ArticlePLoS computational biology2019
CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Phosphoproteomic Landscape of HDLBP: Insights into Function and Disease Associations.International journal of molecular sciences · 2026Article
- Revealing RELL1 signaling relying on the phosphoregulatory network analysis.Frontiers in bioinformatics · 2026Article
- Inference of differential kinase interaction networks with KINference.Bioinformatics (Oxford, England) · 2025Article
- Experimental measurement and computational prediction of bacterial Hanks-type Ser/Thr signaling system regulatory targets.Molecular microbiology · 2024Review
- SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data.NAR genomics and bioinformatics · 2023Article
- Article
- An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells.Nature communications · 2023Article
- Pharmacological approaches to understanding protein kinase signaling networks.Frontiers in pharmacology · 2023Review
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- Functional characterization of co-phosphorylation networks.Bioinformatics (Oxford, England) · 2022Article
- Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data.PLoS computational biology · 2022Article
- Co-phosphorylation networks reveal subtype-specific signaling modules in breast cancer.Bioinformatics (Oxford, England) · 2021Article
- Robust inference of kinase activity using functional networks.Nature communications · 2021Article
- DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases.Bioinformatics (Oxford, England) · 2020Article
- Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources.Clinical proteomics · 2020Review
- Integrated querying and version control of context-specific biological networks.Database : the journal of biological databases and curation · 2020Article
- An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation.Molecular & cellular proteomics : MCP · 2019Article
- Dissecting the mechanisms of cell division.The Journal of biological chemistry · 2019Review
- Footprint-based functional analysis of multiomic data.Current opinion in systems biology · 2019Review
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7 authors.
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Abstract
We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases' substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations.
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