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>

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>

A Geometric Complementarity-Based Tool for Protein–Protein Docking

2021 ◽  
Author(s):  
Sharon Sunny ◽  
P. B. Jayaraj
Keyword(s):  
Structure Prediction ◽  
Complex Structure ◽  
Protein Docking ◽  
Geometric Algorithms ◽  
Coarse Grained ◽  
Biological Impact ◽  
Searching Strategy ◽  
Determining Factor ◽  
Docking Benchmark ◽  
Conformation Space

The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

scholarly journals Integrating ab initio and template-based algorithms for protein–protein complex structure prediction

2019 ◽  
Vol 36 (3) ◽  
pp. 751-757 ◽  
Author(s):  
Sweta Vangaveti ◽  
Thom Vreven ◽  
Yang Zhang ◽  
Zhiping Weng
Keyword(s):  
Protein Complex ◽  
Structure Prediction ◽  
Protein Complexes ◽  
Complex Structure ◽  
Protein Docking ◽  
Supplementary Information ◽  
Test Case ◽  
Binding Modes ◽  
Success Rates ◽  
Template Free

Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein–protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. Availability and implementation ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrating cross-linking experiments with ab initio protein-protein docking

2018 ◽  
Author(s):  
Thom Vreven ◽  
Devin K. Schweppe ◽  
Juan D. Chavez ◽  
Chad R. Weisbrod ◽  
Sayaka Shibata ◽  
...  
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Structure Prediction ◽  
Complex Structure ◽  
Protein Docking ◽  
Cross Linking ◽  
Test Cases ◽  
List Type ◽  
Cross Links ◽  
Chemical Cross Linking

ABSTRACTAb initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 12 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases the rank of the top-scoring near-native prediction was improved by at least two fold compared with docking without the cross-link information, and the success rates for the top 5 and top 10 predictions doubled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.HighlightsIncorporating low-resolution cross-linking experimental data in protein-protein docking algorithms improves performance more than two fold.Integration of protein-protein docking with chemical cross-linking reveals information on the configuration of higher order complexes.Symmetry analysis of protein-protein docking results improves the predictions of multimeric complex structures

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 Protein Complex Structure Prediction Powered by Multiple Sequence Alignment of Interologs from Multiple Taxonomic Ranks and AlphaFold2

2021 ◽  
Author(s):  
Yunda Si ◽  
Chengfei Yan
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Protein Complex ◽  
Structure Prediction ◽  
Complex Structure ◽  
Complex Structures ◽  
Success Rates ◽  
Multiple Sequence ◽  
Taxonomic Rank ◽  
Protein Protein Interaction

AlphaFold2 is expected to be able to predict protein complex structures as long as a multiple sequence alignment (MSA) of the interologs of the target protein-protein interaction (PPI) can be provided. However, preparing the MSA of protein-protein interologs is a non-trivial task. In this study, a simplified phylogeny-based approach was applied to generate the MSA of interologs, which was then used as the input of AlphaFold2 for protein complex structure prediction. Extensively benchmarked this protocol on non-redundant PPI dataset, we show complex structures of 79.5% of the bacterial PPIs and 49.8% of the eukaryotic PPIs can be successfully predicted. Considering PPIs may not be conserved in species with long evolutionary distances, we further restricted interologs in the MSA to different taxonomic ranks of the species of the target PPI in protein complex structure prediction. We found the success rates can be increased to 87.9% for the bacterial PPIs and 56.3% of the eukaryotic PPIs if interologs in the MSA are restricted to a specific taxonomic rank of the species of each target PPI. Finally, we show the optimal taxonomic ranks for protein complex structure prediction can be selected with the application of the predicted TM-scores of the output models.

scholarly journals Using 3dRPC for RNA–protein complex structure prediction

2016 ◽  
Vol 2 (5-6) ◽  
pp. 95-99 ◽  
Author(s):  
Yangyu Huang ◽  
Haotian Li ◽  
Yi Xiao
Keyword(s):  
Protein Complex ◽  
Structure Prediction ◽  
Complex Structure
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