶), Cβ deviation (Lovell et al., 2003 ▶) and clash outliers. Command-line tools are available for these validation methods: phenix.rotalyze, phenix.ramalyze, phenix.cbetadev, phenix.clashscore, phenix.reduce and phenix.probe. Additionally, phenix.validate_model, which analyzes the deviations of bond lengths, bond angles, planarity etc. from ideal library values, complements the MolProbity torsional and atomic clash tools. Phenix.real_space_correlation asserts the local model-to-data correspondence by providing a quantitative measure of how the atomic model fits the electron-density map at the residue or atom level (depending on the resolution). Rapidly obtaining a snapshot of global figures of merit for a crystallographic model and associated experimental data is a frequent task that is performed at all stages of structure solution. This task can be complicated for several reasons: the presence of novel ligands or nonstandard residues in the PDB-format (Berman et al., 2000 ▶) coordinate file, data collected from twinned crystals, various reflection datafile formats, different representation of atomic displacement parameters in the presence of TLS (Schomaker & Trueblood, 1968 ▶), experimental data type (X-ray and/or neutron), files with multiple models and various formatting issues. Phenix.model_vs_data is designed to automatically handle all these complications with minimal user input (a PDB file and a reflection data file) and provide a concise summary output. Phenix.polygon (Urzhumtseva et al., 2009 ▶) is a graphical tool that is designed to indicate the similarity of validation parameters, such as free R value, for a particular structure compared with those deposited in the PDB. This comparison is performed for all other structures solved at similar resolution limits. The result is presented graphically. Phenix.validation combines all of the tools described above in one GUI, providing a single place for assessing the results of structure determination. 5.1. Model and structure-factor manipulation and analysis PHENIX has a range of tools for displaying, analyzing and manipulating structure-factor and model information. Phenix.mtz.dump and phenix.cif_as_mtz display and convert structure-factor data. Phenix.print_sequence, phenix.pdb_atom_selection and phenix.pdbtools display and manipulate coordinate files. Phenix.tls is a tool for the extraction and manipulation of TLS information. Using this tool, TLS matrices and selections can be extracted from REFMAC- or PHENIX-formatted PDB file headers and the total or residual atomic B factors can be computed and output. Future functionality will include the complete analysis of TLS matrices and their graphical visualization. Phenix.get_cc_mtz_mtz and phenix.get_cc_mtz_pdb are tools for analyzing the agreement between maps based on a pair of MTZ files or between maps calculated from an MTZ file and a PDB file. The key attributes of these tools are that they automatically search all allowed origin shifts that might relate the two maps and that they write out a modified version of one of the MTZ files or of the PDB file, shifted to match the other. 6. Structure refinement Phenix.refine is the state-of-the-art crystallographic structure-refinement engine of PHENIX. The foundational refinement machinery is a combination of highly efficient programming tools and new or rethought crystallographic algorithms. Phenix.refine possesses an extensive set of tools that cover the majority of refinement scenarios at any data resolution from low to ultrahigh. Various reflection-data formats (for example, CNS, MTZ and SHELX) are recognized automatically. The input experimental data are checked for outliers (Read, 1999 ▶; Zwart et al., 2005 ▶) and any reflections identified as such are excluded from the refinement calculations. Twinning can also be taken into account by providing a twin-law operator, which can be obtained using phenix.xtriage. Both X-ray and/or neutron diffraction data can be used and an option for joint XN refinement is available (simultaneous refinement against X-ray and neutron data; Adams, Mustyakimov et al., 2009 ▶). Each refinement run begins with robust mask-based bulk-solvent correction and anisotropic scaling (Afonine et al., 2005 ▶). Tools such as efficient rigid-body refinement (multiple-zones algorithm; Afonine et al., 2009 ▶), simulated-annealing refinement (Brünger et al., 1987 ▶) in Cartesian or torsion-angle space (Grosse-Kunstleve et al., 2009 ▶), automatic NCS detection and its use as restraints in refinement are important at low resolution and in the initial stages of refinement. A broad range of atomic displacement parameterizations are available, including grouped isotropic, constrained anisotropic (TLS) and individual atomic isotropic or anisotropic, allowing efficient modelling of atomic displacement parameters at any resolution. Occupancy refinement (grouped, individual, group constrained for alternative conformations or any mixture) can be performed for any user-defined atoms. Atoms in alternative conformations are recognized automatically based on altLoc identifiers in the input PDB file and their occupancies are refined by default. Ordered solvent (water) model updating is integrated into the refinement process. The availability of ultrahigh-resolution data makes it possible to visualize the residual density arising from bonding effects; phenix.refine employs a novel interatomic scatterers model (Afonine et al., 2007 ▶) to adequately account for these features. A flexible parameterization of H atoms allows their use at any resolution from subatomic (where their parameters can be refined individually) to low resolution (where a riding model is used). Refinement can be performed using a variety of refinement target functions, including maximum likelihood, maximum likelihood with experimental phase information and amplitude least squares. The refinement of coordinates can be performed in real or reciprocal space (allowing dual-space refinement). Novel ligands can easily be included in refinement by providing a corresponding CIF file as input (the CIF file can be automatically created using phenix.ready_set). Manual fixing of amino-acid side-chain rotamers can be time-consuming, especially for large structures. Although the use of simulated-annealing refinement increases the convergence radius, it can still fail to fit incorrectly modelled side chains into the correct density. Phenix.refine has an option for automatic selection of the best rotamer based on a rotamer library (Lovell et al., 2000 ▶) and optimal fit into the density (details to be published elsewhere). Furthermore, coupling real-space refinement with the built-in rotamer library and available MolProbity tools allows the automated identification and robust correction of common systematic errors involving backward-fit conformations for Leu, Thr, Val, Ile and Arg side chains, as developed and tested in the Autofix method (Headd et al., 2009 ▶). Phenix.refine allows multi-step complex refinement protocols in which most of the available refinement strategies can be combined with each other and applied to any selected part of the model. For example, a run of phenix.refine may perform rigid-body refinement, simulated annealing, individual and grouped B factors combined with TLS refinement, constrained occupancy refinement and automatic water picking. The output of phenix.refine includes various maps (maximum-likelihood weighted, kicked, incompleteness corrected, anomalous difference and those with any user-defined coefficients), complete model and data statistics and PDB file with a formatted REMARK 3 header ready for PDB deposition. The phenix.refine GUI is integrated with Coot and PyMOL, allowing seamless visual analysis of the refined model and associated maps. Phenix.refine is tightly integrated with other PHENIX components, making structure solution, building and refinement a one-step process (for example, in the AutoMR and AutoBuild wizards). It is routinely tested by automatic re-refinement of all models in the PDB for which the experimental data are available. 6.1. Ligand-coordinate and restraint-geometry generation The electronic Ligand Builder and Optimization Builder (eLBOW; Moriarty et al., 2009 ▶) is a suite of tools designed for the reliable generation of Cartesian coordinates and geometry restraints for both novel and known ligands. In line with the rest of the PHENIX package, the eLBOW modules are written in Python, with the numerically intensive portions of the code written in C++. eLBOW is a flexible platform for converting a majority of common chemical inputs to optimized three-dimensional coordinates and geometry restraints for refinement. Ligand geometries can be minimized using the semi-empirical AM1 quantum-chemical method (Stewart, 2004 ▶), a numerically efficient and chemically accurate technique for the class of molecules commonly complexed with or bound to proteins. In addition, a graphical user interface for editing geometry restraints and simple geometry manipulation of ligands has been developed. The Restraints Editor, Especially Ligands (REEL) removes the tedium of manually editing a restraints file by providing a number of commonly performed actions via pull-down menus and other interactive features. The effect of changes in the restraints can be immediately reflected in the molecule view to provide user feedback. A tool that uses many of the features of eLBOW to quickly and easier prepare a protein model for refinement is known as ReadySet! The flexibility of the Python interface is exemplified by the use of Reduce, eLBOW and several smaller portions of the cctbx toolkit to add H and/or D atoms to the model, ligands and water and to generate metal-coordination files and geometry restraints for unknown ligands. The files required for covalently bound ligands are also generated. 7. Integrated structure determination 7.1. Why automation? Automation has dramatically changed macromolecular crystallography over the past decade, both by greatly speeding up the process of structure solution, model building and refinement and by bringing the tools for structure determination to a much wider group of scientists. As automation becomes increasingly comprehensive, it will allow users to test many more possibilities for structure determination, will allow improved estimation of uncertainties in the final structures and will allow the determination of ever more complex and difficult structures. The PHENIX environment has been developed with automation as a key and defining feature. Each tool within PHENIX can seamlessly and nearly effortlessly be incorporated as part of any other tool or process in PHENIX. This means that very complex tasks can be built up from well tested and characterized tools and that tools and higher-level methods can be re-used in many different contexts. With a full automatic regression testing system as an integral part of the PHENIX environment, all these tasks and high-level methods are tested daily to ensure the integrity of the entire PHENIX system. 7.2. Automated structure solution PHENIX has fully integrated structure-solution capability for both experimental phasing (MAD, SAD, MIR and combinations of these), carried out by phenix.autosol, and for molecular replacement, performed by phenix.automr. Each of these automated procedures feeds directly into the iterative model building, density modification and refinement of phenix.autobuild. Phenix.autosol is designed to allow complete automation of experimental phasing while allowing a high degree of flexibility for advanced users. Beginning with structure-factor amplitudes and the sequence of the macromolecule, phenix.autosol uses phenix.solve (Terwilliger & Berendzen, 1999 ▶) to scale all data sets, phenix.xtriage (Zwart et al., 2005 ▶) to analyze the data for twinning and to correct any anisotropy in the data and phenix.hyss (Grosse-Kunstleve & Adams, 2003 ▶) to find potential heavy-atom or anomalously scattering atoms. Phenix.autosol carries out experimental phasing with phenix.phaser (McCoy et al., 2004 ▶, 2007 ▶) or phenix.solve (Terwilliger & Berendzen, 1999 ▶), density modification with phenix.resolve (Terwilliger, 1999 ▶) and preliminary model building using the methods in phenix.autobuild (Terwilliger, Grosse-Kunstleve, Afonine, Moriarty, Zwart et al., 2008 ▶). A key step in automated structure solution is the identification of which of several possible space-group and heavy-atom or anomalously scattering-atom substructures is correct. Phenix.autosol uses a Bayesian scoring algorithm based on analysis of the experimental electron-density maps to identify which substructures lead to the best maps (Terwilliger et al., 2009 ▶). The main features of the maps that are used in this evaluation are the skewness of the electron density (non-Gaussian histogram of density with more density in the positive tail than the negative tail) and the correlation of local r.m.s. density (large contiguous regions of high variation where the molecule is located and separate large contiguous regions of low variation where the solvent is located). Phenix.autosol is highly flexible, allowing any combination of experimental data, such as MAD + SIRAS or several SAD data sets. Although it is fully automated, the user can control nearly all aspects of the operation of the procedure, including the scoring criteria and decisions about how certain phenix.autosol should be that the correct solution is contained in the current lists of solutions. Phenix.autosol can carry out phasing using a combination of experimental SAD data and molecular-replacement information. If a molecular-replacement model is available, phenix.autosol will use phenix.phaser (McCoy et al., 2004 ▶, 2007 ▶) to complete the anomalous substructure iteratively by constructing log-likelihood gradient maps for the anomalous scatterers based on the model of the non-anomalous structure and any anomalous scatterers that have already been found. The anomalous substructure is then used along with the model to calculate phases with phenix.phaser. Phenix.automr carries out automated likelihood-based molecular replacement using phenix.phaser (Read, 2001 ▶; McCoy et al., 2005 ▶, 2007 ▶; McCoy, 2007 ▶). The procedure is highly automated, allowing several copies of each of several components to be placed in a single run, which can also test different possible choices of space group. If there are alternative choices of model for a component, the molecular-replacement calculation can try each of them in turn or combine them as a statistically weighted ensemble. Although the evaluation of the likelihood targets is slow (Read, 2001 ▶), the use of fast approximations for the rotation search (Storoni et al., 2004 ▶) and the translation search (McCoy et al., 2005 ▶) gives run times that are competitive with traditional Patterson-based methods. Likelihood has been demonstrated to be more sensitive to the correct solution, particularly in difficult cases (Read, 2001 ▶). When there are several copies or several components to place, the ability of the likelihood functions to take advantage of preliminary partial solutions can provide a crucial increase in the signal. 7.3. Iterative model building, density modification and refinement Phenix.autobuild is a highly integrated and automated procedure for model building and model improvement through iterative model building, density modification and refinement. Phenix.autobuild uses phenix.resolve (Terwilliger, 2003a ▶,b ▶) to carry out model building, model extension, model assembly, loop fitting and building outside existing models. It further uses phenix.resolve to improve electron-density maps with statistical density modification, including information from the newly built models as well as that obtained from experiment (e.g. phenix.autosol), from NCS (Terwilliger, 2002b ▶) and from other expected features of electron-density maps such as a flat solvent (Wang, 1985 ▶), the presence of secondary-structural features (Terwilliger, 2001 ▶) and the presence of local patterns of density characteristic of macromolecules (Terwilliger, 2003c ▶). To reduce model bias in the procedure, prime-and-switch phasing can also be used (Terwilliger, 2004 ▶). Phenix.autobuild uses phenix.refine (Afonine et al., 2005 ▶) throughout this process to improve the quality of the models that are built. Phenix.autobuild provides two complementary approaches to model building. For cases in which no model or only a preliminary model has been built, phenix.autobuild will construct a new model considering the main chain of any supplied models as potential coordinates. In cases where a nearly final model is available, phenix.autobuild can apply a rebuild-in-place approach in which the polypeptide chain is rebuilt a few residues at a time without changing the register or the overall features of the model. The rebuild-in-place approach in phenix.autobuild provides a powerful method for the assessment of uncertainties in an atomic model by repetitive rebuilding of the model using different random seeds for each iteration (Terwilliger, Grosse-Kunstleve et al., 2007 ▶). The variability in the coordinates of each atom in the ensemble that is created is a lower bound on the uncertainty of the position of that atom. 8. Conclusions Advances in computational methods and algorithms have made it possible to automate the solution of many structures with PHENIX. However, many challenges still exist. In particular, the development of automated methods that can be applied at low resolution (worse than 3.0 Å) remains a priority. In this resolution range there are typically too few experimental data to uniquely define the macromolecular structure for automated ab initio model building. Thus, methods are required that rely on prior knowledge from existing macromolecular structures to permit productive automated data interpretation. These methods will need to be developed and applied for all stages of structure solution and tightly integrated to maximize the information extracted from the experimental data.

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Contributors

David A. Jacques:

ORCID: http://orcid.org/0000-0002-6426-4510

d.jacques@unsw.edu.au

Greg J. Towers:

ORCID: http://orcid.org/0000-0002-7707-0264

g.towers@ucl.ac.uk

Journal

Journal ID (nlm-ta): Nat Microbiol

Journal ID (iso-abbrev): Nat Microbiol

Title: Nature Microbiology

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2058-5276

Publication date (Electronic): 26 October 2022

Publication date PMC-release: 26 October 2022

Publication date (Print): 2022

Volume: 7

Issue: 11

Pages: 1762-1776

Affiliations

[1 ]GRID grid.83440.3b, ISNI 0000000121901201, Division of Infection and Immunity, UCL, ; London, UK

[2 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, EMBL Australia Node in Single Molecule Science, School of Medical Sciences, , UNSW Sydney, ; Sydney, New South Wales Australia

[3 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA

[4 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Microbiology, , University of Pennsylvania, ; Philadelphia, PA USA

[5 ]GRID grid.88379.3d, ISNI 0000 0001 2324 0507, Institute of Structural and Molecular Biology, , Birkbeck College, ; London, UK

[6 ]GRID grid.42475.30, ISNI 0000 0004 0605 769X, MRC Laboratory of Molecular Biology, ; Cambridge, UK

[7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Present Address: Quantitative Biosciences Institute (QBI), , University of California San Francisco, ; San Francisco, CA USA

[8 ]Present Address: Carnall Farrar, London, UK

[9 ]Present Address: Quell Therapeutics Ltd, Translation & Innovation Hub, London, UK

[10 ]Present Address: Nucleus Global, London, UK

Author information

Lorena Zuliani-Alvarez http://orcid.org/0000-0002-4682-4043

Claire Dickson http://orcid.org/0000-0002-1972-4143

Beatrice H. Hahn http://orcid.org/0000-0002-9400-9887

Till Boecking http://orcid.org/0000-0003-1165-3122

Nikos Pinotsis http://orcid.org/0000-0002-5096-257X

Leo C. James http://orcid.org/0000-0003-2131-0334

David A. Jacques http://orcid.org/0000-0002-6426-4510

Greg J. Towers http://orcid.org/0000-0002-7707-0264

Article

Publisher ID: 1247

DOI: 10.1038/s41564-022-01247-0

PMC ID: 9613477

PubMed ID: 36289397

SO-VID: e786bfec-361d-4166-8259-3268b0bee06e

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 4 February 2022

Date accepted : 8 September 2022

Funding

Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);

Award ID: 220863

Award ID: 108183

Award ID: 220863

Award ID: 108183

Award ID: 220863

Award Recipient : Lorena Zuliani-Alvarez Morten L. Govasli Jane Rasaiyaah Chris Monit Greg J. Towers

Funded by: UCL MRC doctoral training programme PhD studentships

Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);

Award ID: 339223

Award Recipient : Rebecca P. Sumner Greg J. Towers

Funded by: FundRef https://doi.org/10.13039/501100000923, Department of Education and Training | Australian Research Council (ARC);

Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);

Award ID: AI120810

Award Recipient : Frederic Bibollet-Ruche Beatrice H. Hahn

Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);

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