ArticleScientific reports2017
PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 93 citations in OpenAlex.
- KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes.Briefings in bioinformatics · 2026Article
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- KLSD: A Curated Kinase-Ligand Database Mapping Selectivity Landscapes and Polypharmacology.ACS omega · 2026Article
- Proximity-Based Phospho-Interactome (Prob-PhI) Characterization Reveals Distinct Signaling Activities of MEK1 and MEK2.Analytical chemistry · 2026Article
- KSMoFinder - Knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.bioRxiv : the preprint server for biology · 2025Article
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- KinasePred: A Computational Tool for Small-Molecule Kinase Target Prediction.International journal of molecular sciences · 2025Article
- KSMoFinder-knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.Bioinformatics advances · 2025Article
- Unveiling orphan receptor-like kinases in plants: novel client discovery using high-confidence library predictions in the Kinase-Client (KiC) assay.Frontiers in plant science · 2024Article
- Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.Briefings in bioinformatics · 2023Article
- An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images.PLOS digital health · 2023Article
- IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection.Molecular therapy. Nucleic acids · 2023Article
- Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection.Biomolecules · 2023Review
- KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.Genomics, proteomics & bioinformatics · 2023Article
- KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.PeerJ · 2023Article
- How phosphorylation impacts intrinsically disordered proteins and their function.Essays in biochemistry · 2022Article
- Thirty years of molecular dynamics simulations on posttranslational modifications of proteins.Physical chemistry chemical physics : PCCP · 2022Review
- C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features.International journal of molecular sciences · 2022Article
- Shining Light on Protein Kinase Biomarkers with Fluorescent Peptide Biosensors.Life (Basel, Switzerland) · 2022Review
- Ensemble learning-based feature selection for phosphorylation site detection.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
10 authors at 8 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein phosphorylation is a major form of post-translational modification (PTM) that regulates diverse cellular processes. In silico methods for phosphorylation site prediction can provide a useful and complementary strategy for complete phosphoproteome annotation. Here, we present a novel bioinformatics tool, PhosphoPredict, that combines protein sequence and functional features to predict kinase-specific substrates and their associated phosphorylation sites for 12 human kinases and kinase families, including ATM, CDKs, GSK-3, MAPKs, PKA, PKB, PKC, and SRC. To elucidate critical determinants, we identified feature subsets that were most informative and relevant for predicting substrate specificity for each individual kinase family. Extensive benchmarking experiments based on both five-fold cross-validation and independent tests indicated that the performance of PhosphoPredict is competitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and Musite. We found that combining protein functional and sequence features significantly improves phosphorylation site prediction performance across all kinases. Application of PhosphoPredict to the entire human proteome identified 150 to 800 potential phosphorylation substrates for each of the 12 kinases or kinase families. PhosphoPredict significantly extends the bioinformatics portfolio for kinase function analysis and will facilitate high-throughput identification of kinase-specific phosphorylation sites, thereby contributing to both basic and translational research programs.
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