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Tsuchiya Y, Yamamori Y, Tomii K. Protein-protein interaction prediction methods: from docking-based to AI-based approaches. Biophys Rev 2022; 14:1341-1348. [PMID: 36570321 PMCID: PMC9759050 DOI: 10.1007/s12551-022-01032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.
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Affiliation(s)
- Yuko Tsuchiya
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Yu Yamamori
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Kentaro Tomii
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
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2
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Yamamori Y, Tomii K. Application of Homology Modeling by Enhanced Profile-Profile Alignment and Flexible-Fitting Simulation to Cryo-EM Based Structure Determination. Int J Mol Sci 2022; 23:ijms23041977. [PMID: 35216093 PMCID: PMC8879198 DOI: 10.3390/ijms23041977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 12/03/2022] Open
Abstract
Application of cryo-electron microscopy (cryo-EM) is crucially important for ascertaining the atomic structure of large biomolecules such as ribosomes and protein complexes in membranes. Advances in cryo-EM technology and software have made it possible to obtain data with near-atomic resolution, but the method is still often capable of producing only a density map with up to medium resolution, either partially or entirely. Therefore, bridging the gap separating the density map and the atomic model is necessary. Herein, we propose a methodology for constructing atomic structure models based on cryo-EM maps with low-to-medium resolution. The method is a combination of sensitive and accurate homology modeling using our profile–profile alignment method with a flexible-fitting method using molecular dynamics simulation. As described herein, this study used benchmark applications to evaluate the model constructions of human two-pore channel 2 (one target protein in CASP13 with its structure determined using cryo-EM data) and the overall structure of Enterococcus hirae V-ATPase complex.
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Affiliation(s)
- Yu Yamamori
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan;
| | - Kentaro Tomii
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan;
- AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
- Correspondence:
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3
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Takeda H, Tsutsumi A, Nishizawa T, Lindau C, Busto JV, Wenz LS, Ellenrieder L, Imai K, Straub SP, Mossmann W, Qiu J, Yamamori Y, Tomii K, Suzuki J, Murata T, Ogasawara S, Nureki O, Becker T, Pfanner N, Wiedemann N, Kikkawa M, Endo T. Mitochondrial sorting and assembly machinery operates by β-barrel switching. Nature 2021; 590:163-169. [PMID: 33408415 DOI: 10.1038/s41586-020-03113-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 11/09/2020] [Indexed: 01/06/2023]
Abstract
The mitochondrial outer membrane contains so-called β-barrel proteins, which allow communication between the cytosol and the mitochondrial interior1-3. Insertion of β-barrel proteins into the outer membrane is mediated by the multisubunit mitochondrial sorting and assembly machinery (SAM, also known as TOB)4-6. Here we use cryo-electron microscopy to determine the structures of two different forms of the yeast SAM complex at a resolution of 2.8-3.2 Å. The dimeric complex contains two copies of the β-barrel channel protein Sam50-Sam50a and Sam50b-with partially open lateral gates. The peripheral membrane proteins Sam35 and Sam37 cap the Sam50 channels from the cytosolic side, and are crucial for the structural and functional integrity of the dimeric complex. In the second complex, Sam50b is replaced by the β-barrel protein Mdm10. In cooperation with Sam50a, Sam37 recruits and traps Mdm10 by penetrating the interior of its laterally closed β-barrel from the cytosolic side. The substrate-loaded SAM complex contains one each of Sam50, Sam35 and Sam37, but neither Mdm10 nor a second Sam50, suggesting that Mdm10 and Sam50b function as placeholders for a β-barrel substrate released from Sam50a. Our proposed mechanism for dynamic switching of β-barrel subunits and substrate explains how entire precursor proteins can fold in association with the mitochondrial machinery for β-barrel assembly.
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Affiliation(s)
- Hironori Takeda
- Faculty of Life Sciences, Kyoto Sangyo University, Kamigamo-motoyama, Kyoto, Japan
| | - Akihisa Tsutsumi
- Department of Cell Biology and Anatomy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Nishizawa
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Caroline Lindau
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jon V Busto
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lena-Sophie Wenz
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Sanofi Deutschland GmbH, Frankfurt am Main, Germany
| | - Lars Ellenrieder
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Novartis Pharma AG, Basel, Switzerland
| | - Kenichiro Imai
- Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.,Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
| | - Sebastian P Straub
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany.,Sanofi-Aventis (Suisse) ag, Vernier, Switzerland
| | - Waltraut Mossmann
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jian Qiu
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Spemann Graduate School of Biology and Medicine, University of Freiburg, Freiburg, Germany.,Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Yamamori
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
| | - Kentaro Tomii
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.,AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
| | - Junko Suzuki
- Faculty of Life Sciences, Kyoto Sangyo University, Kamigamo-motoyama, Kyoto, Japan
| | - Takeshi Murata
- Department of Chemistry, Graduate School of Science, Chiba University, Chiba, Japan
| | - Satoshi Ogasawara
- Department of Chemistry, Graduate School of Science, Chiba University, Chiba, Japan
| | - Osamu Nureki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Thomas Becker
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany.,BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany.,Institute for Biochemistry and Molecular Biology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Nikolaus Pfanner
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany.,BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Nils Wiedemann
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany.,BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Masahide Kikkawa
- Department of Cell Biology and Anatomy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshiya Endo
- Faculty of Life Sciences, Kyoto Sangyo University, Kamigamo-motoyama, Kyoto, Japan. .,Institute for Protein Dynamics, Kyoto Sangyo University, Kamigamo-motoyama, Kyoto, Japan.
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Emami N, Pakchin PS, Ferdousi R. Computational predictive approaches for interaction and structure of aptamers. J Theor Biol 2020; 497:110268. [PMID: 32311376 DOI: 10.1016/j.jtbi.2020.110268] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/27/2020] [Accepted: 04/02/2020] [Indexed: 02/07/2023]
Abstract
Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers.
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parvin Samadi Pakchin
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
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Dapkūnas J, Olechnovič K, Venclovas Č. Structural modeling of protein complexes: Current capabilities and challenges. Proteins 2019; 87:1222-1232. [PMID: 31294859 DOI: 10.1002/prot.25774] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/21/2019] [Accepted: 07/06/2019] [Indexed: 12/27/2022]
Abstract
Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.
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Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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Nakamura T, Oda T, Fukasawa Y, Tomii K. Template-based quaternary structure prediction of proteins using enhanced profile-profile alignments. Proteins 2017; 86 Suppl 1:274-282. [PMID: 29178285 PMCID: PMC5836938 DOI: 10.1002/prot.25432] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 11/11/2017] [Accepted: 11/22/2017] [Indexed: 12/26/2022]
Abstract
Proteins often exist as their multimeric forms when they function as so‐called biological assemblies consisting of the specific number and arrangement of protein subunits. Consequently, elucidating biological assemblies is necessary to improve understanding of protein function. Template‐Based Modeling (TBM), based on known protein structures, has been used widely for protein structure prediction. Actually, TBM has become an increasingly useful approach in recent years because of the increased amounts of information related to protein amino acid sequences and three‐dimensional structures. An apparently similar situation exists for biological assembly structure prediction as protein complex structures in the PDB increase, although the inference of biological assemblies is not a trivial task. Many methods using TBM, including ours, have been developed for protein structure prediction. Using enhanced profile–profile alignments, we participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12), as the FONT team (Group # 480). Herein, we present experimental procedures and results of retrospective analyses using our approach for the Quaternary Structure Prediction category of CASP12. We performed profile–profile alignments of several types, based on FORTE, our profile–profile alignment algorithm, to identify suitable templates. Results show that these alignment results enable us to find templates in almost all possible cases. Moreover, we have come to understand the necessity of developing a model selection method that provides improved accuracy. Results also demonstrate that, to some extent, finding templates of protein complexes is useful even for MEDIUM and HARD assembly prediction.
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Affiliation(s)
- Tsukasa Nakamura
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8562, Japan
| | - Toshiyuki Oda
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
| | - Yoshinori Fukasawa
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
| | - Kentaro Tomii
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8562, Japan.,Biotechnology Research Institute for Drug Discovery (BRD), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
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