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Adeyelu T, Bordin N, Waman VP, Sadlej M, Sillitoe I, Moya-Garcia AA, Orengo CA. KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units. Biomolecules 2023; 13:277. [PMID: 36830646 PMCID: PMC9953599 DOI: 10.3390/biom13020277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/05/2023] Open
Abstract
Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.
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Affiliation(s)
- Tolulope Adeyelu
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
- Department of Comparative Biomedical Science, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Vaishali P. Waman
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Marta Sadlej
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Aurelio A. Moya-Garcia
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain
- Laboratorio de Biología Molecular del Cáncer, Centro de Investigaciones Médico-Sanitarias (CIMES), Universidad de Málaga, 29071 Málaga, Spain
| | - Christine A. Orengo
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
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2
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Fan YW, Liu WH, Chen YT, Hsu YC, Pathak N, Huang YW, Yang JM. Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations. BMC Bioinformatics 2022; 23:242. [PMID: 35725381 PMCID: PMC9208089 DOI: 10.1186/s12859-022-04760-5] [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: 05/25/2022] [Accepted: 05/31/2022] [Indexed: 12/02/2022] Open
Abstract
Background While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. Results In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. Conclusion With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.
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Affiliation(s)
- You-Wei Fan
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 30050, Taiwan
| | - Wan-Hsin Liu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, 11564, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 11564, Taiwan
| | - Yun-Ti Chen
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan
| | - Yen-Chao Hsu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan
| | - Nikhil Pathak
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 30044, Taiwan
| | - Yu-Wei Huang
- Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, 30050, Taiwan
| | - Jinn-Moon Yang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan. .,Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 30050, Taiwan.
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Chen Y, Wang ZZ, Hao GF, Song BA. Web support for the more efficient discovery of kinase inhibitors. Drug Discov Today 2022; 27:2216-2225. [DOI: 10.1016/j.drudis.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/16/2022] [Accepted: 04/01/2022] [Indexed: 11/24/2022]
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Ren Z, Li Q, Shen Y, Meng L. Intrinsic relative preference profile of pan-kinase inhibitor drug staurosporine towards the clinically occurring gatekeeper mutations in Protein Tyrosine Kinases. Comput Biol Chem 2021; 94:107562. [PMID: 34428735 DOI: 10.1016/j.compbiolchem.2021.107562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/09/2021] [Accepted: 08/10/2021] [Indexed: 01/22/2023]
Abstract
Protein tyrosine kinases (PTKs) have been recognized as the attractive druggable targets of various diseases including cancer. However, many PTKs are clinically observed to establish a gatekeeper mutation in the peripheral hinge section of active site, which plays a primary role in development of acquired drug resistance to kinase inhibitors. The natural product Staurosporine, an ATP-competitive reversible pan-kinase inhibitor, has been found to exhibit wild type-sparing selectivity for some PTK gatekeeper mutants. In this study, totally 23 acquired drug-resistant gatekeeper mutations harbored on 17 PTKs involved in diverse cancers were curated, from which only five amino acid types, namely Thr, Met, Val, Leu and Ile, were observed at both wild-type and mutant residues of these clinically occurring gatekeeper sites. Here, an integrative strategy that combined molecular modeling and kinase assay was described to systematically investigate the relative preference of Staurosporine towards the five gatekeeper amino acid types in real kinase context and in a psendokinase model. A kinase-free, intrinsic relative preference profile of Staurosporine to gatekeeper amino acids was created: (dispreferred) Thr⊳Val⊳Ile⊳Leu⊳Met (preferred). It is found that kinase context has no essential effect on the profile; different kinases and even psendokinase can obtain a consistent conclusion for the preference order. Theoretically, we can use the profile to predict Staurosporine response to any gatekeeper mutation between the five amino acid types in any PTK. Structural and energetic analyses revealed that the multiple-aromatic ring system of Staurosporine can form multiple noncovalent interactions with the weakly polar side chain of Met and can pack tightly or moderately against the nonpolar side chains of Val, Ile and Leu, thus stabilizing the kinase-inhibitor system (ΔU < 0), whereas the polar side chain of Thr may cause unfavorable electronegative and solvent effects with the aromatic electrons of Staurosporine, thus destabilizing the system (ΔU > 0).
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Affiliation(s)
- Zheng Ren
- Department of Pharmacy, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qian Li
- Department of Pharmacy, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yiwen Shen
- Department of Pharmacy, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Ling Meng
- Department of Pharmacy, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
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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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6
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Hu R, Xu H, Jia P, Zhao Z. KinaseMD: kinase mutations and drug response database. Nucleic Acids Res 2021; 49:D552-D561. [PMID: 33137204 PMCID: PMC7779064 DOI: 10.1093/nar/gkaa945] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 12/11/2022] Open
Abstract
Mutations in kinases are abundant and critical to study signaling pathways and regulatory roles in human disease, especially in cancer. Somatic mutations in kinase genes can affect drug treatment, both sensitivity and resistance, to clinically used kinase inhibitors. Here, we present a newly constructed database, KinaseMD (kinase mutations and drug response), to structurally and functionally annotate kinase mutations. KinaseMD integrates 679 374 somatic mutations, 251 522 network-rewiring events, and 390 460 drug response records curated from various sources for 547 kinases. We uniquely annotate the mutations and kinase inhibitor response in four types of protein substructures (gatekeeper, A-loop, G-loop and αC-helix) that are linked to kinase inhibitor resistance in literature. In addition, we annotate functional mutations that may rewire kinase regulatory network and report four phosphorylation signals (gain, loss, up-regulation and down-regulation). Overall, KinaseMD provides the most updated information on mutations, unique annotations of drug response especially drug resistance and functional sites of kinases. KinaseMD is accessible at https://bioinfo.uth.edu/kmd/, having functions for searching, browsing and downloading data. To our knowledge, there has been no systematic annotation of these structural mutations linking to kinase inhibitor response. In summary, KinaseMD is a centralized database for kinase mutations and drug response.
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Affiliation(s)
- Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston TX 77030, USA
| | - Haodong Xu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston TX 77030, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston TX 77030, USA
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7
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Savage SR, Zhang B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteomics 2020; 17:27. [PMID: 32676006 PMCID: PMC7353784 DOI: 10.1186/s12014-020-09290-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 07/04/2020] [Indexed: 12/19/2022] Open
Abstract
Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.
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Affiliation(s)
- Sara R. Savage
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
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8
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Abstract
Protein kinases play important roles in signaling pathways and are widely studied as drug targets. Their active site exhibits remarkable structural variation as observed in the large number of available crystal structures. We have developed a clustering scheme and nomenclature to categorize and label all the observed conformations in human protein kinases. This has enabled us to clearly define the geometry of the active state and to distinguish closely related inactive states which were previously not characterized. Our classification of kinase conformations will help in better understanding the conformational dynamics of these proteins and the development of inhibitors against them. Targeting protein kinases is an important strategy for intervention in cancer. Inhibitors are directed at the active conformation or a variety of inactive conformations. While attempts have been made to classify these conformations, a structurally rigorous catalog of states has not been achieved. The kinase activation loop is crucial for catalysis and begins with the conserved DFGmotif. This motif is observed in two major classes of conformations, DFGin—a set of active and inactive conformations where the Phe residue is in contact with the C-helix of the N-terminal lobe—and DFGout—an inactive form where Phe occupies the ATP site exposing the C-helix pocket. We have developed a clustering of kinase conformations based on the location of the Phe side chain (DFGin, DFGout, and DFGinter or intermediate) and the backbone dihedral angles of the sequence X-D-F, where X is the residue before the DFGmotif, and the DFG-Phe side-chain rotamer, utilizing a density-based clustering algorithm. We have identified eight distinct conformations and labeled them based on the Ramachandran regions (A, alpha; B, beta; L, left) of the XDF motif and the Phe rotamer (minus, plus, trans). Our clustering divides the DFGin group into six clusters including BLAminus, which contains active structures, and two common inactive forms, BLBplus and ABAminus. DFGout structures are predominantly in the BBAminus conformation, which is essentially required for binding type II inhibitors. The inactive conformations have specific features that make them unable to bind ATP, magnesium, and/or substrates. Our structurally intuitive nomenclature will aid in understanding the conformational dynamics of kinases and structure-based development of kinase drugs.
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9
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Xu H, Wang Y, Lin S, Deng W, Peng D, Cui Q, Xue Y. PTMD: A Database of Human Disease-associated Post-translational Modifications. GENOMICS PROTEOMICS & BIOINFORMATICS 2018; 16:244-251. [PMID: 30244175 PMCID: PMC6205080 DOI: 10.1016/j.gpb.2018.06.004] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 06/04/2018] [Accepted: 06/25/2018] [Indexed: 12/20/2022]
Abstract
Various posttranslational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently implicated in human diseases. Therefore, an integral resource of PTM–disease associations (PDAs) would be a great help for both academic research and clinical use. In this work, we reported PTMD, a well-curated database containing PTMs that are associated with human diseases. We manually collected 1950 known PDAs in 749 proteins for 23 types of PTMs and 275 types of diseases from the literature. Database analyses show that phosphorylation has the largest number of disease associations, whereas neurologic diseases have the largest number of PTM associations. We classified all known PDAs into six classes according to the PTM status in diseases and demonstrated that the upregulation and presence of PTM events account for a predominant proportion of disease-associated PTM events. By reconstructing a disease–gene network, we observed that breast cancers have the largest number of associated PTMs and AKT1 has the largest number of PTMs connected to diseases. Finally, the PTMD database was developed with detailed annotations and can be a useful resource for further analyzing the relations between PTMs and human diseases. PTMD is freely accessible at http://ptmd.biocuckoo.org.
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Affiliation(s)
- Haodong Xu
- Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yongbo Wang
- Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shaofeng Lin
- Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wankun Deng
- Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Di Peng
- Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qinghua Cui
- Department of Biomedical Informatics, School of Basic Medical Sciences, MOE Key Laboratory of Molecular Cardiovascular Sciences, Center for Non-coding RNA Medicine, Peking University, Beijing 100191, China.
| | - Yu Xue
- Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
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González-Medina M, Naveja JJ, Sánchez-Cruz N, Medina-Franco JL. Open chemoinformatic resources to explore the structure, properties and chemical space of molecules. RSC Adv 2017. [DOI: 10.1039/c7ra11831g] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Open chemoinformatic servers facilitate analysis of chemical space and structure–activity relationships.
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Affiliation(s)
- Mariana González-Medina
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - J. Jesús Naveja
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - Norberto Sánchez-Cruz
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - José L. Medina-Franco
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
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11
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Shoombuatong W, Prathipati P, Owasirikul W, Worachartcheewan A, Simeon S, Anuwongcharoen N, Wikberg JES, Nantasenamat C. Towards the Revival of Interpretable QSAR Models. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2017. [DOI: 10.1007/978-3-319-56850-8_1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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12
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Prathipati P, Mizuguchi K. Integration of Ligand and Structure Based Approaches for CSAR-2014. J Chem Inf Model 2015; 56:974-87. [PMID: 26492437 DOI: 10.1021/acs.jcim.5b00477] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The prediction of binding poses and affinities is an area of active interest in computer-aided drug design (CADD). Given the documented limitations with either ligand or structure based approaches, we employed an integrated approach and developed a rapid protocol for binding mode and affinity predictions. This workflow was applied to the three protein targets of Community Structure-Activity Resource-2014 (CSAR-2014) exercise: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA (guanine-N(1))-methyltransferase (TrmD). Our docking and scoring workflow incorporates compound clustering and ligand and protein structure based pharmacophore modeling, followed by local docking, minimization, and scoring. While the former part of the protocol ensures high-quality ligand alignments and mapping, the subsequent minimization and scoring provides the predicted binding modes and affinities. We made blind predictions of docking pose for 1, 5, and 14 ligands docked into 1, 2, and 12 crystal structures of FXa, SYK, and TrmD, respectively. The resulting 174 poses were compared with cocrystallized structures (1, 5, and 14 complexes) made available at the end of CSAR. Our predicted poses were related to the experimentally determined structures with a mean root-mean-square deviation value of 3.4 Å. Further, we were able to classify high and low affinity ligands with the area under the curve values of 0.47, 0.60, and 0.69 for FXa, SYK, and TrmD, respectively, indicating the validity of our approach in at least two of the three systems. Detailed critical analysis of the results and CSAR methodology ranking procedures suggested that a straightforward application of our workflow has limitations, as some of the performance measures do not reflect the actual utility of pose and affinity predictions in the biological context of individual systems.
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Affiliation(s)
- Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition , 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes of Biomedical Innovation, Health and Nutrition , 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan
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13
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Kooistra AJ, Kanev GK, van Linden OPJ, Leurs R, de Esch IJP, de Graaf C. KLIFS: a structural kinase-ligand interaction database. Nucleic Acids Res 2015; 44:D365-71. [PMID: 26496949 PMCID: PMC4702798 DOI: 10.1093/nar/gkv1082] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 10/07/2015] [Indexed: 01/18/2023] Open
Abstract
Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all (>2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended (>65% more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) αC-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vu-compmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.
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Affiliation(s)
- Albert J Kooistra
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Georgi K Kanev
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Oscar P J van Linden
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Chris de Graaf
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
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14
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Wang Q, Zorn JA, Kuriyan J. A structural atlas of kinases inhibited by clinically approved drugs. Methods Enzymol 2015; 548:23-67. [PMID: 25399641 DOI: 10.1016/b978-0-12-397918-6.00002-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The aberrant activation of protein kinases is associated with many human diseases, most notably cancer. Due to this link between kinase deregulation and disease progression, kinases are one of the most targeted protein families for small-molecule inhibition. Within the last 15 years, the U.S. Food and Drug Administration has approved over 20 small-molecule inhibitors of protein kinases for use in the clinic. These inhibitors target the kinase active site and represent the successful hurdling by medicinal chemists of the formidable challenge posed by the high similarity among the active sites of the approximately 500 human kinases. We review the conserved structural features of kinases that are important for inhibitor binding as well as for catalysis. Many clinically approved drugs elicit selectivity by exploiting subtle variation within the kinase active site. We highlight some of the crystallographic studies on the kinase-inhibitor complexes that have provided valuable guidance for the development of these drugs as well as for future drug design efforts.
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Affiliation(s)
- Qi Wang
- Department of Molecular and Cell Biology, University of California, Berkeley, California, USA; California Institute for Quantitative Biosciences, University of California, Berkeley, California, USA
| | - Julie A Zorn
- Department of Molecular and Cell Biology, University of California, Berkeley, California, USA; California Institute for Quantitative Biosciences, University of California, Berkeley, California, USA
| | - John Kuriyan
- Department of Molecular and Cell Biology, University of California, Berkeley, California, USA; California Institute for Quantitative Biosciences, University of California, Berkeley, California, USA; Howard Hughes Medical Institute, University of California, Berkeley, California, USA; Department of Chemistry, University of California, Berkeley, California, USA; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
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15
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Abstract
Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.
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Affiliation(s)
- Zhong-Ru Xie
- Institute of Biomedical Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
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16
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Abstract
Fragment-based drug design has become an important strategy for drug design and development over the last decade. It has been used with particular success in the development of kinase inhibitors, which are one of the most widely explored classes of drug targets today. The application of fragment-based methods to discovering and optimizing kinase inhibitors can be a complicated and daunting task; however, a general process has emerged that has been highly fruitful. Here a practical outline of the fragment process used in kinase inhibitor design and development is laid out with specific examples. A guide to the overall process from initial discovery through fragment screening, including the difficulties in detection, to the computational methods available for use in optimization of the discovered fragments is reported.
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Affiliation(s)
- Jon A Erickson
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, 46285, USA,
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17
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Vijayan RSK, He P, Modi V, Duong-Ly KC, Ma H, Peterson JR, Dunbrack RL, Levy RM. Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors. J Med Chem 2014; 58:466-79. [PMID: 25478866 PMCID: PMC4326797 DOI: 10.1021/jm501603h] [Citation(s) in RCA: 139] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
![]()
Structural
coverage of the human kinome has been steadily increasing
over time. The structures provide valuable insights into the molecular
basis of kinase function and also provide a foundation for understanding
the mechanisms of kinase inhibitors. There are a large number of kinase
structures in the PDB for which the Asp and Phe of the DFG motif on
the activation loop swap positions, resulting in the formation of
a new allosteric pocket. We refer to these structures as “classical
DFG-out” conformations in order to distinguish them from conformations
that have also been referred to as DFG-out in the literature but that
do not have a fully formed allosteric pocket. We have completed a
structural analysis of almost 200 small molecule inhibitors bound
to classical DFG-out conformations; we find that they are recognized
by both type I and type II inhibitors. In contrast, we find that nonclassical
DFG-out conformations strongly select against type II inhibitors because
these structures have not formed a large enough allosteric pocket
to accommodate this type of binding mode. In the course of this study
we discovered that the number of structurally validated type II inhibitors
that can be found in the PDB and that are also represented in publicly
available biochemical profiling studies of kinase inhibitors is very
small. We have obtained new profiling results for several additional
structurally validated type II inhibitors identified through our conformational
analysis. Although the available profiling data for type II inhibitors
is still much smaller than for type I inhibitors, a comparison of
the two data sets supports the conclusion that type II inhibitors
are more selective than type I. We comment on the possible contribution
of the DFG-in to DFG-out conformational reorganization to the selectivity.
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Affiliation(s)
- R S K Vijayan
- Center for Biophysics & Computational Biology and Institute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
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18
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Ferrè F, Palmeri A, Helmer-Citterich M. Computational methods for analysis and inference of kinase/inhibitor relationships. Front Genet 2014; 5:196. [PMID: 25071826 PMCID: PMC4075008 DOI: 10.3389/fgene.2014.00196] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 06/13/2014] [Indexed: 12/21/2022] Open
Abstract
The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.
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Affiliation(s)
- Fabrizio Ferrè
- Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata Rome, Italy
| | - Antonio Palmeri
- Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata Rome, Italy
| | - Manuela Helmer-Citterich
- Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata Rome, Italy
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