Using neighborhood cohesiveness to infer interactions between protein domains
Author(s) -
Joan Segura,
Carlos Óscar S. Sorzano,
Jesús Cuenca-Alba,
Patrick Aloy,
J.M. Carazo
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv188
Subject(s) - computer science , cluster analysis , group cohesiveness , clustering coefficient , measure (data warehouse) , domain (mathematical analysis) , protein–protein interaction , interaction information , field (mathematics) , data mining , interaction network , protein interaction networks , theoretical computer science , machine learning , biology , mathematics , statistics , genetics , psychology , social psychology , mathematical analysis , gene , pure mathematics
In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps, or interactomes, for a number of relevant organisms, including human. However, current interactomes provide a somehow limited picture of the molecular details involving protein interactions, mostly because essential experimental information, especially structural data, is lacking. Indeed, the gap between structural and interactomics information is enlarging and thus, for most interactions, key experimental information is missing. We elaborate on the observation that many interactions between proteins involve a pair of their constituent domains and, thus, the knowledge of how protein domains interact adds very significant information to any interactomic analysis.
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