ArticleBMC bioinformatics2015
Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Ten quick tips for sequence-based prediction of protein properties using machine learning.PLoS computational biology · 2022Article
- ProteinGLUE multi-task benchmark suite for self-supervised protein modeling.Scientific reports · 2022Article
- Article
- ClusPro-DC: Dimer Classification by the Cluspro Server for Protein-Protein Docking.Journal of molecular biology · 2017Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundProtein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites.
resultsWe collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9% recall and up to 25.1% precision.
conclusionsTo our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.
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