ArticleGenomics, proteomics & bioinformatics2023
KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.
Article in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- Aurora A exacerbates antibiotic-resistant Staphylococcus aureus infection through ASC phosphorylation.EMBO reports · 2026Article
- Matrix Stiffness Orchestrates Mesenchymal Stem Cell Lineage Commitment Toward Osteogenesis and Adipogenesis Through the PIEZO1/SP1/STC2 Axis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.Briefings in bioinformatics · 2026Article
- Learning the PTM code through a coarse-to-fine mechanism-aware framework.Nature communications · 2026Article
- KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes.Briefings in bioinformatics · 2026Article
- Phosphoproteome Remodeling upon CDK1 Inhibition Restricts HSV-1 IE Gene Transcription and Replication.Cells · 2026Article
- Stratified signaling network remodeling of kinase-transcription factors' interactions in Parkinson's disease.Bioinformatics advances · 2026Article
- KSMoFinder - Knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.bioRxiv : the preprint server for biology · 2025Article
- CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network.Bioinformatics (Oxford, England) · 2025Article
- A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease.Briefings in bioinformatics · 2025Article
- Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets.Briefings in bioinformatics · 2025Article
- KSMoFinder-knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.Bioinformatics advances · 2025Article
- Identification of Critical Phosphorylation Sites Enhancing Kinase Activity With a Bimodal Fusion Framework.Molecular & cellular proteomics : MCP · 2025Article
- Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- Experimental measurement and computational prediction of bacterial Hanks-type Ser/Thr signaling system regulatory targets.Molecular microbiology · 2024Review
- In Vitro Identification of Phosphorylation Sites on TcPolβ by Protein Kinases TcCK1, TcCK2, TcAUK1, and TcPKC1 and Effect of Phorbol Ester on Activation by TcPKC of TcPolβ inMicroorganisms · 2024Article
- KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.iScience · 2024Article
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- Deciphering the phospho-signature induced by hepatitis B virus in primary human hepatocytes.Frontiers in microbiology · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.
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