Your browser doesn't support javascript.
loading
Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models.
Van de Graaf, Matthew W; Eggertsen, Taylor G; Zeigler, Angela C; Tan, Philip M; Saucerman, Jeffrey J.
Afiliación
  • Van de Graaf MW; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
  • Eggertsen TG; Children's National Hospital, Washington, DC, USA.
  • Zeigler AC; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
  • Tan PM; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
  • Saucerman JJ; Yale New Haven Hospital, New Haven, CT, USA.
J Physiol ; 2023 May 18.
Article en En | MEDLINE | ID: mdl-37199469
Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Physiol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Physiol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos