Atomic-level evolutionary information improves protein-protein interface scoring

Abstract Motivation The crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination. Results : We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of individual atomic-level scores SOAP-PP and Rosetta ISC by respectively 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%. Availability All data used for benchmarking and scoring results, as well as a Singularity container of the pipeline, are available at http://biodev.cea.fr/interevol/interevdata/ Supplementary information Supplementary data are available at Bioinformatics online.

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Atomic-level evolutionary information improves protein-protein interface scoring

10.1101/2020.10.26.355073 ◽

2020 ◽

Author(s):

Chloé Quignot ◽

Pierre Granger ◽

Pablo Chacón ◽

Raphael Guerois ◽

Jessica Andreani

Keyword(s):

Success Rate ◽

Protein Interactions ◽

Protein Docking ◽

Atomic Level ◽

Evolutionary Information ◽

General Strategy ◽

Scoring Functions ◽

Success Rates ◽

Novel Strategy ◽

Individual Scores

AbstractThe crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination.We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of these individual scores by respectively 6.5, 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%.All data used for benchmarking and scoring results, as well as pipelining scripts, are available at http://biodev.cea.fr/interevol/interevdata/

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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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A knowledge-based scoring function to assess quaternary associations of proteins

Bioinformatics ◽

10.1093/bioinformatics/btaa207 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3739-3748

Author(s):

Abhilesh S Dhawanjewar ◽

Ankit A Roy ◽

Mallur S Madhusudhan

Keyword(s):

Protein Interactions ◽

Statistical Physics ◽

Binary Classification ◽

Scoring Function ◽

Protein Docking ◽

Supplementary Information ◽

Scoring Functions ◽

Biological Interactions ◽

Protein Protein Interactions ◽

Knowledge Based

Abstract Motivation The elucidation of all inter-protein interactions would significantly enhance our knowledge of cellular processes at a molecular level. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study, we have devised a new scheme for scoring protein–protein interactions. Results Our method, PIZSA (Protein Interaction Z-Score Assessment), is a binary classification scheme for identification of native protein quaternary assemblies (binders/nonbinders) based on statistical potentials. The scoring scheme incorporates residue–residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other contact-based potential scoring functions. The method has been extensively benchmarked and is among the top 6 methods, outperforming 31 other statistical, physics based and machine learning scoring schemes. The PIZSA potentials can also distinguish crystallization artifacts from biological interactions. Availability and implementation PIZSA is implemented as a web server at http://cospi.iiserpune.ac.in/pizsa and can be downloaded as a standalone package from http://cospi.iiserpune.ac.in/pizsa/Download/Download.html. Supplementary information Supplementary data are available at Bioinformatics online.

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P3DOCK: a protein–RNA docking webserver based on template-based and template-free docking

Bioinformatics ◽

10.1093/bioinformatics/btz478 ◽

2019 ◽

Vol 36 (1) ◽

pp. 96-103 ◽

Cited By ~ 4

Author(s):

Jinfang Zheng ◽

Xu Hong ◽

Juan Xie ◽

Xiaoxue Tong ◽

Shiyong Liu

Keyword(s):

Success Rate ◽

Clustering Algorithm ◽

Hybrid Methods ◽

3D Structure ◽

Three Dimensional ◽

Protein Docking ◽

Ease Of Use ◽

Supplementary Information ◽

Main Function ◽

Rna Interaction

AbstractMotivationThe main function of protein–RNA interaction is to regulate the expression of genes. Therefore, studying protein–RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein–protein docking. Therefore, integrating these two methods may improve the prediction accuracy.ResultsIn this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3–20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK’s decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein–RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html.Availability and implementationwww.rnabinding.com/P3DOCK/P3DOCK.html.Supplementary informationSupplementary data are available at Bioinformatics online.

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

BMC Bioinformatics ◽

10.1186/s12859-019-3270-y ◽

2019 ◽

Vol 20 (S25) ◽

Cited By ~ 8

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/.

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MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

10.1101/2021.10.06.463442 ◽

2021 ◽

Author(s):

Yong Jung ◽

Cunliang Geng ◽

Alexandre M. J. J. Bonvin ◽

Li C Xue ◽

Vasant G Honavar

Keyword(s):

Machine Learning ◽

Protein Interactions ◽

Chemical Properties ◽

Protein Docking ◽

Structural Basis ◽

Large Set ◽

Scoring Functions ◽

Computational Docking ◽

3D Structures ◽

Over The Top

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking - the so-called scoring problem - still has considerable room for improvement. We present here MetaScore, a new machine-learning based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using a rich set of features extracted from the respective protein-protein interfaces. These include physico-chemical properties, energy terms, interaction propensity-based features, geometric properties, interface topology features, evolutionary conservation and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging of the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of nine traditional SFs included in this work in terms of success rate and hit rate evaluated over the top 10 predicted conformations; (ii) An ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by judiciously leveraging machine-learning.

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InterPep2: global peptide–protein docking using interaction surface templates

Bioinformatics ◽

10.1093/bioinformatics/btaa005 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2458-2465 ◽

Cited By ~ 2

Author(s):

Isak Johansson-Åkhe ◽

Claudio Mirabello ◽

Björn Wallner

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Structural Features ◽

Protein Docking ◽

Supplementary Information ◽

Peptide Ligand ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Improved Performance

Abstract Motivation Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.

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A knowledge–based scoring function to assess the stability of quaternary protein assemblies

10.1101/562520 ◽

2019 ◽

Cited By ~ 3

Author(s):

Abhilesh S. Dhawanjewar ◽

Ankit Roy ◽

M.S. Madhusudhan

Keyword(s):

Protein Interactions ◽

Binary Classification ◽

Scoring Function ◽

Protein Docking ◽

Scoring Functions ◽

Protein Protein Interactions ◽

Residue Contact ◽

Knowledge Based ◽

Cellular Biochemistry ◽

The Stability

AbstractMotivationElucidation of protein-protein interactions is a necessary step towards understanding the complete repertoire of cellular biochemistry. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study we have devised a new scheme for scoring protein-protein interactions.ResultsOur method, PIZSA (Protein Interaction Z Score Assessment) is a binary classification scheme for identification of stable protein quaternary assemblies (binders/non-binders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other recently published scoring functions, demonstrated through testing on a benchmark set and the CAPRI Score_set. Though not explicitly trained for this purpose, PIZSA potentials can identify spurious interactions that are artefacts of the crystallization process.AvailabilityPIZSA is implemented as awebserverat http://cospi.iiserpune.ac.in/pizsa/[email protected]

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iScore: a novel graph kernel-based function for scoring protein–protein docking models

Bioinformatics ◽

10.1093/bioinformatics/btz496 ◽

2019 ◽

Vol 36 (1) ◽

pp. 112-121 ◽

Cited By ~ 9

Author(s):

Cunliang Geng ◽

Yong Jung ◽

Nicolas Renaud ◽

Vasant Honavar ◽

Alexandre M J J Bonvin ◽

...

Keyword(s):

Protein Complexes ◽

Three Dimensional ◽

Protein Docking ◽

Graph Representation ◽

Supplementary Information ◽

Scoring Functions ◽

Computational Docking ◽

3D Structures ◽

Novel Approach ◽

Protein Interfaces

Abstract Motivation Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Results Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein–protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. Availability and implementation The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). Supplementary information Supplementary data are available at Bioinformatics online.

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

Bioinformatics ◽

10.1093/bioinformatics/btz623 ◽

2019 ◽

Vol 36 (3) ◽

pp. 751-757 ◽

Cited By ~ 1

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.

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