title: Protein interactions in human genetic diseases.
abstract: We present a novel method that combines protein structure information with protein interaction data to identify residues that form part of an interaction interface. Our prediction method can retrieve interaction hotspots with an accuracy of 60% (at a 20% false positive rate). The method was applied to all mutations in the Online Mendelian Inheritance in Man (OMIM) database, predicting 1,428 mutations to be related to an interaction defect. Combining predicted and hand-curated sets, we discuss how mutations affect protein interactions in general.
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK. bsb@sanger.ac.uk
authors.forename: Benjamin
authors.initials: B
authors.lastname: Schuster-Böckler
authors.forename: Alex
authors.initials: A
authors.lastname: Bateman
mesh_terms: D030541:Databases, Genetic; D030562:Databases, Protein; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D006801:Humans; D008722:Methods; D009154:Mutation; D011485:Protein Binding / Q000235:genetics; D011506:Proteins / Q000235:genetics* / Q000378:metabolism
chemical_list: D011506:Proteins