DeepSite: protein-binding site predictor using 3D-convolutional neural networks

The NGL Viewer (http://proteinformatics.charite.de/ngl) is a web application for the visualization of macromolecular structures. By fully adopting capabilities of modern web browsers, such as WebGL, for molecular graphics, the viewer can interactively display large molecular complexes and is also unaffected by the retirement of third-party plug-ins like Flash and Java Applets. Generally, the web application offers comprehensive molecular visualization through a graphical user interface so that life scientists can easily access and profit from available structural data. It supports common structural file-formats (e.g. PDB, mmCIF) and a variety of molecular representations (e.g. ‘cartoon, spacefill, licorice’). Moreover, the viewer can be embedded in other web sites to provide specialized visualizations of entries in structural databases or results of structure-related calculations. Show more

Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features. Show more

LIGSITE is a new program for the automatic and time-efficient detection of pockets on the surface of proteins that may act as binding sites for small molecule ligands. Pockets are identified with a series of simple operations on a cubic grid. Using a set of receptor-ligand complexes we show that LIGSITE is able to identify the binding sites of small molecule ligands with high precision. The main advantage of LIGSITE is its speed. Typical search times are in the range of 5 to 20 s for medium-sized proteins. LIGSITE is therefore well suited for identification of pockets in large sets of proteins (e.g., protein families) for comparative studies. For graphical display LIGSITE produces VRML representations of the protein-ligand complex and the binding site for display with a VRML viewer such as WebSpace from SGI. Show more