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• W2103485392 abstract "Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues.Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone." @default.
• W2103485392 created "2016-06-24" @default.
• W2103485392 creator A5032731817 @default.
• W2103485392 creator A5064905883 @default.
• W2103485392 date "2007-01-15" @default.
• W2103485392 modified "2023-10-18" @default.
• W2103485392 title "ISIS: interaction sites identified from sequence" @default.
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• W2103485392 doi "https://doi.org/10.1093/bioinformatics/btl303" @default.
• W2103485392 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17237081" @default.
• W2103485392 hasPublicationYear "2007" @default.
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