ArticleBioinformatics (Oxford, England)2011
A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Additive energetic contributions of multiple peptide positions determine the relative promiscuity of viral and human sequences for PDZ domain targets.Protein science : a publication of the Protein Society · 2023Article
- Additive energetic contributions of multiple peptide positions determine the relative promiscuity of viral and human sequences for PDZ domain targets.bioRxiv : the preprint server for biology · 2023Article
- Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Adaptable hydrogel networks with reversible linkages for tissue engineering.Advanced materials (Deerfield Beach, Fla.) · 2015Review
- Learning sequence determinants of protein:protein interaction specificity with sparse graphical models.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Data-driven prediction and design of bZIP coiled-coil interactions.PLoS computational biology · 2015Article
- Article
- The development and application of a quantitative peptide microarray based approach to protein interaction domain specificity space.Molecular & cellular proteomics : MCP · 2014Article
- Learning Sequence Determinants of Protein:protein Interaction Specificity with Sparse Graphical Models.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2014Article
- Cluster based prediction of PDZ-peptide interactions.BMC genomics · 2014Article
- Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions.Journal of computer-aided molecular design · 2013Article
- Predicting PDZ domain mediated protein interactions from structure.BMC bioinformatics · 2013Article
- A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.Proteins · 2012Article
- Evolutionarily conserved bias of amino-acid usage refines the definition of PDZ-binding motif.BMC genomics · 2011Article
- The multiple-specificity landscape of modular peptide recognition domains.Molecular systems biology · 2011Article
- Putting into practice domain-linear motif interaction predictions for exploration of protein networks.PloS one · 2011Article
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Authors and funding
6 authors.
Funding
Abstract
motivationPredicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders.
resultsWe developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction. AVAILABILITY AND IMPLEMENTATION: The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.
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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.