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Tsuchiya Y, Yonezawa T, Yamamori Y, Inoura H, Osawa M, Ikeda K, Tomii K. PoSSuM v.3: A Major Expansion of the PoSSuM Database for Finding Similar Binding Sites of Proteins. J Chem Inf Model 2023; 63:7578-7587. [PMID: 38016694 PMCID: PMC10716853 DOI: 10.1021/acs.jcim.3c01405] [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: 09/01/2023] [Revised: 10/28/2023] [Accepted: 11/01/2023] [Indexed: 11/30/2023]
Abstract
Information on structures of protein-ligand complexes, including comparisons of known and putative protein-ligand-binding pockets, is valuable for protein annotation and drug discovery and development. To facilitate biomedical and pharmaceutical research, we developed PoSSuM (https://possum.cbrc.pj.aist.go.jp/PoSSuM/), a database for identifying similar binding pockets in proteins. The current PoSSuM database includes 191 million similar pairs among almost 10 million identified pockets. PoSSuM drug search (PoSSuMds) is a resource for investigating ligand and receptor diversity among a set of pockets that can bind to an approved drug compound. The enhanced PoSSuMds covers pockets associated with both approved drugs and drug candidates in clinical trials from the latest release of ChEMBL. Additionally, we developed two new databases: PoSSuMAg for investigating antibody-antigen interactions and PoSSuMAF to simplify exploring putative pockets in AlphaFold human protein models.
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Affiliation(s)
- Yuko Tsuchiya
- Artificial
Intelligence Research Center, National Institute
of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Tomoki Yonezawa
- Division
of Physics for Life Functions, Keio University
Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, Japan
| | - Yu Yamamori
- Artificial
Intelligence Research Center, National Institute
of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Hiroko Inoura
- Artificial
Intelligence Research Center, National Institute
of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Masanori Osawa
- Division
of Physics for Life Functions, Keio University
Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, Japan
| | - Kazuyoshi Ikeda
- Division
of Physics for Life Functions, Keio University
Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, Japan
- Medicinal
Chemistry Applied AI Unit, HPC- and AI-driven Drug Development Platform
Division, RIKEN Center for Computational
Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kentaro Tomii
- Artificial
Intelligence Research Center, 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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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] [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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3
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Tanebe T, Ishida T. End-to-end learning for compound activity prediction based on binding pocket information. BMC Bioinformatics 2021; 22:529. [PMID: 34715777 PMCID: PMC8555120 DOI: 10.1186/s12859-021-04440-w] [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: 08/03/2021] [Accepted: 09/24/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, docking simulation is generally applied because it only requires a tertiary structure of the target protein. However, the conformation search and the evaluation of binding energy of docking simulation are computationally heavy and thus docking simulation needs huge computational resources. Thus, if we can apply a machine learning-based activity prediction method for a novel target protein, such methods would be highly useful. Recently, Tsubaki et al. proposed an end-to-end learning method to predict the activity of compounds for novel target proteins. However, the prediction accuracy of the method was still insufficient because it only used amino acid sequence information of a protein as the input. RESULTS In this research, we proposed an end-to-end learning-based compound activity prediction using structure information of a binding pocket of a target protein. The proposed method learns the important features by end-to-end learning using a graph neural network both for a compound structure and a protein binding pocket structure. As a result of the evaluation experiments, the proposed method has shown higher accuracy than an existing method using amino acid sequence information. CONCLUSIONS The proposed method achieved equivalent accuracy to docking simulation using AutoDock Vina with much shorter computing time. This indicated that a machine learning-based approach would be promising even for novel target proteins in activity prediction.
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Affiliation(s)
- Toshitaka Tanebe
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-85 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Takashi Ishida
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-85 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
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4
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Sato A, Tanimura N, Honma T, Konagaya A. Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4. Chem Pharm Bull (Tokyo) 2019; 67:1183-1190. [PMID: 31423003 DOI: 10.1248/cpb.c19-00443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from three-dimensional structures, there are few detailed reports on how best to construct and use datasets. Here, we examined how different datasets affected the prediction of the binding mode of CYP3A4 by a three-dimensional neural network when the number of crystal structures for the target protein was limited. We used four different training datasets: one large, general dataset containing various protein complexes and three smaller, more specific datasets containing complexes with CYP3A4-like pockets, complexes with CYP3A4-binding ligands, and complexes with CYP protein family members. We then trained models with different combinations of datasets with or without subsequent fine-tuning and evaluated the binding mode prediction performance of each model. The best receiver operating characteristic (ROC) area under the curve (AUC) model with respect to area under the receiver operating characteristic curve was obtained by training with a combination of the general protein and CYP family datasets. However, the ROC AUC-recall balanced model was obtained by training with this combination of datasets followed by fine-tuning with the CYP3A4-binding ligands dataset. Our results suggest that datasets that balance protein functionality and data size are important for optimizing binding mode prediction performance. In addition, datasets with large median binding pocket sizes may be important for the binding mode prediction specifically of CYP3A4.
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Affiliation(s)
- Atsuko Sato
- School of Computing, Department of Computer Science, Tokyo Institute of Technology
| | - Naoki Tanimura
- Science Solutions Division, Mizuho Information & Research Institute, Inc
| | - Teruki Honma
- School of Computing, Department of Computer Science, Tokyo Institute of Technology.,Center for Biosystems Dynamics Research, RIKEN.,Medical Sciences Innovation Hub Program, RIKEN
| | - Akihiko Konagaya
- School of Computing, Department of Computer Science, Tokyo Institute of Technology
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5
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End-to-End Learning Based Compound Activity Prediction Using Binding Pocket Information. INTELLIGENT COMPUTING THEORIES AND APPLICATION 2019. [DOI: 10.1007/978-3-030-26969-2_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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6
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Govindaraj RG, Brylinski M. Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 2018. [PMID: 29523085 PMCID: PMC5845264 DOI: 10.1186/s12859-018-2109-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges. Results We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock. Conclusions Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.
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Affiliation(s)
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. .,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA.
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7
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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8
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Lee J, Konc J, Janežič D, Brooks BR. Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins. Sci Rep 2017; 7:11652. [PMID: 28912495 PMCID: PMC5599562 DOI: 10.1038/s41598-017-10412-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 08/07/2017] [Indexed: 01/06/2023] Open
Abstract
The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. The analysis reveals that there are two central binding site communities that play the roles of the network hubs of smaller peripheral communities. The sizes of communities follow a power-law distribution, which indicates that the binding sites included in larger communities may be older and have been evolutionary structural scaffolds of more recent ones. Structurally similar binding sites in the same community bind to diverse ligands promiscuously and they are also embedded in diverse domain structures. Understanding the general principles of binding site interplay will pave the way for improved drug design and protein design.
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Affiliation(s)
- Juyong Lee
- Department of Chemistry, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon, 24341, Republic of Korea. .,Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892, United States.
| | - Janez Konc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000, Koper, Slovenia.,National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000, Koper, Slovenia
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892, United States
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9
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Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem 2016; 64:403-413. [PMID: 27620381 DOI: 10.1016/j.compbiolchem.2016.08.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 08/17/2016] [Accepted: 08/25/2016] [Indexed: 11/22/2022]
Abstract
Structural and computational biologists often need to measure the similarity of ligand binding conformations. The commonly used root-mean-square deviation (RMSD) is not only ligand-size dependent, but also may fail to capture biologically meaningful binding features. To address these issues, we developed the Contact Mode Score (CMS), a new metric to assess the conformational similarity based on intermolecular protein-ligand contacts. The CMS is less dependent on the ligand size and has the ability to include flexible receptors. In order to effectively compare binding poses of non-identical ligands bound to different proteins, we further developed the eXtended Contact Mode Score (XCMS). We believe that CMS and XCMS provide a meaningful assessment of the similarity of ligand binding conformations. CMS and XCMS are freely available at http://brylinski.cct.lsu.edu/content/contact-mode-score and http://geaux-computational-bio.github.io/contact-mode-score/.
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10
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Nakamura T, Tomii K. Effects of the difference in similarity measures on the comparison of ligand-binding pockets using a reduced vector representation of pockets. Biophys Physicobiol 2016; 13:139-147. [PMID: 27924268 PMCID: PMC5042158 DOI: 10.2142/biophysico.13.0_139] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 06/06/2016] [Indexed: 12/01/2022] Open
Abstract
Comprehensive analysis and comparison of protein ligand-binding pockets are important to predict the ligands which bind to parts of putative ligand binding pockets. Because of the recent increase of protein structure information, such analysis demands a fast and efficient method for comparing ligand binding pockets. Previously we proposed a fast alignment-free method based on a simple representation of a ligand binding pocket with one 11-dimensional vector, which is suitable for such analysis. Based on that method, we conducted this study to expand and revise similarity measures of binding pockets and to investigate the effects of those modifications with two datasets for improving the ability to detect similar binding pockets. The new method exhibits higher detection performance of similar binding pockets than the previous methods and another existing accurate alignment-dependent method: APoc. Results also show that the effects of the modifications depend on the difficulty of the dataset, implying some avenues for methods of improvement.
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Affiliation(s)
- Tsukasa Nakamura
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-8562, Japan; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan
| | - Kentaro Tomii
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba 277-8562, Japan; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan; Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan
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11
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Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics 2016; 32:579-86. [PMID: 26504143 PMCID: PMC5963357 DOI: 10.1093/bioinformatics/btv597] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 09/24/2015] [Accepted: 10/12/2015] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Many drugs interact with numerous proteins besides their intended therapeutic targets and a substantial portion of these interactions is yet to be elucidated. Protein-Drug Interaction Database (PDID) addresses incompleteness of these data by providing access to putative protein-drug interactions that cover the entire structural human proteome. RESULTS PDID covers 9652 structures from 3746 proteins and houses 16 800 putative interactions generated from close to 1.1 million accurate, all-atom structure-based predictions for several dozens of popular drugs. The predictions were generated with three modern methods: ILbind, SMAP and eFindSite. They are accompanied by propensity scores that quantify likelihood of interactions and coordinates of the putative location of the binding drugs in the corresponding protein structures. PDID complements the current databases that focus on the curated interactions and the BioDrugScreen database that relies on docking to find putative interactions. Moreover, we also include experimentally curated interactions which are linked to their sources: DrugBank, BindingDB and Protein Data Bank. Our database can be used to facilitate studies related to polypharmacology of drugs including repurposing and explaining side effects of drugs. AVAILABILITY AND IMPLEMENTATION PDID database is freely available at http://biomine.ece.ualberta.ca/PDID/.
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Affiliation(s)
- Chen Wang
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, People's Republic of China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, People's Republic of China
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, City University of New York (CUNY), New York, NY 10065, USA and
| | - Lukasz Kurgan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4, Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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12
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Nakamura T, Tomii K. Protein ligand-binding site comparison by a reduced vector representation derived from multidimensional scaling of generalized description of binding sites. Methods 2016; 93:35-40. [DOI: 10.1016/j.ymeth.2015.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 07/25/2015] [Accepted: 08/10/2015] [Indexed: 11/25/2022] Open
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13
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Ito JI, Ikeda K, Yamada K, Mizuguchi K, Tomii K. PoSSuM v.2.0: data update and a new function for investigating ligand analogs and target proteins of small-molecule drugs. Nucleic Acids Res 2014; 43:D392-8. [PMID: 25404129 PMCID: PMC4383952 DOI: 10.1093/nar/gku1144] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
PoSSuM (http://possum.cbrc.jp/PoSSuM/) is a database for detecting similar small-molecule binding sites on proteins. Since its initial release in 2011, PoSSuM has grown to provide information related to 49 million pairs of similar binding sites discovered among 5.5 million known and putative binding sites. This enlargement of the database is expected to enhance opportunities for biological and pharmaceutical applications, such as predictions of new functions and drug discovery. In this release, we have provided a new service named PoSSuM drug search (PoSSuMds) at http://possum.cbrc.jp/PoSSuM/drug_search/, in which we selected 194 approved drug compounds retrieved from ChEMBL, and detected their known binding pockets and pockets that are similar to them. Users can access and download all of the search results via a new web interface, which is useful for finding ligand analogs as well as potential target proteins. Furthermore, PoSSuMds enables users to explore the binding pocket universe within PoSSuM. Additionally, we have improved the web interface with new functions, including sortable tables and a viewer for visualizing and downloading superimposed pockets.
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Affiliation(s)
- Jun-ichi Ito
- Laboratory of Bioinformatics, National Institute of Biomedical Innovation (NIBIO), 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Kazuyoshi Ikeda
- Laboratory of Bioinformatics, National Institute of Biomedical Innovation (NIBIO), 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan Drug Discovery Informatics Group, System Solution Division, Level Five Co. Ltd., Shiodome Shibarikyu Bldg., 1-2-3 Kaigan, Minato-ku, Tokyo 105-0022, Japan
| | - Kazunori Yamada
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Kenji Mizuguchi
- Laboratory of Bioinformatics, National Institute of Biomedical Innovation (NIBIO), 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan
| | - Kentaro Tomii
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
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14
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Jalencas X, Mestres J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol Inform 2013; 32:976-90. [PMID: 27481143 DOI: 10.1002/minf.201300082] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/29/2013] [Indexed: 01/19/2023]
Abstract
The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.
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Affiliation(s)
- Xavier Jalencas
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550.
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15
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von Behren MM, Volkamer A, Henzler AM, Schomburg KT, Urbaczek S, Rarey M. Fast protein binding site comparison via an index-based screening technology. J Chem Inf Model 2013; 53:411-22. [PMID: 23390978 DOI: 10.1021/ci300469h] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies.
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Affiliation(s)
- Mathias M von Behren
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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16
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Kalliokoski T, Olsson TSG, Vulpetti A. Subpocket analysis method for fragment-based drug discovery. J Chem Inf Model 2013; 53:131-41. [PMID: 23327721 DOI: 10.1021/ci300523r] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Although two binding sites might be dissimilar overall, they might still bind the same fragments if they share suitable subpockets. Information about shared subpockets can be therefore used in fragment-based drug design to suggest new fragments or to replace existing fragments within an already known compound. A novel computational method called SubCav is described which allows the similarity searching and alignment of subpockets from a PDB-wide database against a user-defined query. The method is based on pharmacophoric fingerprints combined with a subpocket alignment algorithm. SubCav was shown to be effective in producing reasonable alignments for subpockets with low sequence similarity and be able to retrieve relevant subpockets from a large database of structures including those with different folds. It can also be used to analyze subpockets inside a protein family to facilitate drug design and to rationalize compound selectivity.
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Affiliation(s)
- Tuomo Kalliokoski
- Novartis Institutes for Biomedical Research, Postfach, CH-4002 Basel, Switzerland
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17
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Abstract
The identification and application of druggable pockets of targets play a key role in in silico drug design, which is a fundamental step in structure-based drug design. Herein, some recent progresses and developments of the computational analysis of pockets have been covered. Also, the pockets at the protein-protein interfaces (PPI) have been considered to further explore the pocket space for drug discovery. We have presented two case studies targeting the kinetic pockets generated by normal mode analysis and molecular dynamics method, respectively, in which we focus upon incorporating the pocket flexibility into the two-dimensional virtual screening with both affinity and specificity. We applied the specificity and affinity (SPA) score to quantitatively estimate affinity and evaluate specificity using the intrinsic specificity ratio (ISR) as a quantitative criterion. In one of two cases, we also included some applications of pockets located at the dimer interfaces to emphasize the role of PPI in drug discovery. This review will attempt to summarize the current status of this pocket issue and will present some prospective avenues of further inquiry.
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Affiliation(s)
- Xiliang Zheng
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 5625 Renmin Street, Changchun, Jilin, 130022, People's Republic of China
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Maojo V, Fritts M, de la Iglesia D, Cachau RE, Garcia-Remesal M, Mitchell JA, Kulikowski C. Nanoinformatics: a new area of research in nanomedicine. Int J Nanomedicine 2012; 7:3867-90. [PMID: 22866003 PMCID: PMC3410693 DOI: 10.2147/ijn.s24582] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Over a decade ago, nanotechnologists began research on applications of nanomaterials for medicine. This research has revealed a wide range of different challenges, as well as many opportunities. Some of these challenges are strongly related to informatics issues, dealing, for instance, with the management and integration of heterogeneous information, defining nomenclatures, taxonomies and classifications for various types of nanomaterials, and research on new modeling and simulation techniques for nanoparticles. Nanoinformatics has recently emerged in the USA and Europe to address these issues. In this paper, we present a review of nanoinformatics, describing its origins, the problems it addresses, areas of interest, and examples of current research initiatives and informatics resources. We suggest that nanoinformatics could accelerate research and development in nanomedicine, as has occurred in the past in other fields. For instance, biomedical informatics served as a fundamental catalyst for the Human Genome Project, and other genomic and -omics projects, as well as the translational efforts that link resulting molecular-level research to clinical problems and findings.
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Affiliation(s)
- Victor Maojo
- Biomedical Informatics Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Spain.
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19
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Kinjo AR, Nakamura H. GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution. Biophysics (Nagoya-shi) 2012; 8:79-94. [PMID: 27493524 PMCID: PMC4629647 DOI: 10.2142/biophysics.8.79] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2012] [Accepted: 04/03/2012] [Indexed: 12/01/2022] Open
Abstract
Comparison and classification of protein structures are fundamental means to understand protein functions. Due to the computational difficulty and the ever-increasing amount of structural data, however, it is in general not feasible to perform exhaustive all-against-all structure comparisons necessary for comprehensive classifications. To efficiently handle such situations, we have previously proposed a method, now called GIRAF. We herein describe further improvements in the GIRAF protein structure search and alignment method. The GIRAF method achieves extremely efficient search of similar structures of ligand binding sites of proteins by exploiting database indexing of structural features of local coordinate frames. In addition, it produces refined atom-wise alignments by iterative applications of the Hungarian method to the bipartite graph defined for a pair of superimposed structures. By combining the refined alignments based on different local coordinate frames, it is made possible to align structures involving domain movements. We provide detailed accounts for the database design, the search and alignment algorithms as well as some benchmark results.
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Affiliation(s)
- Akira R Kinjo
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
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Ito JI, Tabei Y, Shimizu K, Tsuda K, Tomii K. PoSSuM: a database of similar protein-ligand binding and putative pockets. Nucleic Acids Res 2011; 40:D541-8. [PMID: 22135290 PMCID: PMC3245044 DOI: 10.1093/nar/gkr1130] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Numerous potential ligand-binding sites are available today, along with hundreds of thousands of known binding sites observed in the PDB. Exhaustive similarity search for such vastly numerous binding site pairs is useful to predict protein functions and to enable rapid screening of target proteins for drug design. Existing databases of ligand-binding sites offer databases of limited scale. For example, SitesBase covers only ~33,000 known binding sites. Inferring protein function and drug discovery purposes, however, demands a much more comprehensive database including known and putative-binding sites. Using a novel algorithm, we conducted a large-scale all-pairs similarity search for 1.8 million known and potential binding sites in the PDB, and discovered over 14 million similar pairs of binding sites. Here, we present the results as a relational database Pocket Similarity Search using Multiple-sketches (PoSSuM) including all the discovered pairs with annotations of various types. PoSSuM enables rapid exploration of similar binding sites among structures with different global folds as well as similar ones. Moreover, PoSSuM is useful for predicting the binding ligand for unbound structures, which provides important clues for characterizing protein structures with unclear functions. The PoSSuM database is freely available at http://possum.cbrc.jp/PoSSuM/.
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Affiliation(s)
- Jun-Ichi Ito
- Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan
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