ArticleMolecular systems biology2011
The multiple-specificity landscape of modular peptide recognition domains.
Article in Molecular systems biology, 2011. 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.
- mimicINT: A workflow for microbe-host protein interaction inference.F1000Research · 2025Article
- Anoctamin 9 determines CaFrontiers in immunology · 2025Article
- A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs.bioRxiv : the preprint server for biology · 2024Article
- MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes.Briefings in bioinformatics · 2024Article
- The MHC Motif Atlas: a database of MHC binding specificities and ligands.Nucleic acids research · 2023Article
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- Protein context shapes the specificity of SH3 domain-mediated interactions in vivo.Nature communications · 2021Article
- Learning peptide recognition rules for a low-specificity protein.Protein science : a publication of the Protein Society · 2020Article
- Article
- Specificity in PDZ-peptide interaction networks: Computational analysis and review.Journal of structural biology: X · 2020Review
- Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.Nature biotechnology · 2019Article
- Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads.eLife · 2019Article
- Scribble: A master scaffold in polarity, adhesion, synaptogenesis, and proliferation.The Journal of cell biology · 2019Review
- Alteration of Amino Acid Profiling Influenced by the Active Ingredients of DanHong Injection After Prescription Optimization.Drug design, development and therapy · 2019Article
- Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries.Nature communications · 2018Article
- Combinatorial engineering of N-TIMP2 variants that selectively inhibit MMP9 and MMP14 function in the cell.Oncotarget · 2018Article
- The C-terminal extension landscape of naturally presented HLA-I ligands.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Accurate PDZ/Peptide Binding Specificity with Additive and Polarizable Free Energy Simulations.Biophysical journal · 2018Article
- Predicting Antigen Presentation-What Could We Learn From a Million Peptides?Frontiers in immunology · 2018Review
- Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.PLoS computational biology · 2017Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks.
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