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- W2076152612 abstract "Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology." @default.
- W2076152612 created "2016-06-24" @default.
- W2076152612 creator A5023964815 @default.
- W2076152612 creator A5080442426 @default.
- W2076152612 date "2012-10-01" @default.
- W2076152612 modified "2023-10-03" @default.
- W2076152612 title "Identification of SRC as a Potent Drug Target for Asthma, Using an Integrative Approach of Protein Interactome Analysis and <i>In Silico</i> Drug Discovery" @default.
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- W2076152612 doi "https://doi.org/10.1089/omi.2011.0160" @default.
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