Validation of protein-protein interactions in databases and resources: the need to identify interaction detection methods that provide binary or indirect experimental evidences
Year: 2019
Type: other
Abstract: The mapping and assembly of all the protein–protein physical interactions that occur in a cellular system is essential to compile the protein interaction networks that drive the different biological processes at molecular level. We have performed an update of APID interactomes database ( http://apid.dep.usal.es ) including a redefinition of several key properties of the protein-to-protein physical interactions (PPIs) to provide a more precise data integration and to avoid false duplicated records. APID integrates all experimentally detected PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data (BioPIex, http://bioplex.hms.harvard.edu/ and HuRI, http://interactome.baderlab.org/) and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the ontology and controlled vocabulary developed by HUPO PSI-MI). Within this data mining framework, all interaction detection methods have been grouped into two main types: binary physical direct detection methods and indirect detection methods. As a result of these redefinitions, we obtained unified protein interactomes including the specific experimental evidences that support each PPI, indicating whether the interactions can be considered binary (i.e. supported by at least one binary detection method) or not.
Authors: Diego Alonso-López, Francisco J. Campos-Laborie, Miguel Angel Gutierrez-García, Luke Lambourne, Michael A. Calderwood, Marc Vidal, Javier De Las Rivas
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2954835803