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Allows structural prediction of protein-protein interactions (PPIs). pyDock is a docking protocol scoring docking poses generated with FFT-based algorithms. The software contains the pyDockNIP module for predicting interface residues in a given protein-protein complex. It can improve the characterization of genomic variants involved in PPIs, especially in cases with low or limited structural information on the binding complexes. The pyDockWEB server allows the academic community to use the pyDock rigid-body docking and scoring method.
Predicts structures of transmembrane protein complexes. DOCK/PIERR is a docking algorithm that predicts, in atomic resolution, the structure of the complex formed by two proteins, given their individual tertiary structure. The conformational space of complexes is sampled exhaustively using Fast Fourier Transforms. The software uses the potentials Protein Interaction Energy (PIE) and Protein Interfaces, Surfaces and Assemblies (PISA) for scoring residue and atomic contacts at protein interfaces.
A server for protein docking based on a free rigid-body docking strategy. It relies on the integration of various components for decoy generation and scoring. Particularly, the combination of the SOAP-PP, FRODOCK and InterEvScore makes it very efficient for the identification of complex conformations not undergoing large conformational changes. The server has many advantages: a user-specific workspace for easy job management, fast evaluation of several tens of thousands of models, high success rates of the consensus method and a user-friendly graphical interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective.
HADDOCK / High Ambiguity Driven protein-protein DOCKing
An information-driven flexible docking approach for the modeling of biomolecular complexes. HADDOCK distinguishes itself from ab-initio docking methods in the fact that it encodes information from identified or predicted protein interfaces in ambiguous interaction restraints (AIRs) to drive the docking process. HADDOCK can deal with a large class of modeling problems including protein-protein, protein-nucleic acids and protein-ligand complexes.
Aims to reduce visual “clutter” by using a single-window approach. Sculptor is a multi-scale modeling program which combines various visualization techniques with pattern matching and feature extraction algorithms. The main user interface elements are arranged around a central 3D graphics area, using non-overlapping frames. Using graphics card processors (GPUs) acceleration, this method is able to display molecular systems with hundreds of thousands of atoms in various rendering styles.
LZerD / Local 3D Zernike descriptor-based Docking algorithm
Generates ligand orientations using geometric hashing and the 3D Zernike descriptor. LZerD is a protein-protein docking program that represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The 3DZD are a soft representation of the surface shape, which confers tolerance to the conformational changes associated with binding. The software generates docking decoys.
PEPSI-Dock / Polynomial Expansion of Protein Structures and Interactions for Docking
Combines a distant-dependent knowledge-based potential with Fast Fourier Transform (FFT)-accelerated exhaustive sampling on spherical grids. Our potential approximates the binding free energy of protein complexes. We deduce its polynomial expansion coefficients using a training set of protein–protein interfaces and a novel convex optimization problem inspired by a robust machine learning technique. Then, we insert the obtained expansion coefficients into the Hex exhaustive sampling library. This is the first attempt to combine data-driven arbitrary-shaped potentials with a FFT-exhaustive search.
Finds docking transformations that yield good molecular shape complementarity. A wide interface is ensured to include several matched local features of the docked molecules. PatchDock divides the Connolly dot surface representation of the molecules into concave, convex and flat patches. Then, complementary patches are matched to generate candidate transformations. Each candidate is further evaluated by a scoring function that considers both geometric fit and atomic desolvation. An root mean square deviation clustering is applied to the candidate solutions to discard redundant solutions. PatchDock performs structure prediction of protein–protein and protein–small molecule complexes.
Calculates bimolecular protein–protein association rate constants. SDA has been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. It offers the possibility to account for different conformations of the solutes and parallelization on multi-core processors for bimolecular simulations.
PROBE / PROtein Binding Evaluation
Refines, scores and ranks a subset of docking poses generated during sampling search. PROBE is a web service performs ranking of pruned poses, after structure refinement and scoring using a regression model for geometric compatibility, and normalized interaction energy. The former uses an interface area based edge-scoring function to eliminate >95% of the poses generated during docking search. The method has been shown to efficiently score docking poses using its simple scoring function.
Selects a subset of docking poses generated during sampling search. PRUNE uses a single parameter, interface-area-based edge function that has been shown to time-efficiently select subsets of docking poses for improved scoring and ranking of poses. It calculates the intelligence artificial (IA) of the unrefined docking poses generated between the reference (static) and the mobile molecule. The tool was tested on 922 bound, and 77 unbound binary docking targets covering 193–7658A2 interface ranges.
COCOMAPS / bioCOmplexes COntact MAPS
A web application to easily and effectively analyse and visualize the interface in biological complexes (such as protein-protein, protein-DNA and protein-RNA complexes), by making use of intermolecular contact maps. The user only needs to download input files directly from the wwPDB or upload her/his own PDB formatted files and to specify the chain identifiers for the molecules involved in the interaction to be analysed. Please note that more chains can be selected for each interacting partner.
A scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein–protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further.
Establishes a link between topographic images from atomic force microscopy (AFM) and molecular dynamics of single proteins. DockAFM is an online computational tool that computes the fit of input conformations of a molecule with the topographic surface of AFM images. It can be used to benchmark protein 3D structures or models against an experimental data obtained by AFM. DockAFM uses a real-space description of atoms and surfaces and has been developed as the first step to assemble large macromolecules using their individual constituent.
Allows protein-protein docking. LightDock is based on the Glowworm Swarm Optimization (GSO) algorithm for sampling the translational and rotational space of protein-protein docking, and anisotropic network model (ANM) representation for the inclusion of flexibility. The software allows testing and development of new scoring strategies for protein-protein docking. It can accommodate many different scoring functions (alone or in combination) at different resolution level, and conformational flexibility.
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