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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
Human Interactomics: Comparative Analysis of Different Protein Interaction Resources and Construction of a Cancer
Javier De Las Rivas1, Diego Alonso-López1, Mónica M Arroyo2
1Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.
Abstract:
Unraveling the protein interaction wiring that occurs in human cells as a scaffold of biological processes requires the identification of all elements that constitute such molecular interaction networks. Proteome-wide experimental studies and bioinformatic comprehensive efforts have provided reliable and updated compendiums of the human protein interactome. In this work, we present a current view of available databases of human protein-protein interactions (PPIs) that allow building protein interaction networks. We also investigate human proteins as targets of specific drugs to analyze how chemicals interact with different target proteins, placing also the study in a network relational space. Hence, we undertake a description of several major drug-target resources to provide a present perspective of the associations between human proteins and specific chemicals. The identification of molecular targets for specific drugs is a critical step to improve disease therapy. As different diseases have different biomolecular scenarios, we addressed the identification of drug-targeted genes focusing our investigations on cancer and cancer genes. So, a description of resources that provide curated compendiums of human cancer genes is presented. Cancer is a complex disease where multiple genetic changes rewire cellular networks during carcinogenesis. This indicates that cancer drug therapy needs the implementation of network-driven studies to reveal multiplex interactions between cancer genes and drugs. To make progress in this direction, in the last part of this work we provide a bipartite network of cancer genes and their drugs shown in a graph landscape that disclose the existence of specific drug-target modules.
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