1
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Ishitani R, Takemoto M, Tomii K. Protein ligand binding site prediction using graph transformer neural network. PLoS One 2024; 19:e0308425. [PMID: 39106255 DOI: 10.1371/journal.pone.0308425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 07/23/2024] [Indexed: 08/09/2024] Open
Abstract
Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to be still insufficient. In this study, we introduce an approach that leverages a graph transformer neural network to rank the results of a geometry-based pocket detection method. We also created a larger training dataset compared to the conventionally used sc-PDB and investigated the correlation between the dataset size and prediction performance. Our findings indicate that utilizing a graph transformer-based method alongside a larger training dataset could enhance the performance of ligand binding site prediction.
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Affiliation(s)
- Ryuichiro Ishitani
- Division of Computational Drug Discovery and Design, Medical Research Institute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Preferred Networks, Inc., Chiyoda-ku, Tokyo, Japan
| | - Mizuki Takemoto
- Division of Computational Drug Discovery and Design, Medical Research Institute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Kentaro Tomii
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan
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2
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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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3
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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] [Grants] [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
- Artificial 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
- 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
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4
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Gozashti L, Roy SW, Thornlow B, Kramer A, Ares M, Corbett-Detig R. Transposable elements drive intron gain in diverse eukaryotes. Proc Natl Acad Sci U S A 2022; 119:e2209766119. [PMID: 36417430 PMCID: PMC9860276 DOI: 10.1073/pnas.2209766119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/19/2022] [Indexed: 11/24/2022] Open
Abstract
There is massive variation in intron numbers across eukaryotic genomes, yet the major drivers of intron content during evolution remain elusive. Rapid intron loss and gain in some lineages contrast with long-term evolutionary stasis in others. Episodic intron gain could be explained by recently discovered specialized transposons called Introners, but so far Introners are only known from a handful of species. Here, we performed a systematic search across 3,325 eukaryotic genomes and identified 27,563 Introner-derived introns in 175 genomes (5.2%). Species with Introners span remarkable phylogenetic diversity, from animals to basal protists, representing lineages whose last common ancestor dates to over 1.7 billion years ago. Aquatic organisms were 6.5 times more likely to contain Introners than terrestrial organisms. Introners exhibit mechanistic diversity but most are consistent with DNA transposition, indicating that Introners have evolved convergently hundreds of times from nonautonomous transposable elements. Transposable elements and aquatic taxa are associated with high rates of horizontal gene transfer, suggesting that this combination of factors may explain the punctuated and biased diversity of species containing Introners. More generally, our data suggest that Introners may explain the episodic nature of intron gain across the eukaryotic tree of life. These results illuminate the major source of ongoing intron creation in eukaryotic genomes.
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Affiliation(s)
- Landen Gozashti
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA95064
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA95064
| | - Scott W. Roy
- Department of Biology, San Francisco State University, San Francisco, CA94117
| | - Bryan Thornlow
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA95064
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA95064
| | - Alexander Kramer
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA95064
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA95064
| | - Manuel Ares
- Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA95064
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA95064
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA95064
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5
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Chen YA, Allendes Osorio RS, Mizuguchi K. TargetMine 2022: a new vision into drug target analysis. Bioinformatics 2022; 38:4454-4456. [PMID: 35894632 PMCID: PMC9477527 DOI: 10.1093/bioinformatics/btac507] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/08/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY We introduce the newest version of TargetMine, which includes the addition of new visualization options; integration of previously disaggregated functionality; and the migration of the front-end to the newly available Bluegenes service. AVAILABILITY AND IMPLEMENTATION TargeteMine is accessible online at https://targetmine.mizuguchilab.org/bluegenes. Users do not need to register to use the software. Source code for the different components listed in the article is available from TargetMine's organizational account at http://github.com/targetmine. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi-An Chen
- Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
| | - Rodolfo S Allendes Osorio
- Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
- Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
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6
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Computational Methods for Drug Repurposing. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:119-141. [PMID: 35230686 DOI: 10.1007/978-3-030-91836-1_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.
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7
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K, Gkonta M, Hotova J, Kasionis I, Hatzis P, Pavlopoulos GA. Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review. Biomolecules 2021; 11:1245. [PMID: 34439912 PMCID: PMC8391349 DOI: 10.3390/biom11081245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Kleanthi Voutsadaki
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Ioannis Kasionis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
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8
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Structural Modeling and Ligand-Binding Prediction for Analysis of Structure-Unknown and Function-Unknown Proteins Using FORTE Alignment and PoSSuM Pocket Search. Methods Mol Biol 2020. [PMID: 32621216 DOI: 10.1007/978-1-0716-0708-4_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Structural data of biomolecules, such as those of proteins and nucleic acids, provide much information for estimation of their functions. For structure-unknown proteins, structure information is obtainable by modeling their structures based on sequence similarity of proteins. Moreover, information related to ligands or ligand-binding sites is necessary to elucidate protein functions because the binding of ligands can engender not only the activation and inactivation of the proteins but also the modification of protein functions. This chapter presents methods using our profile-profile alignment server FORTE and the PoSSuM ligand-binding site database for prediction of the structure and potential ligand-binding sites of structure-unknown and function-unknown proteins, aimed at protein function prediction.
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9
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Gong J, Chen Y, Pu F, Sun P, He F, Zhang L, Li Y, Ma Z, Wang H. Understanding Membrane Protein Drug Targets in Computational Perspective. Curr Drug Targets 2020; 20:551-564. [PMID: 30516106 DOI: 10.2174/1389450120666181204164721] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 01/16/2023]
Abstract
Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.
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Affiliation(s)
- Jianting Gong
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Yongbing Chen
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
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10
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Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, Zhang Y, Li S, Yang F, Sun Q, Qin C, Zeng X, Chen Z, Chen YZ, Zhu F. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 2019; 46:D1121-D1127. [PMID: 29140520 PMCID: PMC5753365 DOI: 10.1093/nar/gkx1076] [Citation(s) in RCA: 376] [Impact Index Per Article: 75.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 11/10/2017] [Indexed: 12/25/2022] Open
Abstract
Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.
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Affiliation(s)
- Ying Hong Li
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Chun Yan Yu
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiao Xu Li
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Peng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiaoyu Zhang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Gao Tu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yang Zhang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Shuang Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Fengyuan Yang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qiu Sun
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Chu Qin
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Xian Zeng
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Zhe Chen
- Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, Hangzhou 310006, China
| | - Yu Zong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Feng Zhu
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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11
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Chakraborti S, Ramakrishnan G, Srinivasan N. Repurposing Drugs Based on Evolutionary Relationships Between Targets of Approved Drugs and Proteins of Interest. Methods Mol Biol 2019; 1903:45-59. [PMID: 30547435 DOI: 10.1007/978-1-4939-8955-3_3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Drug repurposing has garnered much interest as an effective method for drug development among biopharmaceutical companies. The availability of information on complete sequences of genomes and their associated biological data, genotype-phenotype-disease relationships, and properties of small molecules offers opportunities to explore the repurpose-able potential of existing pharmacopoeia. This method gains further importance, especially, in the context of development of drugs against infectious diseases, some of which pose serious complications due to emergence of drug-resistant pathogens. In this article, we describe computational means to achieve potential repurpose-able drug candidates that may be used against infectious diseases by exploring evolutionary relationships between established targets of FDA-approved drugs and proteins of pathogen of interest.
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Affiliation(s)
- Sohini Chakraborti
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Gayatri Ramakrishnan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.,Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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12
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Klein R, Linciano P, Celenza G, Bellio P, Papaioannou S, Blazquez J, Cendron L, Brenk R, Tondi D. In silico identification and experimental validation of hits active against KPC-2 β-lactamase. PLoS One 2018; 13:e0203241. [PMID: 30496182 PMCID: PMC6264499 DOI: 10.1371/journal.pone.0203241] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 11/06/2018] [Indexed: 01/25/2023] Open
Abstract
Bacterial resistance has become a worldwide concern, particularly after the emergence of resistant strains overproducing carbapenemases. Among these, the KPC-2 carbapenemase represents a significant clinical challenge, being characterized by a broad substrate spectrum that includes aminothiazoleoxime and cephalosporins such as cefotaxime. Moreover, strains harboring KPC-type β-lactamases are often reported as resistant to available β-lactamase inhibitors (clavulanic acid, tazobactam and sulbactam). Therefore, the identification of novel non β-lactam KPC-2 inhibitors is strongly necessary to maintain treatment options. This study explored novel, non-covalent inhibitors active against KPC-2, as putative hit candidates. We performed a structure-based in silico screening of commercially available compounds for non-β-lactam KPC-2 inhibitors. Thirty-two commercially available high-scoring, fragment-like hits were selected for in vitro validation and their activity and mechanism of action vs the target was experimentally evaluated using recombinant KPC-2. N-(3-(1H-tetrazol-5-yl)phenyl)-3-fluorobenzamide (11a), in light of its ligand efficiency (LE = 0.28 kcal/mol/non-hydrogen atom) and chemistry, was selected as hit to be directed to chemical optimization to improve potency vs the enzyme and explore structural requirement for inhibition in KPC-2 binding site. Further, the compounds were evaluated against clinical strains overexpressing KPC-2 and the most promising compound reduced the MIC of the β-lactam antibiotic meropenem by four-fold.
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Affiliation(s)
- Raphael Klein
- Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany
| | - Pasquale Linciano
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
| | - Giuseppe Celenza
- Dipartimento di Scienze Cliniche Applicate e Biotecnologie, Università dell’Aquila,L’Aquila, Italy
| | - Pierangelo Bellio
- Dipartimento di Scienze Cliniche Applicate e Biotecnologie, Università dell’Aquila,L’Aquila, Italy
| | - Sofia Papaioannou
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
| | - Jesus Blazquez
- Department of Microbial Biotechnology, National Center for Biotechnology, Consejo Superior de Investigaciones Científicas (CSIC), Campus de la Universidad Autonoma-Cantoblanco, Madrid, Spain
| | - Laura Cendron
- Dipartimento di Biologia, Università di Padova, Padova, Italy
| | - Ruth Brenk
- Department of Biomedicine, University of Bergen, Bergen, Norway
- * E-mail: (DT); (RB)
| | - Donatella Tondi
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
- * E-mail: (DT); (RB)
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13
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Kandagalla S, Sharath BS, Bharath BR, Hani U, Manjunatha H. Molecular docking analysis of curcumin analogues against kinase domain of ALK5. In Silico Pharmacol 2017; 5:15. [PMID: 29308351 DOI: 10.1007/s40203-017-0034-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Accepted: 11/02/2017] [Indexed: 12/22/2022] Open
Abstract
During metastasis, cancer cells transcend from primary site to normal cells area upon attaining epithelial to mesenchymal transition (EMT) causing malignant cancer disease. Increased expression of TGF-β and its receptor ALK5 is an important hallmark of malignant cancer. In the present study, efficacy of curcumin and its analogues as inhibitors of ALK5 (TGFβR-I) receptor was evaluated using in silico approaches. A total of 142 curcumin analogues and curcumin were retrieved from peer reviewed literature and constructed a combinatorial library. Further their drug-likeness was assessed using Molinspiration, cheminformatics and preADMET online servers. The interaction of 142 curcumin analogues and curcumin with ALK5 receptor was studied using Autodock Vina. This study revealed six curcumin analogues as promising ALK5 inhibitors with significant binding energy and H-bonding interaction.
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Affiliation(s)
- Shivananda Kandagalla
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Shivamogga, Karnataka 577451 India
| | - B S Sharath
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Shivamogga, Karnataka 577451 India
| | | | - Umme Hani
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Shivamogga, Karnataka 577451 India
| | - Hanumanthappa Manjunatha
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Shivamogga, Karnataka 577451 India
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14
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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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15
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Rota SG, Roma A, Dude I, Ma C, Stevens R, MacEachern J, Graczyk J, Espiritu SMG, Rao PN, Minden MD, Kreinin E, Hess DA, Doxey AC, Spagnuolo PA. Estrogen Receptor β Is a Novel Target in Acute Myeloid Leukemia. Mol Cancer Ther 2017; 16:2618-2626. [DOI: 10.1158/1535-7163.mct-17-0292] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 07/11/2017] [Accepted: 08/16/2017] [Indexed: 11/16/2022]
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16
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Ledón-Rettig CC, Zattara EE, Moczek AP. Asymmetric interactions between doublesex and tissue- and sex-specific target genes mediate sexual dimorphism in beetles. Nat Commun 2017; 8:14593. [PMID: 28239147 PMCID: PMC5333360 DOI: 10.1038/ncomms14593] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 01/11/2017] [Indexed: 12/29/2022] Open
Abstract
Sexual dimorphisms fuel significant intraspecific variation and evolutionary diversification. Yet the developmental-genetic mechanisms underlying sex-specific development remain poorly understood. Here, we focus on the conserved sex-determination gene doublesex (dsx) and the mechanisms by which it mediates sex-specific development in a horned beetle species by combining systemic dsx knockdown, high-throughput sequencing of diverse tissues and a genome-wide analysis of Dsx-binding sites. We find that Dsx regulates sex-biased expression predominantly in males, that Dsx's target repertoires are highly sex- and tissue-specific and that Dsx can exercise its regulatory role via two distinct mechanisms: as a sex-specific modulator by regulating strictly sex-specific targets, or as a switch by regulating the same genes in males and females in opposite directions. More generally, our results suggest Dsx can rapidly acquire new target gene repertoires to accommodate evolutionarily novel traits, evidenced by the large and unique repertoire identified in head horns, a recent morphological innovation.
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Affiliation(s)
- C. C. Ledón-Rettig
- Department of Biology, Indiana University, 915 E. Third Street, Myers Hall 150, Bloomington, Indiana 47405-7107, USA
| | - E. E. Zattara
- Department of Biology, Indiana University, 915 E. Third Street, Myers Hall 150, Bloomington, Indiana 47405-7107, USA
| | - A. P. Moczek
- Department of Biology, Indiana University, 915 E. Third Street, Myers Hall 150, Bloomington, Indiana 47405-7107, USA
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17
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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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18
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Tanramluk D, Narupiyakul L, Akavipat R, Gong S, Charoensawan V. MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein-ligand fragment interaction, pathways and SNPs. Nucleic Acids Res 2016; 44:W514-21. [PMID: 27131358 PMCID: PMC4987895 DOI: 10.1093/nar/gkw314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 04/07/2016] [Accepted: 04/13/2016] [Indexed: 11/15/2022] Open
Abstract
Protein-ligand interaction analysis is an important step of drug design and protein engineering in order to predict the binding affinity and selectivity between ligands to the target proteins. To date, there are more than 100 000 structures available in the Protein Data Bank (PDB), of which ∼30% are protein-ligand (MW below 1000 Da) complexes. We have developed the integrative web server MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) with the aim of providing a user-friendly web interface to assist structural study and design of protein-ligand interactions. In brief, the server allows the users to input the chemical fragments and present all the unique molecular interactions to the target proteins with available three-dimensional structures in the PDB. The users can also link the ligands of interest to assess possible off-target proteins, human variants and pathway information using our all-in-one integrated tools. Taken together, we envisage that the server will facilitate and improve the study of protein-ligand interactions by allowing observation and comparison of ligand interactions with multiple proteins at the same time. (http://manoraa.org).
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Affiliation(s)
- Duangrudee Tanramluk
- Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
| | - Lalita Narupiyakul
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Computer Engineering, Faculty of Engineering, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
| | - Ruj Akavipat
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Computer Science, Faculty of Science, Kasetsart University, Chatuchak, Bangkok 10900, Thailand
| | - Sungsam Gong
- Department of Obstetrics and Gynaecology, University of Cambridge, The Rosie Hospital, Cambridge CB2 0SW, UK
| | - Varodom Charoensawan
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Biochemistry, Faculty of Science, Mahidol University, Ratchathewi, Bangkok 10400, Thailand
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19
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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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20
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Shin WH, Bures MG, Kihara D. PatchSurfers: Two methods for local molecular property-based binding ligand prediction. Methods 2016; 93:41-50. [PMID: 26427548 PMCID: PMC4718779 DOI: 10.1016/j.ymeth.2015.09.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Revised: 09/27/2015] [Accepted: 09/28/2015] [Indexed: 01/09/2023] Open
Abstract
Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Mark Gregory Bures
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
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21
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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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