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

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.

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Induced fit with replica exchange improves protein complex structure prediction

10.1101/2021.12.08.471786 ◽

2021 ◽

Author(s):

Ameya Harmalkar ◽

Sai Pooja Mahajan ◽

Jeffrey J. Gray

Keyword(s):

Conformational Changes ◽

Protein Complex ◽

Structure Prediction ◽

Protein Complexes ◽

Complex Structure ◽

Replica Exchange ◽

Conformational Sampling ◽

Success Rates ◽

Induced Fit ◽

Protein Size

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Angstroms), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 angstrom accuracy. This indicates that additional gains are possible when mobile protein segments are known.

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

10.1101/2021.12.21.473437 ◽

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.

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Faculty Opinions recommendation of Evaluating template-based and template-free protein-protein complex structure prediction.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718025638.793480806 ◽

2013 ◽

Author(s):

Yaoqi Zhou

Keyword(s):

Protein Complex ◽

Structure Prediction ◽

Complex Structure ◽

Free Protein ◽

Template Free

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pyDockEneRes: per-residue decomposition of protein–protein docking energy

Bioinformatics ◽

10.1093/bioinformatics/btz884 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2284-2285 ◽

Cited By ~ 1

Author(s):

Miguel Romero-Durana ◽

Brian Jiménez-García ◽

Juan Fernández-Recio

Keyword(s):

Binding Affinity ◽

Protein Interactions ◽

Structural Model ◽

Protein Complexes ◽

Complex Structure ◽

Protein Docking ◽

Supplementary Information ◽

Scoring Functions ◽

Residue Decomposition ◽

Docking Energy

Abstract Motivation Protein–protein interactions are key to understand biological processes at the molecular level. As a complement to experimental characterization of protein interactions, computational docking methods have become useful tools for the structural and energetics modeling of protein–protein complexes. A key aspect of such algorithms is the use of scoring functions to evaluate the generated docking poses and try to identify the best models. When the scoring functions are based on energetic considerations, they can help not only to provide a reliable structural model for the complex, but also to describe energetic aspects of the interaction. This is the case of the scoring function used in pyDock, a combination of electrostatics, desolvation and van der Waals energy terms. Its correlation with experimental binding affinity values of protein–protein complexes was explored in the past, but the per-residue decomposition of the docking energy was never systematically analyzed. Results Here, we present pyDockEneRes (pyDock Energy per-Residue), a web server that provides pyDock docking energy partitioned at the residue level, giving a much more detailed description of the docking energy landscape. Additionally, pyDockEneRes computes the contribution to the docking energy of the side-chain atoms. This fast approach can be applied to characterize a complex structure in order to identify energetically relevant residues (hot-spots) and estimate binding affinity changes upon mutation to alanine. Availability and implementation The server does not require registration by the user and is freely accessible for academics at https://life.bsc.es/pid/pydockeneres. Supplementary information Supplementary data are available at Bioinformatics online.

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Protein docking model evaluation by 3D deep convolutional neural networks

Bioinformatics ◽

10.1093/bioinformatics/btz870 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2113-2118 ◽

Cited By ~ 7

Author(s):

Xiao Wang ◽

Genki Terashi ◽

Charles W Christoffer ◽

Mengmeng Zhu ◽

Daisuke Kihara

Keyword(s):

Neural Network ◽

Structure Prediction ◽

Deep Neural Network ◽

Molecular Mechanisms ◽

Complex Structure ◽

Protein Docking ◽

Supplementary Information ◽

Atomic Interaction ◽

Deep Convolutional Neural Networks ◽

Docking Model

Abstract Motivation Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models. Results We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein–protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions. Availability and implementation Codes available at http://github.com/kiharalab/DOVE, http://kiharalab.org/dove/. Supplementary information Supplementary data are available at Bioinformatics online.

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Evaluating template-based and template-free protein-protein complex structure prediction

Briefings in Bioinformatics ◽

10.1093/bib/bbt047 ◽

2013 ◽

Vol 15 (2) ◽

pp. 169-176 ◽

Cited By ~ 22

Author(s):

T. Vreven ◽

H. Hwang ◽

B. G. Pierce ◽

Z. Weng

Keyword(s):

Protein Complex ◽

Structure Prediction ◽

Complex Structure ◽

Free Protein ◽

Template Free

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

10.36227/techrxiv.13619462 ◽

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>

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

10.36227/techrxiv.13619462.v1 ◽

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

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Protein-protein docking with large-scale backbone flexibility

10.1101/2021.02.22.432196 ◽

2021 ◽

Author(s):

Mateusz Kurcinski ◽

Sebastian Kmiecik ◽

Mateusz Zalewski ◽

Andrzej Kolinski

Keyword(s):

Binding Site ◽

Structure Prediction ◽

Large Scale ◽

Protein Complexes ◽

Complex Structure ◽

Protein Docking ◽

Coarse Grained ◽

Backbone Flexibility ◽

Docking Simulations ◽

Docking Protocol

AbstractStructure prediction of protein-protein complexes is one of the most critical challenges in computational structural biology. It is often difficult to predict the complex structure, even for relatively rigid proteins. Modeling significant structural flexibility in protein docking remains an unsolved problem. This work demonstrates a protein-protein docking protocol with enhanced sampling that accounts for large-scale backbone flexibility. The docking protocol starts from unbound x-ray structures and is not using any binding site information. In docking, one protein partner undergoes multiple fold rearrangements, rotations, and translations during docking simulations, while the other protein exhibits small backbone fluctuations. Including significant backbone flexibility during the search for the binding site has been made possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics. In our simulations, we obtained acceptable quality models for the set of 12 protein-protein complexes, while for selected cases, models were close to high accuracy.

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UNRES-Dock—protein–protein and peptide–protein docking by coarse-grained replica-exchange MD simulations

Bioinformatics ◽

10.1093/bioinformatics/btaa897 ◽

2020 ◽

Cited By ~ 1

Author(s):

Paweł Krupa ◽

Agnieszka S Karczyńska ◽

Magdalena A Mozolewska ◽

Adam Liwo ◽

Cezary Czaplewski

Keyword(s):

Protein Complexes ◽

Md Simulations ◽

Protein Docking ◽

Conformational Space ◽

Coarse Grained ◽

Supplementary Information ◽

Replica Exchange ◽

Variable Degree ◽

Single Chain ◽

Simulation Speed

Abstract Motivation The majority of the proteins in living organisms occur as homo- or hetero-multimeric structures. Although there are many tools to predict the structures of single-chain proteins or protein complexes with small ligands, peptide–protein and protein–protein docking is more challenging. In this work, we utilized multiplexed replica-exchange molecular dynamics (MREMD) simulations with the physics-based heavily coarse-grained UNRES model, which provides more than a 1000-fold simulation speed-up compared with all-atom approaches to predict structures of protein complexes. Results We present a new protein–protein and peptide–protein docking functionality of the UNRES package, which includes a variable degree of conformational flexibility. UNRES-Dock protocol was tested on a set of 55 complexes with size from 43 to 587 amino-acid residues, showing that structures of the complexes can be predicted with good quality, if the sampling of the conformational space is sufficient, especially for flexible peptide–protein systems. The developed automatized protocol has been implemented in the standalone UNRES package and in the UNRES server. Availability and implementation UNRES server: http://unres-server.chem.ug.edu.pl; UNRES package and data used in testing of UNRES-Dock: http://unres.pl. Supplementary information Supplementary data are available at Bioinformatics online.

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