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The power of computational proteomics platforms to decipher protein-protein interactions.
González-Avendaño, Mariela; López, Joaquín; Vergara-Jaque, Ariela; Cerda, Oscar.
Afiliación
  • González-Avendaño M; Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, Talca, Chile; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile.
  • López J; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile; Program of Cellular and Molecular Biology, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, Universidad de Chile, Santiago, Chile.
  • Vergara-Jaque A; Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, Talca, Chile; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile. Electronic address: arvergara@utalca.cl.
  • Cerda O; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile; Program of Cellular and Molecular Biology, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, Universidad de Chile, Santiago, Chile. Electronic address: oscarcerda@uchile.cl.
Curr Opin Struct Biol ; 88: 102882, 2024 Oct.
Article en En | MEDLINE | ID: mdl-39003917
ABSTRACT
Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / Mapeo de Interacción de Proteínas / Proteómica Límite: Humans Idioma: En Revista: Curr Opin Struct Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Chile Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / Mapeo de Interacción de Proteínas / Proteómica Límite: Humans Idioma: En Revista: Curr Opin Struct Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Chile Pais de publicación: Reino Unido