Extending protein interaction networks using proteoforms and small molecules.
Luis Francisco Hernández Sánchez, Bram Burger, Rodrigo Alexander Castro Campos, Stefan Johansson, Pål Rasmus Njølstad, Harald Barsnes, Marc Vaudel
Author Information
Luis Francisco Hernández Sánchez: Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway.
Bram Burger: Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway. ORCID
Rodrigo Alexander Castro Campos: Departamento de Sistemas, Universidad Autónoma Metropolitana Azcapotzalco, Mexico City 02128, Mexico.
Stefan Johansson: Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway. ORCID
Pål Rasmus Njølstad: Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway.
Harald Barsnes: Department of Biomedicine, Proteomics Unit, University of Bergen, Bergen 5020, Norway.
Marc Vaudel: Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway. ORCID
MOTIVATION: Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation. RESULTS: We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability. AVAILABILITY AND IMPLEMENTATION: Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.
