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Zhang Y, Wang X, Zhang Z, Huang Y, Kihara D. Assessment of Protein-Protein Docking Models Using Deep Learning. Methods Mol Biol 2024; 2780:149-162. [PMID: 38987469 DOI: 10.1007/978-1-0716-3985-6_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.
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Affiliation(s)
- Yuanyuan Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Yunhan Huang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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2
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Schweke H, Xu Q, Tauriello G, Pantolini L, Schwede T, Cazals F, Lhéritier A, Fernandez-Recio J, Rodríguez-Lumbreras LÁ, Schueler-Furman O, Varga JK, Jiménez-García B, Réau MF, Bonvin A, Savojardo C, Martelli PL, Casadio R, Tubiana J, Wolfson H, Oliva R, Barradas-Bautista D, Ricciardelli T, Cavallo L, Venclovas Č, Olechnovič K, Guerois R, Andreani J, Martin J, Wang X, Kihara D, Marchand A, Correia B, Zou X, Dey S, Dunbrack R, Levy E, Wodak S. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study. Proteomics 2023; 23:e2200323. [PMID: 37365936 PMCID: PMC10937251 DOI: 10.1002/pmic.202200323] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
Abstract
Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Julia K. Varga
- Hebrew University of Jerusalem Institute for Medical Research Israel-Canada
| | | | | | | | | | | | | | - Jérôme Tubiana
- Tel Aviv University Blavatnik School of Computer Science
| | - Haim Wolfson
- Tel Aviv University Blavatnik School of Computer Science
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Institute for Data Science and Informatics, University of Missouri
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Wodak SJ, Vajda S, Lensink MF, Kozakov D, Bates PA. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annu Rev Biophys 2023; 52:183-206. [PMID: 36626764 PMCID: PMC10885158 DOI: 10.1146/annurev-biophys-102622-084607] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.
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Affiliation(s)
- Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vrije Universiteit Brussel, Brussels, Belgium;
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA;
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Marc F Lensink
- Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France;
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA;
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom;
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Tam C, Kumar A, Zhang KYJ. NbX: Machine Learning-Guided Re-Ranking of Nanobody-Antigen Binding Poses. Pharmaceuticals (Basel) 2021; 14:ph14100968. [PMID: 34681192 PMCID: PMC8537642 DOI: 10.3390/ph14100968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 12/02/2022] Open
Abstract
Modeling the binding pose of an antibody is a prerequisite to structure-based affinity maturation and design. Without knowing a reliable binding pose, the subsequent structural simulation is largely futile. In this study, we have developed a method of machine learning-guided re-ranking of antigen binding poses of nanobodies, the single-domain antibody which has drawn much interest recently in antibody drug development. We performed a large-scale self-docking experiment of nanobody–antigen complexes. By training a decision tree classifier through mapping a feature set consisting of energy, contact and interface property descriptors to a measure of their docking quality of the refined poses, significant improvement in the median ranking of native-like nanobody poses by was achieved eightfold compared with ClusPro and an established deep 3D CNN classifier of native protein–protein interaction. We further interpreted our model by identifying features that showed relatively important contributions to the prediction performance. This study demonstrated a useful method in improving our current ability in pose prediction of nanobodies.
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Affiliation(s)
- Chunlai Tam
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (C.T.); (A.K.)
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (C.T.); (A.K.)
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (C.T.); (A.K.)
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Correspondence:
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Wang X, Flannery ST, Kihara D. Protein Docking Model Evaluation by Graph Neural Networks. Front Mol Biosci 2021; 8:647915. [PMID: 34113650 PMCID: PMC8185212 DOI: 10.3389/fmolb.2021.647915] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 04/26/2021] [Indexed: 12/03/2022] Open
Abstract
Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions, it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning-based approach named Graph Neural Network-based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph, respectively. GNN-DOVE was trained, validated, and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods, including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models.
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Affiliation(s)
- Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Sean T. Flannery
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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Wang X, Terashi G, Christoffer CW, Zhu M, Kihara D. Protein docking model evaluation by 3D deep convolutional neural networks. Bioinformatics 2020; 36:2113-2118. [PMID: 31746961 DOI: 10.1093/bioinformatics/btz870] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/25/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023] Open
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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Affiliation(s)
- Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | | | - Mengmeng Zhu
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.,Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
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Abstract
Many of the biological functions of the cell are driven by protein-protein interactions. However, determining which proteins interact and exactly how they do so to enable their functions, remain major research questions. Functional interactions are dependent on a number of complicated factors; therefore, modeling the three-dimensional structure of protein-protein complexes is still considered a complex endeavor. Nevertheless, the rewards for modeling protein interactions to atomic level detail are substantial, and there are numerous examples of how models can provide useful information for drug design, protein engineering, systems biology, and understanding of the immune system. Here, we provide practical guidelines for docking proteins using the web-server, SwarmDock, a flexible protein-protein docking method. Moreover, we provide an overview of the factors that need to be considered when deciding whether docking is likely to be successful.
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Affiliation(s)
- Iain H Moal
- European Bioinformatics Institute, Hinxton, UK
| | | | | | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK.
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Christoffer C, Terashi G, Shin WH, Aderinwale T, Maddhuri Venkata Subramaniya SR, Peterson L, Verburgt J, Kihara D. Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46. Proteins 2019; 88:948-961. [PMID: 31697428 DOI: 10.1002/prot.25850] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/07/2019] [Accepted: 11/03/2019] [Indexed: 01/17/2023]
Abstract
We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.
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Affiliation(s)
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Department of Chemistry Education, Sunchon National University, Suncheon, Jeollanam-do, Republic of Korea
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Lenna Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana.,Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana.,Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
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Coevolutive, evolutive and stochastic information in protein-protein interactions. Comput Struct Biotechnol J 2019; 17:1429-1435. [PMID: 31871588 PMCID: PMC6906720 DOI: 10.1016/j.csbj.2019.10.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 10/19/2019] [Accepted: 10/22/2019] [Indexed: 11/24/2022] Open
Abstract
Here, we investigate the contributions of coevolutive, evolutive and stochastic information in determining protein-protein interactions (PPIs) based on primary sequences of two interacting protein families A and B. Specifically, under the assumption that coevolutive information is imprinted on the interacting amino acids of two proteins in contrast to other (evolutive and stochastic) sources spread over their sequences, we dissect those contributions in terms of compensatory mutations at physically-coupled and uncoupled amino acids of A and B. We find that physically-coupled amino-acids at short range distances store the largest per-contact mutual information content, with a significant fraction of that content resulting from coevolutive sources alone. The information stored in coupled amino acids is shown further to discriminate multi-sequence alignments (MSAs) with the largest expectation fraction of PPI matches – a conclusion that holds against various definitions of intermolecular contacts and binding modes. When compared to the informational content resulting from evolution at long-range interactions, the mutual information in physically-coupled amino-acids is the strongest signal to distinguish PPIs derived from cospeciation and likely, the unique indication in case of molecular coevolution in independent genomes as the evolutive information must vanish for uncorrelated proteins.
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