KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns.
paper
Cited
Public
- Authors
- Wong, Yung-Hao; Lee, Tzong-Yi; Liang, Han-Kuen; Huang, Chia-Mao; Wang, Ting-Yuan; Yang, Yi-Huan; Chu, Chia-Huei; Huang, Hsien-Da; Ko, Ming-Tat; Hwang, Jenn-Kang
- Year
- 2007
- Journal
- Nucleic acids research
- PMID
- 17517770
- DOI
- 10.1093/nar/gkm322
- PMCID
- PMC1933228
Due to the importance of protein phosphorylation in cellular control, many researches are undertaken to predict the kinase-specific phosphorylation sites. Referred to our previous work, KinasePhos 1.0, incorporated profile hidden Markov model (HMM) with flanking residues of the kinase-specific phosphorylation sites. Herein, a new web server, KinasePhos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites. The coupling pattern [XdZ] denotes the amino acid coupling-pattern of amino acid types X and Z that are separated by d amino acids. The differences or quotients of coupling strength C(XdZ) between the positive set of phosphorylation sites and the background set of whole protein sequences from Swiss-Prot are computed to determine the number of coupling patterns for training SVM models. After the evaluation based on k-fold cross-validation and Jackknife cross-validation, the average predictive accuracy of phosphorylated serine, threonine, tyrosine and histidine are 90, 93, 88 and 93%, respectively. KinasePhos 2.0 performs better than other tools previously developed. The proposed web server is freely available at http://KinasePhos2.mbc.nctu.edu.tw/.
The system flow of KinasePhos 2.0.
LLM interpretation
This figure is a system flow diagram illustrating the architecture of KinasePhos 2.0. It outlines a pipeline consisting of four main stages: data preprocessing (including sequence logo generation), feature extraction based on coupling patterns, model training using a Support Vector Machine (SVM) with parameter optimization, and model evaluation via cross-validation. The diagram includes a performance plot showing precision, sensitivity, specificity, and accuracy across different bit scores.
The comparison for the average precision (Prec), sensitivity (Sn), specificity (Sp) and accuracy (Acc) among the models trained with various features in phosphoserine, phosphothreonine, phosphotyrosine and phosphohistidine.
LLM interpretation
This figure consists of four line graphs comparing the performance of various feature-based models across four phosphorylation types: phosphoserine, phosphothreonine, phosphotyrosine, and phosphohistidine. The x-axes list four performance metrics (Precision, Sensitivity, Specificity, and Accuracy), while the y-axes represent the score from 0.00 to 1.00. Multiple models, including "KinasePhos 1.0" and various sequence-based feature sets, are plotted as colored lines to show relative performance trends across these metrics.
The web interface of KinasePhos 2.0.
LLM interpretation
This figure is a screenshot of the KinasePhos 2.0 web interface, divided into input and output sections. The left side shows the "Submission" area for entering protein sequences in FASTA format and a "Predict on" section with checklists for various kinase groups. The right side displays "Predicted" results, including a summary table of phosphorylated sites for CTNB1_HUMAN and a sequence alignment map showing specific predicted sites and their corresponding predictive models.
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| EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC. | Chang TH et al. | β | 2013 | β |
| Imatinib mesilate-induced phosphatidylinositol 3-kinase signalling and improved survival in insulin-producing cells: role of Src homology 2-containing inositol 5'-phosphatase interaction with c-Abl. | Mokhtari D et al. | β | 2013 | β |
| Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures. | Su MG et al. | β | 2013 | β |
| iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. | Xu Y et al. | β | 2013 | β |
| iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. | Xu Y et al. | β | 2013 | β |
| Mutations in DNA-binding loop of NFAT5 transcription factor produce unique outcomes on protein-DNA binding and dynamics. | Li M et al. | β | 2013 | β |
| PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites. | Zou L et al. | β | 2013 | β |
| Power and promise of ubiquitin carboxyl-terminal hydrolase 37 as a target of cancer therapy. | Chen YJ et al. | β | 2013 | β |
| Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins. | Jiang Y et al. | β | 2013 | β |
| Prediction of carbamylated lysine sites based on the one-class k-nearest neighbor method. | Huang G et al. | β | 2013 | β |
| Prediction of S-glutathionylation sites based on protein sequences. | Sun C et al. | β | 2013 | β |
| Promotion of DNA repair by nuclear IKKΞ² phosphorylation of ATM in response to genotoxic stimuli. | Sakamoto K et al. | β | 2013 | β |
| Protein analysis by shotgun/bottom-up proteomics. | Zhang Y et al. | β | 2013 | β |
| Proteomic and phosphoproteomic analysis of polyethylene glycol-induced osmotic stress in root tips of common bean (Phaseolus vulgaris L.). | Yang ZB et al. | β | 2013 | β |
| Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies. | Bruce C et al. | β | 2013 | β |
| Src regulates the activity of the ING1 tumor suppressor. | Yu L et al. | β | 2013 | β |
| Systematic analysis of the Plk-mediated phosphoregulation in eukaryotes. | Liu Z et al. | β | 2013 | β |
| ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins. | Huang KY et al. | β | 2013 | β |
| AMS 4.0: consensus prediction of post-translational modifications in protein sequences. | Plewczynski D et al. | β | 2012 | β |
| Computational prediction and analysis of protein Ξ³-carboxylation sites based on a random forest method. | Zhang N et al. | β | 2012 | β |
| CPhos: a program to calculate and visualize evolutionarily conserved functional phosphorylation sites. | Zhao B et al. | β | 2012 | β |
| Functional analyses of endometriosis-related polymorphisms in the estrogen synthesis and metabolism-related genes. | Wang HS et al. | β | 2012 | β |
| Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. | Goel R et al. | β | 2012 | β |
| Identifying protein phosphorylation sites with kinase substrate specificity on human viruses. | BretaΓ±a NA et al. | β | 2012 | β |
| In silico studies of potential phosphoresidues in the human nucleophosmin/B23: its kinases and related biological processes. | Ramos-EchazΓ‘bal G et al. | β | 2012 | β |
| MMFPh: a maximal motif finder for phosphoproteomics datasets. | Wang T et al. | β | 2012 | β |
| Novel tyrosine phosphorylation sites in rat skeletal muscle revealed by phosphopeptide enrichment and HPLC-ESI-MS/MS. | Zhang X et al. | β | 2012 | β |
| Phosphoproteomics and molecular cardiology: techniques, applications and challenges. | Sun Z et al. | β | 2012 | β |
| PhosphoRice: a meta-predictor of rice-specific phosphorylation sites. | Que S et al. | β | 2012 | β |
| Prediction of functional phosphorylation sites by incorporating evolutionary information. | Niu S et al. | β | 2012 | β |
| Probabilistic Prediction of Protein Phosphorylation Sites Using Classification Relevance Units Machines. | Menor M et al. | β | 2012 | β |
| Protein kinase C (PKC) isozyme-specific substrates and their design. | Kang JH et al. | β | 2012 | β |
| Retracted: Prediction of posttranslational modification sites from sequences with kernel methods. | β | β | 2012 | β |
| Structural and functional characterization of the protein kinase Mps1 in Arabidopsis thaliana. | de Oliveira EA et al. | β | 2012 | β |
| Virtual interactomics of proteins from biochemical standpoint. | Kubrycht J et al. | β | 2012 | β |
| A majority of the cancer/testis antigens are intrinsically disordered proteins. | Rajagopalan K et al. | β | 2011 | β |
| A single amino acid residue controls Ca2+ signaling by an octopamine receptor from Drosophila melanogaster. | Hoff M et al. | β | 2011 | β |
| Computational prediction of eukaryotic phosphorylation sites. | Trost B et al. | β | 2011 | β |
| IGF-1 participates differently in regulation of severing activity of katanin and spastin. | Korulu S et al. | β | 2011 | β |
| In silico prediction of post-translational modifications. | Liu C et al. | β | 2011 | β |
| Interaction proteomics analysis of polycomb proteins defines distinct PRC1 complexes in mammalian cells. | Vandamme J et al. | β | 2011 | β |
| Investigation and identification of protein Ξ³-glutamyl carboxylation sites. | Lee TY et al. | β | 2011 | β |
| Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications. | Remmerie N et al. | β | 2011 | β |
| Phosphorylation of the zebrafish M6Ab at serine 263 contributes to filopodium formation in PC12 cells and neurite outgrowth in zebrafish embryos. | Huang KY et al. | β | 2011 | β |
| PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity. | Lee TY et al. | β | 2011 | β |
| ProteoConnections: a bioinformatics platform to facilitate proteome and phosphoproteome analyses. | Courcelles M et al. | β | 2011 | β |
| Proteomic databases and tools to decipher post-translational modifications. | Kamath KS et al. | β | 2011 | β |
| RegPhos: a system to explore the protein kinase-substrate phosphorylation network in humans. | Lee TY et al. | β | 2011 | β |
| AMS 3.0: prediction of post-translational modifications. | Basu S et al. | β | 2010 | β |
| AtSIG6, a plastid sigma factor from Arabidopsis, reveals functional impact of cpCK2 phosphorylation. | Schweer J et al. | β | 2010 | β |
| Identifying protein-kinase-specific phosphorylation sites based on the Bagging-AdaBoost ensemble approach. | Yu Z et al. | β | 2010 | β |
| Learning biological network using mutual information and conditional independence. | Kim DC et al. | β | 2010 | β |
| Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. | Gao J et al. | β | 2010 | β |
| Novel molecular imaging platform for monitoring oncological kinases. | Nyati S et al. | β | 2010 | β |
| Phosphoproteomics for the masses. | Grimsrud PA et al. | β | 2010 | β |
| Phosphorylated and nonphosphorylated serine and threonine residues evolve at different rates in mammals. | Chen SC et al. | β | 2010 | β |
| PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship. | Jung I et al. | β | 2010 | β |
| Prediction of posttranslational modification of proteins from their amino acid sequence. | Eisenhaber B et al. | β | 2010 | β |
| PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies. | Naegle KM et al. | β | 2010 | β |
| Ser 524 is a phosphorylation site in MUTYH and Ser 524 mutations alter 8-oxoguanine (OG): a mismatch recognition. | Kundu S et al. | β | 2010 | β |
| SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study. | Saccone SF et al. | β | 2010 | β |
| Strategies for quantitation of phosphoproteomic data. | Palmisano G et al. | β | 2010 | β |
| The Musite open-source framework for phosphorylation-site prediction. | Gao J et al. | β | 2010 | β |
| Toward a complete in silico, multi-layered embryonic stem cell regulatory network. | Xu H et al. | β | 2010 | β |
| A biochemical genomics screen for substrates of Ste20p kinase enables the in silico prediction of novel substrates. | Annan RB et al. | β | 2009 | β |
| A comprehensive resource for integrating and displaying protein post-translational modifications. | Lee TY et al. | β | 2009 | β |
| CAPIH: a Web interface for comparative analyses and visualization of host-HIV protein-protein interactions. | Lin FK et al. | β | 2009 | β |
| Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins. | Durek P et al. | β | 2009 | β |
| Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks. | Tan CS et al. | β | 2009 | β |
| Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information. | Xiong W et al. | β | 2009 | β |
| Site specificity of agonist and second messenger-activated kinases for somatostatin receptor subtype 2A (Sst2A) phosphorylation. | Liu Q et al. | β | 2009 | β |
| The box H/ACA snoRNP assembly factor Shq1p is a chaperone protein homologous to Hsp90 cochaperones that binds to the Cbf5p enzyme. | Godin KS et al. | β | 2009 | β |
| Charge environments around phosphorylation sites in proteins. | Kitchen J et al. | β | 2008 | β |
| Development of phosphopeptide enrichment techniques for phosphoproteome analysis. | Han G et al. | β | 2008 | β |
| GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. | Xue Y et al. | β | 2008 | β |
| Linear motif atlas for phosphorylation-dependent signaling. | Miller ML et al. | β | 2008 | β |
| NetworKIN: a resource for exploring cellular phosphorylation networks. | Linding R et al. | β | 2008 | β |
| PhosphoPOINT: a comprehensive human kinase interactome and phospho-protein database. | Yang CY et al. | β | 2008 | β |
| Prediction of compounds' biological function (metabolic pathways) based on functional group composition. | Cai YD et al. | β | 2008 | β |
| Prediction of kinase-specific phosphorylation sites using conditional random fields. | Dang TH et al. | β | 2008 | β |
| Upstream stimulatory factors, USF1 and USF2 are differentially expressed during Xenopus embryonic development. | Fujimi TJ et al. | β | 2008 | β |