Protein phosphorylation database and prediction tools

Protein phosphorylation, one of the main protein post-translational modifications, is required for regulating various life activities. Kinases and phosphatases that regulate protein phosphorylation in humans have been targeted to treat various diseases, particularly cancer. High-throughput experimental methods to discover protein phosphosites are laborious and time-consuming. The burgeoning databases and predictors provide essential infrastructure to the research community. To date, >60 publicly available phosphorylation databases and predictors each have been developed. In this review, we have comprehensively summarized the status and applicability of major online phosphorylation databases and predictors, thereby helping researchers rapidly select tools that are most suitable for their projects. Moreover, the organizational strategies and limitations of these databases and predictors have been highlighted, which may facilitate the development of better protein phosphorylation predictors in silico.

[1]  Haifeng Chen,et al.  PTMint database of experimentally verified PTM regulation on protein–protein interaction , 2022, Bioinform..

[2]  Ruifeng Xu,et al.  qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast , 2022, Nucleic Acids Res..

[3]  D. Mohanty,et al.  Pf-Phospho: a machine learning-based phosphorylation sites prediction tool for Plasmodium proteins , 2022, Briefings Bioinform..

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[5]  Xiaofeng Song,et al.  pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties , 2022, BMC Bioinformatics.

[6]  S. Kouchaki,et al.  Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network , 2022, Briefings Bioinform..

[7]  L. Elo,et al.  PhosPiR: an automated phosphoproteomic pipeline in R , 2021, Briefings Bioinform..

[8]  Zhongyan Li,et al.  dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications , 2021, Nucleic Acids Res..

[9]  Ze-xian Liu,et al.  qPTMplants: an integrative database of quantitative post-translational modifications in plants , 2021, Nucleic Acids Res..

[10]  Norman E. Davey,et al.  The Eukaryotic Linear Motif resource: 2022 release , 2021, Nucleic Acids Res..

[11]  Shaoping Shi,et al.  PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis , 2021, Briefings Bioinform..

[12]  Duolin Wang,et al.  A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction. , 2022, Methods in molecular biology.

[13]  Patrick Willems.  Exploring Posttranslational Modifications with the Plant PTM Viewer. , 2022, Methods in molecular biology.

[14]  Tzong-Yi Lee,et al.  KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites , 2021, bioRxiv.

[15]  Ao Li,et al.  PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information , 2021, Bioinform..

[16]  Z. Dosztányi,et al.  IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation , 2021, Nucleic Acids Res..

[17]  Yan Li,et al.  HisPhosSite: A comprehensive database of histidine phosphorylated proteins and sites. , 2021, Journal of proteomics.

[18]  S. Mande,et al.  Structure–sequence features based prediction of phosphosites of serine/threonine protein kinases of Mycobacterium tuberculosis , 2021, Proteins.

[19]  Miles W. Mee,et al.  ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update) , 2021, Frontiers in Cell and Developmental Biology.

[20]  Geoffrey I. Webb,et al.  iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization , 2021, Nucleic acids research.

[21]  Peilin Jia,et al.  KinaseMD: kinase mutations and drug response database , 2020, Nucleic Acids Res..

[22]  Quan Zou,et al.  VPTMdb: a viral posttranslational modification database , 2020, Briefings Bioinform..

[23]  Chris de Graaf,et al.  KLIFS: an overhaul after the first 5 years of supporting kinase research , 2020, Nucleic Acids Res..

[24]  Peter K. Sorger,et al.  The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases , 2020, Nucleic Acids Res..

[25]  Shaofeng Lin,et al.  EPSD: a well-annotated data resource of protein phosphorylation sites in eukaryotes , 2020, Briefings Bioinform..

[26]  Kuo-Chen Chou,et al.  iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[27]  Jingjing Xu,et al.  DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites. , 2020, Journal of proteome research.

[28]  Muhammad Arif,et al.  DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information. , 2020, Analytical biochemistry.

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[30]  Geoffrey I. Webb,et al.  PROSPECT: A web server for predicting protein histidine phosphorylation sites , 2020, J. Bioinform. Comput. Biol..

[31]  Yu Xue,et al.  dbPSP 2.0, an updated database of protein phosphorylation sites in prokaryotes , 2020, Scientific Data.

[32]  Duolin Wang,et al.  MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization , 2020, Nucleic Acids Res..

[33]  James C. Whisstock,et al.  PhosTransfer: A Deep Transfer Learning Framework for Kinase-Specific Phosphorylation Site Prediction in Hierarchy , 2020, PAKDD.

[34]  Kathryn E. Kirchoff,et al.  EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning , 2020, bioRxiv.

[35]  Yu Xue,et al.  GPS 5.0: An Update on the Prediction of Kinase-specific Phosphorylation Sites in Proteins , 2020, Genom. Proteom. Bioinform..

[36]  Yifan Zhang,et al.  Feature selection may improve deep neural networks for the bioinformatics problems , 2019, Bioinform..

[37]  H. Yao,et al.  NMR-based investigation into protein phosphorylation. , 2019, International journal of biological macromolecules.

[38]  Georgi K. Kanev,et al.  The Landscape of Atypical and Eukaryotic Protein Kinases. , 2019, Trends in pharmacological sciences.

[39]  Lennart Martens,et al.  Scop3P: a comprehensive resource of human phosphosites within their full context , 2019, bioRxiv.

[40]  Iman Deznabi,et al.  DeepKinZero: Zero-Shot Learning for Predicting Kinase-Phosphosite Associations Involving Understudied Kinases , 2019, bioRxiv.

[41]  A. D. de Brevern,et al.  Investigation of the impact of PTMs on the protein backbone conformation , 2019, Amino Acids.

[42]  P. Willems,et al.  The Plant PTM Viewer, a central resource for exploring plant protein modifications. , 2019, The Plant journal : for cell and molecular biology.

[43]  Maja Köhn,et al.  The human DEPhOsphorylation Database DEPOD: 2019 update , 2019, Database J. Biol. Databases Curation.

[44]  Yanchun Liang,et al.  Capsule network for protein post-translational modification site prediction , 2018, Bioinform..

[45]  Bin Zhang,et al.  15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms , 2018, Nucleic Acids Res..

[46]  Hsien-Da Huang,et al.  dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications , 2018, Nucleic Acids Res..

[47]  Yu Xue,et al.  iEKPD 2.0: an update with rich annotations for eukaryotic protein kinases, protein phosphatases and proteins containing phosphoprotein-binding domains , 2018, Nucleic Acids Res..

[48]  Qi Zhao,et al.  qPhos: a database of protein phosphorylation dynamics in humans , 2018, Nucleic Acids Res..

[49]  Morten Nielsen,et al.  A generic deep convolutional neural network framework for prediction of receptor–ligand interactions—NetPhosPan: application to kinase phosphorylation prediction , 2018, Bioinform..

[50]  Stéphane Bourg,et al.  PKIDB: A Curated, Annotated and Updated Database of Protein Kinase Inhibitors in Clinical Trials , 2018, Molecules.

[51]  Cathy H. Wu,et al.  iPTMnet: an integrated resource for protein post-translational modification network discovery , 2017, Nucleic Acids Res..

[52]  S. Komatsu,et al.  Phosphoproteomics: Protein Phosphorylation in Regulation of Seed Germination and Plant Growth. , 2017, Current protein & peptide science.

[53]  S. Ross,et al.  New Perspectives, Opportunities, and Challenges in Exploring the Human Protein Kinome. , 2018, Cancer research.

[54]  Ao Li,et al.  ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning , 2017, PeerJ.

[55]  Yanchun Liang,et al.  MusiteDeep: a deep‐learning framework for general and kinase‐specific phosphorylation site prediction , 2017, Bioinform..

[56]  Dinesh Gupta,et al.  KiPho: malaria parasite kinome and phosphatome portal , 2017, Database J. Biol. Databases Curation.

[57]  D. Barh,et al.  Two-Component Signal Transduction Systems of Pathogenic Bacteria As Targets for Antimicrobial Therapy: An Overview , 2017, Front. Microbiol..

[58]  Ming Chen,et al.  FPD: A comprehensive phosphorylation database in fungi , 2016, bioRxiv.

[59]  B. F. Francis Ouellette,et al.  ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins , 2017, bioRxiv.

[60]  Mikael Bodén,et al.  PhosphoPICK‐SNP: quantifying the effect of amino acid variants on protein phosphorylation , 2017, Bioinform..

[61]  Gerard Manning,et al.  Genomics and evolution of protein phosphatases , 2017, Science Signaling.

[62]  Dong Xu,et al.  Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB. , 2017, Methods in molecular biology.

[63]  S. Turk,et al.  KinMap: a web-based tool for interactive navigation through human kinome data , 2017, BMC Bioinformatics.

[64]  Shima Dastgheib,et al.  KinView: a visual comparative sequence analysis tool for integrated kinome research. , 2016, Molecular bioSystems.

[65]  Mikael Bodén,et al.  Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence , 2016, bioRxiv.

[66]  Shaofeng Lin,et al.  dbPAF: an integrative database of protein phosphorylation in animals and fungi , 2016, Scientific Reports.

[67]  Hamid D. Ismail,et al.  RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest , 2016, BioMed research international.

[68]  Hsien-Da Huang,et al.  dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins , 2015, Nucleic Acids Res..

[69]  Chris de Graaf,et al.  KLIFS: a structural kinase-ligand interaction database , 2015, Nucleic Acids Res..

[70]  Jean Yee Hwa Yang,et al.  Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data , 2015, Bioinform..

[71]  Tien Dung Nguyen,et al.  LymPHOS 2.0: an update of a phosphosite database of primary human T cells , 2015, Database J. Biol. Databases Curation.

[72]  Sean J. Humphrey,et al.  PhosphOrtholog: a web-based tool for cross-species mapping of orthologous protein post-translational modifications , 2015, BMC Genomics.

[73]  Wei Wang,et al.  Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites , 2015, Scientific Reports.

[74]  Ying Zhang,et al.  dbPSP: a curated database for protein phosphorylation sites in prokaryotes , 2015, Database J. Biol. Databases Curation.

[75]  G. Barton,et al.  14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides , 2015, Bioinform..

[76]  Shao-Ping Shi,et al.  Using support vector machines to identify protein phosphorylation sites in viruses. , 2015, Journal of molecular graphics & modelling.

[77]  Mikael Bodén,et al.  PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events , 2015, Bioinform..

[78]  Xiang Chen,et al.  Proteomic analysis and prediction of human phosphorylation sites in subcellular level reveal subcellular specificity , 2015, Bioinform..

[79]  Bin Zhang,et al.  PhosphoSitePlus, 2014: mutations, PTMs and recalibrations , 2014, Nucleic Acids Res..

[80]  Susan S. Taylor,et al.  ProKinO: A Unified Resource for Mining the Cancer Kinome , 2014, Human mutation.

[81]  Joaquín Dopazo,et al.  PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins , 2014, Nucleic Acids Res..

[82]  Xun Li,et al.  The human DEPhOsphorylation database DEPOD: a 2015 update , 2014, Nucleic Acids Res..

[83]  Philippe Ortet,et al.  P2CS: updates of the prokaryotic two-component systems database , 2014, Nucleic Acids Res..

[84]  Yu Xue,et al.  dbPPT: a comprehensive database of protein phosphorylation in plants , 2014, Database J. Biol. Databases Curation.

[85]  Yu Xue,et al.  Systematic Analysis of the Phosphoproteome and Kinase-substrate Networks in the Mouse Testis* , 2014, Molecular & Cellular Proteomics.

[86]  M. Mann,et al.  Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. , 2014, Cell reports.

[87]  Pierrick Craveur,et al.  PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins , 2014, Database J. Biol. Databases Curation.

[88]  K. Mechtler,et al.  Chasing Phosphoarginine Proteins: Development of a Selective Enrichment Method Using a Phosphatase Trap* , 2014, Molecular & Cellular Proteomics.

[89]  Hsien-Da Huang,et al.  RegPhos 2.0: an updated resource to explore protein kinase–substrate phosphorylation networks in mammals , 2014, Database J. Biol. Databases Curation.

[90]  Wei Li,et al.  SysPTM 2.0: an updated systematic resource for post-translational modification , 2014, Database J. Biol. Databases Curation.

[91]  Yongchao Dou,et al.  PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine , 2014, Amino Acids.

[92]  I. D. de Esch,et al.  KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space. , 2014, Journal of medicinal chemistry.

[93]  Dong Xu,et al.  P3DB 3.0: From plant phosphorylation sites to protein networks , 2013, Nucleic Acids Res..

[94]  Yu Xue,et al.  EKPD: a hierarchical database of eukaryotic protein kinases and protein phosphatases , 2013, Nucleic Acids Res..

[95]  Tzong-Yi Lee,et al.  ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins , 2013, BMC Bioinformatics.

[96]  Kara Dolinski,et al.  The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update , 2013, Database J. Biol. Databases Curation.

[97]  Peer Bork,et al.  PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins , 2012, Nucleic Acids Res..

[98]  Hsien-Da Huang,et al.  dbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications , 2012, Nucleic Acids Res..

[99]  Monika Zulawski,et al.  PhosPhAt goes kinases—searchable protein kinase target information in the plant phosphorylation site database PhosPhAt , 2012, Nucleic Acids Res..

[100]  Livia Perfetto,et al.  HuPho: the human phosphatase portal , 2012, The FEBS journal.

[101]  Zhengwei Zhu,et al.  CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..

[102]  Qiuming Yao,et al.  P3DB: An Integrated Database for Plant Protein Phosphorylation , 2012, Front. Plant Sci..

[103]  W. Lim,et al.  Systematic Functional Prioritization of Protein Posttranslational Modifications , 2012, Cell.

[104]  M. Westphall,et al.  Medicago PhosphoProtein Database: a repository for Medicago truncatula phosphoprotein data , 2012, Front. Plant Sci..

[105]  Bin Zhang,et al.  PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse , 2011, Nucleic Acids Res..

[106]  Yongfei Wang,et al.  PhosphoRice: a meta-predictor of rice-specific phosphorylation sites , 2012, Plant Methods.

[107]  M. Helmer-Citterich,et al.  PhosTryp: a phosphorylation site predictor specific for parasitic protozoa of the family trypanosomatidae , 2011, BMC Genomics.

[108]  Krys J. Kochut,et al.  ProKinO: An Ontology for Integrative Analysis of Protein Kinases in Cancer , 2011, PloS one.

[109]  M. Chou,et al.  Using the scan‐x Web Site to Predict Protein Post‐Translational Modifications , 2011, Current protocols in bioinformatics.

[110]  D. S. Reiner,et al.  The minimal kinome of Giardia lamblia illuminates early kinase evolution and unique parasite biology , 2011, Genome Biology.

[111]  Yu Xue,et al.  GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection. , 2011, Protein engineering, design & selection : PEDS.

[112]  Ladislav Stacho,et al.  Prediction of 492 human protein kinase substrate specificities , 2010, Proteome Science.

[113]  Florian Gnad,et al.  PHOSIDA 2011: the posttranslational modification database , 2010, Nucleic Acids Res..

[114]  Cathryn M. Gould,et al.  Phospho.ELM: a database of phosphorylation sites—update 2011 , 2010, Nucleic acids research.

[115]  Philippe Ortet,et al.  P2CS: a database of prokaryotic two-component systems , 2010, Nucleic Acids Res..

[116]  Hsien-Da Huang,et al.  RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans , 2010, Nucleic Acids Res..

[117]  Anna Tramontano,et al.  Phospho3D 2.0: an enhanced database of three-dimensional structures of phosphorylation sites , 2010, Nucleic Acids Res..

[118]  George M Church,et al.  Collection and Motif-Based Prediction of Phosphorylation Sites in Human Viruses , 2010, Science Signaling.

[119]  Dong Xu,et al.  Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites* , 2010, Molecular & Cellular Proteomics.

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[122]  Mike Tyers,et al.  PhosphoGRID: a database of experimentally verified in vivo protein phosphorylation sites from the budding yeast Saccharomyces cerevisiae , 2010, Database J. Biol. Databases Curation.

[123]  Eric D. Scheeff,et al.  Genomics, evolution, and crystal structure of a new family of bacterial spore kinases , 2009, Proteins.

[124]  Robert Schmidt,et al.  PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update , 2009, Nucleic Acids Res..

[125]  Gary D Bader,et al.  NetPath: a public resource of curated signal transduction pathways , 2010, Genome Biology.

[126]  P. Ortet,et al.  P2CS: a two-component system resource for prokaryotic signal transduction research , 2009, BMC Genomics.

[127]  Montserrat Carrascal,et al.  LymPHOS: Design of a phosphosite database of primary human T cells , 2009, Proteomics.

[128]  Yixue Li,et al.  SysPTM: A Systematic Resource for Proteomic Research on Post-translational Modifications* , 2009, Molecular & Cellular Proteomics.

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[131]  Geoffrey J. Barton,et al.  Kinomer v. 1.0: a database of systematically classified eukaryotic protein kinases , 2008, Nucleic Acids Res..

[132]  Dong Xu,et al.  P3DB: a plant protein phosphorylation database , 2008, Nucleic Acids Res..

[133]  Ivan Mijakovic,et al.  NetPhosBac – A predictor for Ser/Thr phosphorylation sites in bacterial proteins , 2009, Proteomics.

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[136]  Yu Xue,et al.  GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy *S , 2008, Molecular & Cellular Proteomics.

[137]  Bostjan Kobe,et al.  Predikin and PredikinDB: a computational framework for the prediction of protein kinase peptide specificity and an associated database of phosphorylation sites , 2008, BMC Bioinformatics.

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[139]  Joachim Selbig,et al.  PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor , 2007, Nucleic Acids Res..

[140]  Allegra Via,et al.  Phospho.ELM: a database of phosphorylation sites—update 2008 , 2007, Nucleic Acids Res..

[141]  Marco Bellinzoni,et al.  Mycobacterial Ser/Thr protein kinases and phosphatases: physiological roles and therapeutic potential. , 2008, Biochimica et biophysica acta.

[142]  M. Mann,et al.  PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites , 2007, Genome Biology.

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[146]  Hsien-Da Huang,et al.  KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns , 2007, Nucleic Acids Res..

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[151]  Allegra Via,et al.  Phospho3D: a database of three-dimensional structures of protein phosphorylation sites , 2006, Nucleic Acids Res..

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[157]  Hsien-Da Huang,et al.  dbPTM: an information repository of protein post-translational modification , 2005, Nucleic Acids Res..

[158]  Zoran Obradovic,et al.  Length-dependent prediction of protein intrinsic disorder , 2006, BMC Bioinformatics.

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