ArticlePLoS computational biology2020
Accurate prediction of kinase-substrate networks using knowledge graphs.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Knowledge Graph-Driven AI in Biohealth: From Biomedical Discovery to Health Risk Prediction.Delaware journal of public health · 2026Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- KSMoFinder - Knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.bioRxiv : the preprint server for biology · 2025Article
- Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.Nature communications · 2025Article
- KSMoFinder-knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.Bioinformatics advances · 2025Article
- Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.Nature communications · 2024Article
- Article
- Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.PeerJ · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- A knowledge graph representation learning approach to predict novel kinase-substrate interactions.Molecular omics · 2022Article
- Proteasomal down-regulation of the proapoptotic MST2 pathway contributes to BRAF inhibitor resistance in melanoma.Life science alliance · 2022Article
- Interaction of LATS1 with SMAC links the MST2/Hippo pathway with apoptosis in an IAP-dependent manner.Cell death & disease · 2022Article
- Systematic identification of ALK substrates by integrated phosphoproteome and interactome analysis.Life science alliance · 2022Article
- Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.