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DISPOT: a simple knowledge-based protein domain interaction statistical potential.
Narykov, Oleksandr; Bogatov, Dmytro; Korkin, Dmitry.
Afiliação
  • Narykov O; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Bogatov D; Department of Computer Science, Boston University, Boston, MA, USA.
  • Korkin D; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.
Bioinformatics ; 35(24): 5374-5378, 2019 12 15.
Article em En | MEDLINE | ID: mdl-31350874
ABSTRACT
MOTIVATION The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task.

RESULTS:

Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. AVAILABILITY AND IMPLEMENTATION DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub https//github.com/korkinlab/dispot and standalone docker images on DockerHub https//hub.docker.com/r/korkinlab/dispot. The web server is freely available at http//dispot.korkinlab.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software Idioma: En Ano de publicação: 2019 Tipo de documento: Article