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Gou Y, Liu D, Chen M, Wei Y, Huang X, Han C, Feng Z, Zhang C, Lu T, Peng D, Xue Y. GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs. Nucleic Acids Res 2024:gkae346. [PMID: 38709873 DOI: 10.1093/nar/gkae346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
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Affiliation(s)
- Yujie Gou
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Dan Liu
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yuxiang Wei
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xinhe Huang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Zihao Feng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Chi Zhang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Teng Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing100190, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing210031, China
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2
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Esmaili F, Pourmirzaei M, Ramazi S, Shojaeilangari S, Yavari E. A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1266-1285. [PMID: 37863385 PMCID: PMC11082408 DOI: 10.1016/j.gpb.2023.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 01/16/2023] [Accepted: 03/23/2023] [Indexed: 10/22/2023]
Abstract
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.
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Affiliation(s)
- Farzaneh Esmaili
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Mahdi Pourmirzaei
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Shahin Ramazi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-111, Iran.
| | - Seyedehsamaneh Shojaeilangari
- Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran 33535-111, Iran
| | - Elham Yavari
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
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3
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Goldtzvik Y, Sen N, Lam SD, Orengo C. Protein diversification through post-translational modifications, alternative splicing, and gene duplication. Curr Opin Struct Biol 2023; 81:102640. [PMID: 37354790 DOI: 10.1016/j.sbi.2023.102640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/05/2023] [Accepted: 05/24/2023] [Indexed: 06/26/2023]
Abstract
Proteins provide the basis for cellular function. Having multiple versions of the same protein within a single organism provides a way of regulating its activity or developing novel functions. Post-translational modifications of proteins, by means of adding/removing chemical groups to amino acids, allow for a well-regulated and controlled way of generating functionally distinct protein species. Alternative splicing is another method with which organisms possibly generate new isoforms. Additionally, gene duplication events throughout evolution generate multiple paralogs of the same genes, resulting in multiple versions of the same protein within an organism. In this review, we discuss recent advancements in the study of these three methods of protein diversification and provide illustrative examples of how they affect protein structure and function.
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Affiliation(s)
- Yonathan Goldtzvik
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Neeladri Sen
- Department of Structural and Molecular Biology, University College London, London, United Kingdom. https://twitter.com/@NeeladriSen
| | - Su Datt Lam
- Department of Structural and Molecular Biology, University College London, London, United Kingdom; Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Christine Orengo
- Department of Structural and Molecular Biology, University College London, London, United Kingdom.
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4
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Mohammadi-Shemirani P, Sood T, Paré G. From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators. Curr Atheroscler Rep 2023; 25:55-65. [PMID: 36595202 PMCID: PMC9807989 DOI: 10.1007/s11883-022-01078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW 'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed. RECENT FINDINGS Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
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Affiliation(s)
- Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
| | - Tushar Sood
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON Canada
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5
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Deichaite I, Hopper A, Krockenberger L, Sears TJ, Sutton L, Ray X, Sharabi A, Navon A, Sanghvi P, Carter H, Moiseenko V. Germline genetic biomarkers to stratify patients for personalized radiation treatment. J Transl Med 2022; 20:360. [PMID: 35962345 PMCID: PMC9373374 DOI: 10.1186/s12967-022-03561-x] [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: 06/13/2022] [Accepted: 07/29/2022] [Indexed: 11/10/2022] Open
Abstract
Background Precision medicine incorporating genetic profiling is becoming a standard of care in medical oncology. However, in the field of radiation oncology there is limited use of genetic profiling and the impact of germline genetic biomarkers on radiosensitivity, radioresistance, or patient outcomes after radiation therapy is poorly understood. In HNSCC, the toxicity associated with treatment can cause delays or early cessation which has been associated with worse outcomes. Identifying potential biomarkers which can help predict toxicity, as well as response to treatment, is of significant interest. Methods Patients with HNSCC who received RT and underwent next generation sequencing of somatic tumor samples, transcriptome RNA-seq with matched normal tissue samples were included. Patients were then grouped by propensity towards increased late vs. early toxicity (Group A) and those without (Group B), assessed by CTCAE v5.0. The groups were then analyzed for association of specific germline variants with toxicity and clinical outcomes. Results In this study we analyzed 37 patients for correlation between germline variants and toxicity. We observed that TSC2, HLA-A, TET2, GEN1, NCOR2 and other germline variants were significantly associated with long term toxicities. 34 HNSCC patients treated with curative intent were evaluated for clinical outcomes. Group A had significantly improved overall survival as well as improved rates of locoregional recurrence and metastatic disease. Specific variants associated with improved clinical outcomes included TSC2, FANCD2, and PPP1R15A, while the HLA-A and GEN1 variants were not correlated with survival or recurrence. A group of five HLA-DMA/HLA-DMB variants was only found in Group B and was associated with a higher risk of locoregional recurrence. Conclusions This study indicates that germline genetic biomarkers may have utility in predicting toxicity and outcomes after radiation therapy and deserve further investigation in precision radiation medicine approaches.
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Affiliation(s)
- Ida Deichaite
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA. .,Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
| | - Austin Hopper
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Lena Krockenberger
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Timothy J Sears
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Leisa Sutton
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Xenia Ray
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Andrew Sharabi
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.,Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Ami Navon
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Parag Sanghvi
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.,Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
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6
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Pellegrina D, Bahcheli AT, Krassowski M, Reimand J. Human phospho-signaling networks of SARS-CoV-2 infection are rewired by population genetic variants. Mol Syst Biol 2022; 18:e10823. [PMID: 35579274 PMCID: PMC9112486 DOI: 10.15252/msb.202110823] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 11/23/2022] Open
Abstract
SARS-CoV-2 infection hijacks signaling pathways and induces protein-protein interactions between human and viral proteins. Human genetic variation may impact SARS-CoV-2 infection and COVID-19 pathology; however, the genetic variation in these signaling networks remains uncharacterized. Here, we studied human missense single nucleotide variants (SNVs) altering phosphorylation sites modulated by SARS-CoV-2 infection, using machine learning to identify amino acid substitutions altering kinase-bound sequence motifs. We found 2,033 infrequent phosphorylation-associated SNVs (pSNVs) that are enriched in sequence motif alterations, potentially reflecting the evolution of signaling networks regulating host defenses. Proteins with pSNVs are involved in viral life cycle and host responses, including RNA splicing, interferon response (TRIM28), and glucose homeostasis (TBC1D4) with potential associations with COVID-19 comorbidities. pSNVs disrupt CDK and MAPK substrate motifs and replace these with motifs of Tank Binding Kinase 1 (TBK1) involved in innate immune responses, indicating consistent rewiring of signaling networks. Several pSNVs associate with severe COVID-19 and hospitalization (STARD13, ARFGEF2). Our analysis highlights potential genetic factors contributing to inter-individual variation of SARS-CoV-2 infection and COVID-19 and suggests leads for mechanistic and translational studies.
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Affiliation(s)
- Diogo Pellegrina
- Computational Biology ProgramOntario Institute for Cancer ResearchTorontoONCanada
| | - Alexander T Bahcheli
- Computational Biology ProgramOntario Institute for Cancer ResearchTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Michal Krassowski
- Medical Sciences DivisionNuffield Department of Women's and Reproductive HealthUniversity of OxfordOxfordUK
| | - Jüri Reimand
- Computational Biology ProgramOntario Institute for Cancer ResearchTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- Department of Medical BiophysicsUniversity of TorontoTorontoONCanada
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7
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Xiao D, Kim HJ, Pang I, Yang P. Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities. Bioinformatics 2022; 38:1956-1963. [PMID: 35015814 PMCID: PMC9113330 DOI: 10.1093/bioinformatics/btac015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 12/21/2021] [Accepted: 01/06/2022] [Indexed: 11/29/2022] Open
Abstract
Motivation The advance of mass spectrometry-based technologies enabled the profiling of the phosphoproteomes of a multitude of cell and tissue types. However, current research primarily focused on investigating the phosphorylation dynamics in specific cell types and experimental conditions, whereas the phosphorylation events that are common across cell/tissue types and stable regardless of experimental conditions are, so far, mostly ignored. Results Here, we developed a statistical framework to identify the stable phosphoproteome across 53 human phosphoproteomics datasets, covering 40 cell/tissue types and 194 conditions/treatments. We demonstrate that the stably phosphorylated sites (SPSs) identified from our statistical framework are evolutionarily conserved, functionally important and enriched in a range of core signaling and gene pathways. Particularly, we show that SPSs are highly enriched in the RNA splicing pathway, an essential cellular process in mammalian cells, and frequently disrupted by cancer mutations, suggesting a link between the dysregulation of RNA splicing and cancer development through mutations on SPSs. Availability and implementation The source code for data analysis in this study is available from Github repository https://github.com/PYangLab/SPSs under the open-source license of GPL-3. The data used in this study are publicly available (see Section 2.8). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Di Xiao
- Computational Systems Biology Group
| | - Hani Jieun Kim
- Computational Systems Biology Group.,Charles Perkins Centre, School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
| | - Ignatius Pang
- Bioinformatics Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Pengyi Yang
- Computational Systems Biology Group.,Charles Perkins Centre, School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
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8
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Li Z, Li S, Luo M, Jhong JH, Li W, Yao L, Pang Y, Wang Z, Wang R, Ma R, Yu J, Huang Y, Zhu X, Cheng Q, Feng H, Zhang J, Wang C, Hsu JBK, Chang WC, Wei FX, Huang HD, Lee TY. dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications. Nucleic Acids Res 2021; 50:D471-D479. [PMID: 34788852 PMCID: PMC8728263 DOI: 10.1093/nar/gkab1017] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/08/2021] [Accepted: 10/13/2021] [Indexed: 01/02/2023] Open
Abstract
Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.
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Affiliation(s)
- Zhongyan Li
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Mengqi Luo
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenshuo Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Rulan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Renfei Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jinhan Yu
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuqi Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xiaoning Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Qifan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hexiang Feng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jiahong Zhang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chunxuan Wang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Feng-Xiang Wei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi 154007, China.,Shenzhen Children's Hospital of China Medical University, Shenzhen 518172, China
| | - Hsien-Da Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
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