Your browser doesn't support javascript.
loading
Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.
Guerler, Aysam; Baker, Dannon; van den Beek, Marius; Gruening, Bjoern; Bouvier, Dave; Coraor, Nate; Shank, Stephen D; Zehr, Jordan D; Schatz, Michael C; Nekrutenko, Anton.
Afiliación
  • Guerler A; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. aysam.guerler@gmail.com.
  • Baker D; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • van den Beek M; Department of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
  • Gruening B; Department of Bioinformatics, Freiburg University, Freiburg, Germany.
  • Bouvier D; Department of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
  • Coraor N; Department of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
  • Shank SD; Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
  • Zehr JD; Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
  • Schatz MC; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Nekrutenko A; Department of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
BMC Bioinformatics ; 24(1): 263, 2023 Jun 23.
Article en En | MEDLINE | ID: mdl-37353753
BACKGROUND: Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models. RESULTS: Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2's non-structural protein 3. We also produced models of SARS-CoV2's spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4. CONCLUSIONS: The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Mapeo de Interacción de Proteínas / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Mapeo de Interacción de Proteínas / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido