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Data-mining technique identifies potential target proteins playing a dual role in inflammation and oxidative stress pathways in relation to atherosclerosis plaque development

A. Gurung,P. Borah,A. Bhattacharjee

Published 2020 in Informatics in Medicine Unlocked

ABSTRACT

Abstract Cardiovascular diseases (CVDs) are the leading cause of death worldwide. An underlying source of most CVDs is atherosclerosis. The scarcity of effective drugs, and the side effects of existing marketed drugs, necessitates the discovery of new promising therapeutics and identification of target proteins. CVDs involve several pathway aberrations which lead to onset of disease. Inflammation and oxidative stress are hallmarks. Identification of proteins involved in these pathways and modulation of their function by small molecule inhibitors can help to reduce disease progression. In the present work, we have used the concept of set theory and various statistical scoring functions of R in protein datasets derived from the NCBI Gene database to prioritise protein targets involved in both inflammatory as well as oxidative stress pathways. Subsequently, we have studied the interaction of these targets with small drug-like molecules using a molecular docking approach. Our results show that Serine/Threonine-Protein Kinase (AKT1), Mitogen-Activated Protein Kinase 1 (MAPK1), and Proto-oncogene Tyrosine-Protein Kinase (SRC) are the most promising drug targets in CVDs, as they are involved in both inflammation and oxidative stress pathways and ZINC40196622, ZINC14129555 and ZINC14540970 as their respective lead molecules. The lead compounds produced a Gold Score in the range of 73.79–87.44, and exhibit favourable interaction with the targets through hydrogen bonds as well as hydrophobic interactions. The current findings may greatly facilitate the drug discovery pipeline in proposing suitable target proteins and their modulators for CVDs.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Informatics in Medicine Unlocked

  • Publication date

    Unknown publication date

  • Fields of study

    Biology, Medicine, Chemistry, Environmental Science

  • Identifiers
    DOI 10.1016/j.imu.2019.100278
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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