3D protein-protein docking using shape complementarity and fast alignment

2011 ◽  
Author(s):  
Apostolos Axenopoulos ◽  
Petros Daras ◽  
Georgios Papadopoulos ◽  
Elias Houstis
Keyword(s):  
Protein Docking ◽  
Shape Complementarity

scholarly journals Protein-protein docking using learned three-dimensional representations

2019 ◽  
Author(s):  
Georgy Derevyanko ◽  
Guillaume Lamoureux
Keyword(s):  
Protein Interactions ◽  
Network Architecture ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Translational Invariance ◽  
Shape Complementarity ◽  
Spatial Features

AbstractProtein-protein interactions are determined by a number of hard-to-capture features related to shape complementarity, electrostatics, and hydrophobicity. These features may be intrinsic to the protein or induced by the presence of a partner. A conventional approach to protein-protein docking consists in engineering a small number of spatial features for each protein, and in minimizing the sum of their correlations with respect to the spatial arrangement of the two proteins. To generalize this approach, we introduce a deep neural network architecture that transforms the raw atomic densities of each protein into complex three-dimensional representations. Each point in the volume containing the protein is described by 48 learned features, which are correlated and combined with the features of a second protein to produce a score dependent on the relative position and orientation of the two proteins. The architecture is based on multiple layers of SE(3)-equivariant convolutional neural networks, which provide built-in rotational and translational invariance of the score with respect to the structure of the complex. The model is trained end-to-end on a set of decoy conformations generated from 851 nonredundant protein-protein complexes and is tested on data from the Protein-Protein Docking Benchmark Version 4.0.

scholarly journals Pushing the accuracy limit of shape complementarity for protein-protein docking

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Yumeng Yan ◽  
Sheng-You Huang
Keyword(s):  
Success Rate ◽  
Protein Interactions ◽  
Shape Representation ◽  
Scoring Function ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Second Best ◽  
Shape Complementarity ◽  
Docking Program ◽  
Docking Approach

Abstract Background Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches. Results We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1. Conclusions The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

scholarly journals A Unified Protein Docking Procedure with a Shape Complementarity Screening using 3D Zernike Descriptors

2010 ◽  
Vol 98 (3) ◽  
pp. 462a-463a
Author(s):  
Daisuke Kihara ◽  
Vishwesh Venkatraman ◽  
Yifeng D. Yang ◽  
Lee Sael
Keyword(s):  
Protein Docking ◽  
Shape Complementarity

RESIDUE CONSERVATION INFORMATION FOR GENERATING NEAR-NATIVE STRUCTURES IN PROTEIN–PROTEIN DOCKING

2006 ◽  
Vol 04 (04) ◽  
pp. 793-806 ◽  
Author(s):  
YUHUA DUAN ◽  
BOOJALA V. B. REDDY ◽  
YIANNIS N. KAZNESSIS
Keyword(s):  
Protein Complexes ◽  
Predictive Ability ◽  
Accessible Surface Area ◽  
Protein Docking ◽  
Solvent Accessible Surface Area ◽  
Complex Structures ◽  
Native Structure ◽  
Interacting Protein ◽  
Residue Conservation ◽  
Shape Complementarity

Motivation: Protein–protein docking algorithms typically generate large numbers of possible complex structures with only a few of them resembling the native structure. Recently (Duan et al., Protein Sci, 14:316–218, 2005), it was observed that the surface density of conserved residue positions is high at the interface regions of interacting protein surfaces, except for antibody–antigen complexes, where a lesser number of conserved positions than average is observed at the interface regions. Using this observation, we identified putative interacting regions on the surface of interacting partners and significantly improved docking results by assigning top ranks to near-native complex structures. In this paper, we combine the residue conservation information with a widely used shape complementarity algorithm to generate candidate complex structures with a higher percentage of near-native structures (hits). What is new in this work is that the conservation information is used early in the generation stage and not only in the ranking stage of the docking algorithm. This results in a significantly larger number of generated hits and an improved predictive ability in identifying the native structure of protein–protein complexes. Results: We report on results from 48 well-characterized protein complexes, which have enough residue conservation information from the same 59 benchmark complexes used in our previous work. We compute conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences from UNIPROT and calculate the solvent accessible surface area. We combine this information with shape-complementarity scores to generate candidate protein–protein complex structures. When compared with pure shape-complementarity algorithms, performed by FTDock, our method results in significantly more hits, with the improvement being over 100% in many instances. We demonstrate that residue conservation information is useful not only in refinement and scoring of docking solutions, but also helpful in enrichment of near-native-structures during the generation of candidate geometries of complex structures.

scholarly journals A novel volumetric criterion for optimal shape matching of surfaces for protein-protein docking

2017 ◽  
Vol 5 (2) ◽  
pp. 180-190
Author(s):  
Hari K. Voruganti ◽  
Bhaskar Dasgupta
Keyword(s):  
Shape Matching ◽  
Contact Point ◽  
Scoring Function ◽  
Protein Docking ◽  
Surface Generation ◽  
Shape Complementarity ◽  
Surface Patches ◽  
Docking Algorithm ◽  
Cad Cam ◽  
Top 40

Abstract The problem of molecular docking is to predict whether two given molecules bind together to interact. A shape-based algorithm is proposed for predictive docking by noting that shape complementarity between their outer surfaces is necessary for two molecules to bind. A methodology with five stages has been developed to find the pose in which the shape complementarity is maximum. It involves surface generation, segmentation, parameterization, shape matching, and filtering and scoring. The most significant contribution of this paper is the novel scoring function called ‘Normalized Volume Mismatch’ which evaluates the matching between a pair of surface patches efficiently by measuring the gap or solid volume entrapped between two patches of a pair of proteins when they are placed one against the other at a contact point. After the evaluation, it is found that, with local shape complementarity as the only criterion, the algorithm is able to predict a conformation close to the exact one, in case of known docking conformations, and also rank the same among the top 40 solutions. This is remarkable considering the fact that many existing docking methods fail to rank a near-native conformation among top 50 solutions. The shape-based approaches are used for the initial stage of docking to identify a small set of candidate solutions to be investigated further with exhaustive energy studies etc. The ability of capturing the correct conformation as highly ranked among top few candidate solutions is the most valuable facet of this new predictive docking algorithm. Highlights A new rigid-body docking algorithm is proposed for protein–protein docking. An approach using techniques of cad/cam for a problem in biology is presented. Unlike many existing ones, a volume based scoring criterion is proposed. The new criteria can capture even multiple possible docking conformation. Entire automatic docking procedures is based on shape complementarity only.

scholarly journals ResDock: Protein-Protein Docking Using Shape Complementarity of Surface Residues

2021 ◽  
Author(s):  
Sharon Sunny ◽  
Jayaraj PB
Keyword(s):  
Ab Initio ◽  
Protein Complex ◽  
Structure Prediction ◽  
Complex Structure ◽  
Scoring Function ◽  
Protein Docking ◽  
New Method ◽  
Protein Surfaces ◽  
Shape Complementarity ◽  
Conformation Space

ResDock is a new method to improve the performance of protein-protein complex structure prediction. It utilizes shape complementarity of the protein surfaces to generate the conformation space. The use of an appropriate scoring function helps to select the feasible structures. An interplay between pose generation phase and scoring phase enhance the performance of the proposed ab initio technique. <br>

Sign in / Sign up

Export Citation Format

Share Document