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Li M, Qing R, Tao F, Xu P, Zhang S. Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy. Comput Struct Biotechnol J 2024; 23:278-286. [PMID: 38173876 PMCID: PMC10762321 DOI: 10.1016/j.csbj.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.
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Affiliation(s)
- Mengke Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Rui Qing
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shuguang Zhang
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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2
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Min J, Rong X, Zhang J, Su R, Wang Y, Qi W. Computational Design of Peptide Assemblies. J Chem Theory Comput 2024; 20:532-550. [PMID: 38206800 DOI: 10.1021/acs.jctc.3c01054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.
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Affiliation(s)
- Jiwei Min
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
| | - Xi Rong
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
| | - Jiaxing Zhang
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
| | - Rongxin Su
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, P. R. China
- Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin 300072, P. R. China
| | - Yuefei Wang
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
- Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin 300072, P. R. China
| | - Wei Qi
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, P. R. China
- Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin 300072, P. R. China
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3
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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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4
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Kuder KJ. Docking Foundations: From Rigid to Flexible Docking. Methods Mol Biol 2024; 2780:3-14. [PMID: 38987460 DOI: 10.1007/978-1-0716-3985-6_1] [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
Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.
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Affiliation(s)
- Kamil J Kuder
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland.
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5
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Zięba A, Matosiuk D. Sampling and Scoring in Protein-Protein Docking. Methods Mol Biol 2024; 2780:15-26. [PMID: 38987461 DOI: 10.1007/978-1-0716-3985-6_2] [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 docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.
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Affiliation(s)
- Agata Zięba
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland.
| | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
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Kao HW, Lu WL, Ho MR, Lin YF, Hsieh YJ, Ko TP, Danny Hsu ST, Wu KP. Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN. ACS Synth Biol 2023; 12:2310-2319. [PMID: 37556858 DOI: 10.1021/acssynbio.3c00042] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
We used the deep learning tool ProteinMPNN to redesign ubiquitin (Ub) as a specific and functionally stimulating/enhancing binder of the Rsp5 E3 ligase. We generated 20 extensively mutated─up to 37 of 76 residues─recombinant Ub variants (UbVs), named R1 to R20, displaying well-folded structures and high thermal stabilities. These UbVs can also form stable complexes with Rsp5, as predicted using AlphaFold2. Three of the UbVs bound to Rsp5 with low micromolar affinity, with R4 and R12 effectively enhancing the Rsp5 activity six folds. AlphaFold2 predicts that R4 and R12 bind to Rsp5's exosite in an identical manner to the Rsp5-Ub template, thereby allosterically activating Rsp5-Ub thioester formation. Thus, we present a virtual solution for rapidly and cost-effectively designing UbVs as functional modulators of Ub-related enzymes.
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Affiliation(s)
- Hsi-Wen Kao
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
| | - Wei-Lin Lu
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
| | - Meng-Ru Ho
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
| | - Yu-Fong Lin
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
- Institute of Biochemical Science, National Taiwan University, Taipei 106, Taiwan
| | - Yun-Jung Hsieh
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
- Institute of Biochemical Science, National Taiwan University, Taipei 106, Taiwan
| | - Tzu-Ping Ko
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
| | - Shang-Te Danny Hsu
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
- Institute of Biochemical Science, National Taiwan University, Taipei 106, Taiwan
- International Institute for Sustainability with Knotted Chiral Meta Matter, Hiroshima University, Higashihiroshima 739-8527, Japan
| | - Kuen-Phon Wu
- Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan
- Institute of Biochemical Science, National Taiwan University, Taipei 106, Taiwan
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7
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Beglov D, Vajda S, Kozakov D. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 2023; 18:1814-1840. [PMID: 37188806 PMCID: PMC10898366 DOI: 10.1038/s41596-023-00826-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/19/2023] [Indexed: 05/17/2023]
Abstract
Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.
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Affiliation(s)
- Israel T Desta
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - Daron M Standley
- Department of Genome Informatics, Osaka University, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
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8
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Shor B, Schneidman-Duhovny D. Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541003. [PMID: 37293053 PMCID: PMC10245790 DOI: 10.1101/2023.05.16.541003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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9
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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: 15] [Impact Index Per Article: 15.0] [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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10
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Sheffler W, Yang EC, Dowling Q, Hsia Y, Fries CN, Stanislaw J, Langowski MD, Brandys M, Li Z, Skotheim R, Borst AJ, Khmelinskaia A, King NP, Baker D. Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock. PLoS Comput Biol 2023; 19:e1010680. [PMID: 37216343 PMCID: PMC10237659 DOI: 10.1371/journal.pcbi.1010680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 06/02/2023] [Accepted: 04/09/2023] [Indexed: 05/24/2023] Open
Abstract
Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations.
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Affiliation(s)
- William Sheffler
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Erin C. Yang
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Graduate Program in Biological Physics, Structure & Design, University of Washington, Seattle, Washington, United States of America
| | - Quinton Dowling
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Yang Hsia
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Chelsea N. Fries
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Jenna Stanislaw
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions (TRA Matter)”, University of Bonn, Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Mark D. Langowski
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, Washington, United States of America
| | - Marisa Brandys
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Zhe Li
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Rebecca Skotheim
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Andrew J. Borst
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Alena Khmelinskaia
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions (TRA Matter)”, University of Bonn, Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Neil P. King
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - David Baker
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington, United States of America
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11
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Durham J, Zhang J, Humphreys IR, Pei J, Cong Q. Recent advances in predicting and modeling protein-protein interactions. Trends Biochem Sci 2023; 48:527-538. [PMID: 37061423 DOI: 10.1016/j.tibs.2023.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/17/2023]
Abstract
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
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Affiliation(s)
- Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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12
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Rui H, Ashton KS, Min J, Wang C, Potts PR. Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction. RSC Chem Biol 2023; 4:192-215. [PMID: 36908699 PMCID: PMC9994104 DOI: 10.1039/d2cb00207h] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.
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Affiliation(s)
- Huan Rui
- Center for Research Acceleration by Digital Innovation, Amgen Research Thousand Oaks CA 91320 USA
| | - Kate S Ashton
- Medicinal Chemistry, Amgen Research Thousand Oaks CA 91320 USA
| | - Jaeki Min
- Induced Proximity Platform, Amgen Research Thousand Oaks CA 91320 USA
| | - Connie Wang
- Digital, Technology & Innovation, Amgen Thousand Oaks CA 91320 USA
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13
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Guo L, He J, Lin P, Huang SY, Wang J. TRScore: a three-dimensional RepVGG-based scoring method for ranking protein docking models. Bioinformatics 2022; 38:2444-2451. [PMID: 35199137 DOI: 10.1093/bioinformatics/btac120] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 01/19/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. RESULTS We present a new deep learning-based scoring method for ranking protein-protein docking models based on a three-dimensional (3D) RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set, and overall obtained a significant improvement over existing methods in cross validation and independent evaluations. AVAILABILITY Codes available at: https://github.com/BioinformaticsCSU/TRScore.
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Affiliation(s)
- Linyuan Guo
- School of Computer Science, Central South University, Changsha, Hunan 410083, China
| | - Jiahua He
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jianxin Wang
- School of Computer Science, Central South University, Changsha, Hunan 410083, China
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14
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Wang Y, Qin M, Wang X, Han J, Chen R, Zhang M, Gu W. Residual behaviors and metabolic pathway of ethylparaben in Drosophila melanogaster. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 230:113124. [PMID: 34968799 DOI: 10.1016/j.ecoenv.2021.113124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Parabens are commonly used as preservatives in foodstuffs, cosmetics, and pharmaceutical products. The widespread use of parabens has led to their leaking into the environment. Concerns about the safety of parabens have recently increased due to their potential endocrine-disrupting effects as an emerging contaminant. Thus, it is necessary to study the metabolism of parabens in vivo. METHODS In this study, Drosophila melanogaster in males and females were exposed to ethylparaben (EP) concentration group (300 mg/L, 700 mg/L, and 1000 mg/L), and control group (0 mg/L) by the capillary feeding assay (CAFE). We quantified the activity of the detoxification-related carboxylesterase (CarE). The contents of EP metabolites in D. melanogaster, including p-hydroxybenzoic acid (PHBA), methylparaben (MP), and intact EP were carried out by high-performance liquid chromatography (HPLC). The regression model between EP metabolites (PHBA and MP) and CarE was developed using the Fourier series fitting method. RESULTS The general level of EP metabolites (PHBA, MP, and intact EP) accumulation was accounted for 5.6-11.5% in D. melanogaster. As EP accumulated, the activity of CarE increased, and the activity of CarE in females was higher than males, which is inconsistent with the result of EP intake dose. Additionally, there were significant differences in the proportion of EP metabolites between female and male flies, and the results of sex comparison were different depending on the EP treated groups and EP metabolites. In general, PHBA of EP hydrolytic product and MP of EP transesterification product in D. melanogaster were 41.4-63.9% and 10.4-24.6%, respectively. In terms of the rest of the EP existed in intact form and ranged from 22.4% to 34.0%. Moreover, the EP metabolites in the conjugated form were higher than those in the free form. The regression model between EP metabolites and CarE was established, showing that the CarE activity can be used to estimate the content of PHBA and MP. CONCLUSION The result indicates that the EP can accumulate in the body through food. Hydrolysis is the main metabolic pathway of EP in D. melanogaster, and transesterification is another metabolic pathway of EP. Additionally, the EP metabolites in flies mainly exist in conjugated form. Furthermore, the Fourier series fitting method model between EP metabolites and CarE, providing theoretical support to study the dose-effect relationship between metabolites of parabens and CarE. This study not only provides a mathematical basis for the safety evaluation of parabens, but also provides support for the further study of the toxicological effects of parabens.
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Affiliation(s)
- Yuan Wang
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Mengbei Qin
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Xiao Wang
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Junling Han
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Ruidun Chen
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Min Zhang
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| | - Wei Gu
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China.
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15
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Yu T, Dou C, Lu Y, Duan L, Tan J, Li J, Kang F, Dong S, Bai Y, Xu J. Klotho upregulates the interaction between RANK and TRAF6 to facilitate RANKL-induced osteoclastogenesis via the NF-κB signaling pathway. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1499. [PMID: 34805361 PMCID: PMC8573428 DOI: 10.21037/atm-21-4332] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/16/2021] [Indexed: 11/06/2022]
Abstract
Background α-Klotho (Klotho) plays a wide range of roles in pathophysiological processes, such as low-turnover osteoporosis observed in klotho mutant mice (kl/kl mice). However, the precise function and underlying mechanism of klotho during osteoclastogenesis are not fully understood. Here, we investigated the effects of klotho on osteoclastogenesis induced by receptor activator of nuclear factor kappa-B ligand (RANKL). Methods The effects of klotho deficiency on osteoclastogenesis were explored using kl/kl mice both in vivo and in vitro. In in vitro experiments, lentivirus transfection, real-time quantitative PCR (RT-qPCR) analysis, western blot analysis, immunostaining, RNA-seq analysis, differential pathway analysis, Energy-based protein docking analysis and co-immunoprecipitation were used for deeply investigating the effects of klotho on RANKL-induced Osteoclastogenesis and the underlying mechanism. Results We found that klotho deficiency impaired osteoclastogenesis. Furthermore, in vitro studies revealed that klotho facilitated osteoclastogenesis and upregulated the expression of c-Fos and nuclear factor of activated T cells cytoplasmic 1 (NFATc1) during osteoclastogenesis. Mechanistically, we confirmed that klotho co-localized with nuclear factor kappa B (RANK) and facilitated the interaction between activated RANK and TNFR-associated factor 6 (TRAF6), thus klotho exerts its function in osteoclastogenesis through the activation of the NF-κB signaling pathway. Conclusions Klotho promotes RANKL-induced osteoclastogenesis through upregulating the interaction between RANK and TARF6, Targeting on klotho may be an attractive therapeutic method for osteopenic diseases.
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Affiliation(s)
- Tao Yu
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Ce Dou
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Yanzhu Lu
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Lianli Duan
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jiulin Tan
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jianmei Li
- Department of Biomedical Materials Science, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Fei Kang
- Department of Biomedical Materials Science, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Shiwu Dong
- Department of Biomedical Materials Science, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Yun Bai
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jianzhong Xu
- Department of Orthopedics, Southwest Hospital, Third Military Medical University, Chongqing, China
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16
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Abstract
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.
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17
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Johansson-Åkhe I, Mirabello C, Wallner B. InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network. FRONTIERS IN BIOINFORMATICS 2021; 1:763102. [PMID: 36303778 PMCID: PMC9581042 DOI: 10.3389/fbinf.2021.763102] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modeling such interactions is to exhaustively sample the conformational space by fast-Fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection fast enough for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical potentials. We present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine learning-based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD < 4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC between 0.65 and 0.79. When included as a selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of medium and high quality models produced by 80% and 40%, respectively. The InterPepRank program as well as all scripts for reproducing and retraining it are available from: http://wallnerlab.org/InterPepRank.
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18
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Agamennone M, Nicoli A, Bayer S, Weber V, Borro L, Gupta S, Fantacuzzi M, Di Pizio A. Protein-protein interactions at a glance: Protocols for the visualization of biomolecular interactions. Methods Cell Biol 2021; 166:271-307. [PMID: 34752337 DOI: 10.1016/bs.mcb.2021.06.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Protein-protein interactions (PPIs) play a key role in many biological processes and are intriguing targets for drug discovery campaigns. Advancements in experimental and computational techniques are leading to a growth of data accessibility, and, with it, an increased need for the analysis of PPIs. In this respect, visualization tools are essential instruments to represent and analyze biomolecular interactions. In this chapter, we reviewed some of the available tools, highlighting their features, and describing their functions with practical information on their usage.
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Affiliation(s)
| | - Alessandro Nicoli
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Sebastian Bayer
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Verena Weber
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Luca Borro
- Department of Imaging, Advanced Cardiovascular Imaging Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | | | - Antonella Di Pizio
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany.
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19
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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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20
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Harmalkar A, Gray JJ. Advances to tackle backbone flexibility in protein docking. Curr Opin Struct Biol 2020; 67:178-186. [PMID: 33360497 DOI: 10.1016/j.sbi.2020.11.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022]
Abstract
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Program in Molecular Biophysics, Institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
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21
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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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22
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 420] [Impact Index Per Article: 105.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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23
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Ding X, Wu Y, Wang Y, Vilseck JZ, Brooks CL. Accelerated CDOCKER with GPUs, Parallel Simulated Annealing, and Fast Fourier Transforms. J Chem Theory Comput 2020; 16:3910-3919. [PMID: 32374996 DOI: 10.1021/acs.jctc.0c00145] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fast Fourier transform (FFT)-based protein ligand docking together with parallel simulated annealing for both rigid and flexible receptor docking are implemented on graphical processing unit (GPU) accelerated platforms to significantly enhance the throughput of the CDOCKER and flexible CDOCKER - the docking algorithms in the CHARMM program for biomolecule modeling. The FFT-based approach for docking, first applied in protein-protein docking to efficiently search for the binding position and orientation of proteins, is adapted here to search ligand translational and rotational spaces given a ligand conformation in protein-ligand docking. Running on GPUs, our implementation of FFT docking in CDOCKER achieves a 15 000 fold speedup in the ligand translational and rotational space search in protein-ligand docking problems. With this significant speedup it becomes practical to exhaustively search ligand translational and rotational space when docking a rigid ligand into a protein receptor. We demonstrate in this paper that this provides an efficient way to calculate an upper bound for docking accuracy in the assessment of scoring functions for protein-ligand docking, which can be useful for improving scoring functions. The parallel molecular dynamics (MD) simulated annealing, also running on GPUs, aims to accelerate the search algorithm in CDOCKER by running MD simulated annealing in parallel on GPUs. When utilized as part of the general CDOCKER docking protocol, acceleration in excess of 20 times is achieved. With this acceleration, we demonstrate that the performance of CDOCKER for redocking is significantly improved compared with three other popular protein-ligand docking programs on two widely used protein ligand complex data sets: the Astex diverse set and the SB2012 test set. The flexible CDOCKER is similarly improved by the parallel MD simulated annealing on GPUs. Based on the results presented here, we suggest that the accelerated CDOCKER platform provides a highly competitive docking engine for both rigid-receptor and flexible-receptor docking studies and will further facilitate continued improvement in the physics-based scoring function employed in CDOCKER docking studies.
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24
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Terayama K, Shinobu A, Tsuda K, Takemura K, Kitao A. evERdock BAI: Machine-learning-guided selection of protein-protein complex structure. J Chem Phys 2019; 151:215104. [DOI: 10.1063/1.5129551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Kei Terayama
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology and Innovation Hub, Tsurumi-ku, Kanagawa 230-0045, Japan
- Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Ai Shinobu
- School of Life Sciences and Technology, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Koji Tsuda
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Ibaraki 305-0047, Japan
| | - Kazuhiro Takemura
- School of Life Sciences and Technology, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Akio Kitao
- School of Life Sciences and Technology, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
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25
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Takemura K, Kitao A. More efficient screening of protein-protein complex model structures for reducing the number of candidates. Biophys Physicobiol 2019; 16:295-303. [PMID: 31984184 PMCID: PMC6975980 DOI: 10.2142/biophysico.16.0_295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 08/01/2019] [Indexed: 01/29/2023] Open
Abstract
Rigid-body protein-protein docking is very efficient in generating tens of thousands of docked complex models (decoys) in a very short time without considering structure change upon binding, but typical docking scoring functions are not necessarily sufficiently accurate to narrow these decoys down to a small number of plausible candidates. Flexible refinements and sophisticated evaluation of the decoys are thus required to achieve more accurate prediction. Since this process is time-consuming, an efficient screening method to reduce the number of decoys is necessary immediately following rigid-body dockings. We attempted to develop an efficient screening method by clustering decoys generated by the rigid-body docking ZDOCK. We introduced the three metrics ligand-root-mean-square deviation (L-RMSD), interface-ligand-RMSD (iL-RMSD), and the fraction of common contacts (FCC), and examined various ranges of cut-offs for clusters to determine the best set of clustering parameters. Although the employed clustering algorithm is simple, it successfully reduced the number of decoys. Using iL-RMSD with a cut-off radius of 8 Å, the number of decoys that contain at least one near-native model with 90% probability decreased from 4,808 to 320, a 93% reduction in the original number of decoys. Using FCC for the clustering step, the top 1,000 success rates, defined as the probability that the top 1,000 models contain at least one near-native structure, reached 97%. We conclude that the proposed method is very efficient in selecting a small number of decoys that include near-native decoys.
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Affiliation(s)
- Kazuhiro Takemura
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
| | - Akio Kitao
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
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26
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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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27
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Porter KA, Padhorny D, Desta I, Ignatov M, Beglov D, Kotelnikov S, Sun Z, Alekseenko A, Anishchenko I, Cong Q, Ovchinnikov S, Baker D, Vajda S, Kozakov D. Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking. Proteins 2019; 87:1241-1248. [PMID: 31444975 DOI: 10.1002/prot.25808] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/21/2019] [Accepted: 07/30/2019] [Indexed: 12/29/2022]
Abstract
As a participant in the joint CASP13-CAPRI46 assessment, the ClusPro server debuted its new template-based modeling functionality. The addition of this feature, called ClusPro TBM, was motivated by the previous CASP-CAPRI assessments and by the proven ability of template-based methods to produce higher-quality models, provided templates are available. In prior assessments, ClusPro submissions consisted of models that were produced via free docking of pre-generated homology models. This method was successful in terms of the number of acceptable predictions across targets; however, analysis of results showed that purely template-based methods produced a substantially higher number of medium-quality models for targets for which there were good templates available. The addition of template-based modeling has expanded ClusPro's ability to produce higher accuracy predictions, primarily for homomeric but also for some heteromeric targets. Here we review the newest additions to the ClusPro web server and discuss examples of CASP-CAPRI targets that continue to drive further development. We also describe ongoing work not yet implemented in the server. This includes the development of methods to improve template-based models and the use of co-evolutionary information for data-assisted free docking.
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Affiliation(s)
- Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, Washington.,Institute for Protein Design, University of Washington, Seattle, Washington
| | - Qian Cong
- Department of Biochemistry, University of Washington, Seattle, Washington.,Institute for Protein Design, University of Washington, Seattle, Washington
| | - Sergey Ovchinnikov
- Center for Systems Biology, Harvard University, Cambridge, Massachusetts
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington.,Institute for Protein Design, University of Washington, Seattle, Washington.,Howard Hughes Medical Institute, University of Washington, Seattle, Washington
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts.,Department of Chemistry, Boston University, Boston, Massachusetts
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
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28
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Kong R, Wang F, Zhang J, Wang F, Chang S. CoDockPP: A Multistage Approach for Global and Site-Specific Protein–Protein Docking. J Chem Inf Model 2019; 59:3556-3564. [DOI: 10.1021/acs.jcim.9b00445] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Feng Wang
- School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Fengfei Wang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
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29
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Yu W, Jo S, Lakkaraju SK, Weber DJ, MacKerell AD. Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology. Proteins 2019; 87:289-301. [PMID: 30582220 PMCID: PMC6408985 DOI: 10.1002/prot.25650] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 01/05/2023]
Abstract
Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
| | | | | | - David J. Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
- SilcsBio LLC, Baltimore, MD 21202
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30
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Jiménez-García B, Roel-Touris J, Romero-Durana M, Vidal M, Jiménez-González D, Fernández-Recio J. LightDock: a new multi-scale approach to protein-protein docking. Bioinformatics 2018; 34:49-55. [PMID: 28968719 DOI: 10.1093/bioinformatics/btx555] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/01/2017] [Indexed: 12/18/2022] Open
Abstract
Motivation Computational prediction of protein-protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. Results We describe here a new multi-scale protein-protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases. Availability and implementation The source code of the software and installation instructions are available for download at https://life.bsc.es/pid/lightdock/. Contact juanf@bsc.es. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Brian Jiménez-García
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Jorge Roel-Touris
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Miguel Romero-Durana
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Miquel Vidal
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Daniel Jiménez-González
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Department of Computer Architecture, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Structural Biology Unit, IBMB-CSIC, 08028 Barcelona, Spain
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31
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Hogues H, Gaudreault F, Corbeil CR, Deprez C, Sulea T, Purisima EO. ProPOSE: Direct Exhaustive Protein-Protein Docking with Side Chain Flexibility. J Chem Theory Comput 2018; 14:4938-4947. [PMID: 30107730 DOI: 10.1021/acs.jctc.8b00225] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Despite decades of development, protein-protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedom and the prediction of the conformational changes of proteins upon binding. FFT is generally the preferred method to exhaustively explore the translation-rotation space at a fine grid resolution, albeit with the trade-off of approximating force fields with correlation functions. This work presents a direct search alternative that samples the states in Cartesian space at the same resolution and computational cost as standard FFT methods. Operating in real space allows the use of standard force field functional forms used in typical non-FFT methods as well as the implementation of strategies for focused exploration of conformational flexibility. Currently, a few misplaced side chains can cause docking programs to fail. This work specifically addresses the problem of side chain rearrangements upon complex formation. Based on the observation that most side chains retain their unbound conformation upon binding, each rigidly docked pose is initially scored ignoring up to a limited number of side chain overlaps which are resolved in subsequent repacking and minimization steps. On test systems where side chains are altered and backbones held in their bound state, this implementation provides significantly better native pose recovery and higher quality (lower RMSD) predictions when compared with five of the most popular docking programs. The method is implemented in the software program ProPOSE (Protein Pose Optimization by Systematic Enumeration).
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Affiliation(s)
- Hervé Hogues
- Human Health Therapeutics , National Research Council Canada , 6100 Royalmount Avenue , Montreal , Quebec H4P 2R2 , Canada
| | - Francis Gaudreault
- Human Health Therapeutics , National Research Council Canada , 6100 Royalmount Avenue , Montreal , Quebec H4P 2R2 , Canada
| | - Christopher R Corbeil
- Human Health Therapeutics , National Research Council Canada , 6100 Royalmount Avenue , Montreal , Quebec H4P 2R2 , Canada
| | - Christophe Deprez
- Human Health Therapeutics , National Research Council Canada , 6100 Royalmount Avenue , Montreal , Quebec H4P 2R2 , Canada
| | - Traian Sulea
- Human Health Therapeutics , National Research Council Canada , 6100 Royalmount Avenue , Montreal , Quebec H4P 2R2 , Canada
| | - Enrico O Purisima
- Human Health Therapeutics , National Research Council Canada , 6100 Royalmount Avenue , Montreal , Quebec H4P 2R2 , Canada
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32
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Abstract
Protein-RNA interactions play an important role in many biological processes. Computational methods such as docking have been developed to complement existing biophysical and structural biology techniques. Computational prediction of protein-RNA complex structures includes two steps: generating candidate structures from the individual protein and RNA parts and scoring the generated poses to pick out the correct one. In this work, we considered three recently developed data sets of protein-RNA complexes to evaluate and improve the performance of the FFT-based rigid-body docking algorithm implemented in the ICM package. An electrostatic term describing interactions between negatively charged phosphate groups and positively charged protein residues was added to the energy function used during the docking step to take into account the greater role that electrostatic interactions play in protein-RNA complexes. Next, the docking results were used to optimize a scoring function including van der Waals, electrostatic, and solvation terms. This optimization yielded a much smaller weight for the solvation term indicating that solvation energy may be less important for the scoring of protein-RNA structures. Rescoring of the generated poses with the new scoring function led to much higher success rates, while pose clustering by contact fingerprints produced further improvements, achieving a success rate of 0.66 for the top 100 structures.
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Affiliation(s)
- Yelena A Arnautova
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209 , San Diego , California 92121 , United States
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Maxim Totrov
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209 , San Diego , California 92121 , United States
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33
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Ignatov M, Kazennov A, Kozakov D. ClusPro FMFT-SAXS: Ultra-fast Filtering Using Small-Angle X-ray Scattering Data in Protein Docking. J Mol Biol 2018; 430:2249-2255. [DOI: 10.1016/j.jmb.2018.03.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 03/08/2018] [Accepted: 03/12/2018] [Indexed: 02/01/2023]
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Nakajima N, Hayashida M, Jansson J, Maruyama O, Akutsu T. Determining the minimum number of protein-protein interactions required to support known protein complexes. PLoS One 2018; 13:e0195545. [PMID: 29698482 PMCID: PMC5919440 DOI: 10.1371/journal.pone.0195545] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 03/23/2018] [Indexed: 11/18/2022] Open
Abstract
The prediction of protein complexes from protein-protein interactions (PPIs) is a well-studied problem in bioinformatics. However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of (additional) required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets (STRING, MINT, BioGRID, IntAct, DIP, BIND, WI-PHI, iRefIndex). The results show that the minimum number of additional required PPIs ranges from 51 (STRING) to 964 (BIND), and that even the four best PPI databases, STRING (51), BioGRID (67), WI-PHI (93) and iRefIndex (85), do not include enough PPIs to form all CYC2008 protein complexes. We also demonstrate that the proposed problem framework and our solutions can enhance the prediction accuracy of existing PPI prediction methods. ILPMinPPI can be freely downloaded from http://sunflower.kuicr.kyoto-u.ac.jp/~nakajima/.
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Affiliation(s)
- Natsu Nakajima
- Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
- * E-mail: (NN); (TA)
| | - Morihiro Hayashida
- Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, 14-4, Nishiikumacho, Matsue, Shimane 690-8518, Japan
| | - Jesper Jansson
- Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - Osamu Maruyama
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
- * E-mail: (NN); (TA)
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35
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Griffiths M, Niblett SP, Wales DJ. Optimal Alignment of Structures for Finite and Periodic Systems. J Chem Theory Comput 2017; 13:4914-4931. [PMID: 28841314 DOI: 10.1021/acs.jctc.7b00543] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Finding the optimal alignment between two structures is important for identifying the minimum root-mean-square distance (RMSD) between them and as a starting point for calculating pathways. Most current algorithms for aligning structures are stochastic, scale exponentially with the size of structure, and the performance can be unreliable. We present two complementary methods for aligning structures corresponding to isolated clusters of atoms and to condensed matter described by a periodic cubic supercell. The first method (Go-PERMDIST), a branch and bound algorithm, locates the global minimum RMSD deterministically in polynomial time. The run time increases for larger RMSDs. The second method (FASTOVERLAP) is a heuristic algorithm that aligns structures by finding the global maximum kernel correlation between them using fast Fourier transforms (FFTs) and fast SO(3) transforms (SOFTs). For periodic systems, FASTOVERLAP scales with the square of the number of identical atoms in the system, reliably finds the best alignment between structures that are not too distant, and shows significantly better performance than existing algorithms. The expected run time for Go-PERMDIST is longer than FASTOVERLAP for periodic systems. For finite clusters, the FASTOVERLAP algorithm is competitive with existing algorithms. The expected run time for Go-PERMDIST to find the global RMSD between two structures deterministically is generally longer than for existing stochastic algorithms. However, with an earlier exit condition, Go-PERMDIST exhibits similar or better performance.
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Affiliation(s)
- Matthew Griffiths
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Samuel P Niblett
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - David J Wales
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
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36
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Computational modeling of protein assemblies. Curr Opin Struct Biol 2017; 44:179-189. [DOI: 10.1016/j.sbi.2017.04.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
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37
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Antal Z, Szoverfi J, Fejer SN. Predicting the Initial Steps of Salt-Stable Cowpea Chlorotic Mottle Virus Capsid Assembly with Atomistic Force Fields. J Chem Inf Model 2017; 57:910-917. [DOI: 10.1021/acs.jcim.7b00078] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zoltan Antal
- Provitam Foundation, 16 Caisului
Street, Cluj-Napoca, Romania
| | - Janos Szoverfi
- Provitam Foundation, 16 Caisului
Street, Cluj-Napoca, Romania
- Faculty
of Applied Chemistry and Materials Science, University Politehnica of Bucharest, 1-7 Gh. Polizu Street, Bucharest, Romania
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38
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Zhang Z, Ehmann U, Zacharias M. Monte Carlo replica-exchange based ensemble docking of protein conformations. Proteins 2017; 85:924-937. [DOI: 10.1002/prot.25262] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 01/11/2017] [Accepted: 01/19/2017] [Indexed: 12/14/2022]
Affiliation(s)
- Zhe Zhang
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
- Department Chemie; Technische Universität München, Biomolecular NMR and Munich Center for Integrated Protein Science; Garching 85747 Germany
- College of Life and Health Sciences; Northeast University; Shenyang P.R. China
| | - Uwe Ehmann
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
| | - Martin Zacharias
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
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39
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Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S. The ClusPro web server for protein-protein docking. Nat Protoc 2017. [PMID: 28079879 DOI: 10.1038/nprot2016169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.
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Affiliation(s)
- Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, New York, USA
| | | | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
| | - Christine Yueh
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
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40
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Abstract
The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.
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41
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Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, Kozakov D. New additions to the ClusPro server motivated by CAPRI. Proteins 2017; 85:435-444. [PMID: 27936493 DOI: 10.1002/prot.25219] [Citation(s) in RCA: 362] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 11/28/2016] [Accepted: 11/29/2016] [Indexed: 12/12/2022]
Abstract
The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for small angle X-ray scattering data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally, we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. Proteins 2017; 85:435-444. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215.,Department of Chemistry, Boston University, Boston, Massachusetts, 02215
| | - Christine Yueh
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215
| | - Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215.,Program in Bioinformatics, Boston University, Boston, Massachusetts, 02215
| | - Scott E Mottarella
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215.,Program in Bioinformatics, Boston University, Boston, Massachusetts, 02215
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215
| | | | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, New York
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