ArticleMolecular & cellular proteomics : MCP2016
Uncovering Phosphorylation-Based Specificities through Functional Interaction Networks.
Article in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- An AI-Ready Phosphorylation Meta-Analysis forJournal of proteome research · 2026Pooled it
- Phosphorylation in theJournal of proteome research · 2024Pooled it
- Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.Journal of proteome research · 2024Pooled it
- Mapping the proteome landscape inPlant signaling & behavior · 2026Article
- Article
- SnRK2.6 phosphorylates sucrose transporter ZmSUT1 to enhance yield by modulating leaf senescence in maize.Science advances · 2026Article
- A Landscape Analysis of Human SUMOylation.Molecular & cellular proteomics : MCP · 2026Article
- Orbitrap Astral-based proteome and phosphoproteome analysis identifies candidate proteins associated with the phosphatidate phosphatase MoPah1 in Magnaporthe oryzae.Scientific reports · 2026Article
- Phosphoproteomics unveils the signaling dynamics in neuronal cells stimulated with insulin and insulin-like growth factors.Cell communication and signaling : CCS · 2025Article
- Decoding protein N-phosphorylation functionality and sequence patterns.Briefings in bioinformatics · 2025Article
- KSMoFinder - Knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.bioRxiv : the preprint server for biology · 2025Article
- Transitions in the proteome and phospho-proteome during Xenopus laevis development.Developmental biology · 2025Article
- Searching for Sulfotyrosines (sY) in a HA(pY)STACK.Journal of proteome research · 2025Article
- KSMoFinder-knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.Bioinformatics advances · 2025Article
- PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals.Cell reports methods · 2024Article
- Nphos: Database and Predictor of Protein N-phosphorylation.Genomics, proteomics & bioinformatics · 2024Article
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- Chilling stress response in tobacco seedlings: insights from transcriptome, proteome, and phosphoproteome analyses.Frontiers in plant science · 2024Article
- A meta-analysis of rice phosphoproteomics data to understand variation in cell signalling across the rice pan-genome.bioRxiv : the preprint server for biology · 2023Article
- Deep Proteogenomics of a Photosynthetic Cyanobacterium.Journal of proteome research · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein kinases are an important class of enzymes involved in the phosphorylation of their targets, which regulate key cellular processes and are typically mediated by a specificity for certain residues around the target phospho-acceptor residue. While efforts have been made to identify such specificities, only ∼30% of human kinases have a significant number of known binding sites. We describe a computational method that utilizes functional interaction data and phosphorylation data to predict specificities of kinases. We applied this method to human kinases to predict substrate preferences for 57% of all known kinases and show that we are able to reconstruct well-known specificities. We used an in vitro mass spectrometry approach to validate four understudied kinases and show that predicted models closely resemble true specificities. We show that this method can be applied to different organisms and can be extended to other phospho-recognition domains. Applying this approach to different types of posttranslational modifications (PTMs) and binding domains could uncover specificities of understudied PTM recognition domains and provide significant insight into the mechanisms of signaling networks.
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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.