Supercomputer Simulations of Dopamine-Derived Ligands Complexed with Cyclooxygenases

Authors

  • Valentina D. Maslova Lomonosov Moscow State University
  • Roman V. Reshetnikov I.M. Sechenov First Moscow State Medical University Faculty of Computer Science, Higher School of Economics
  • Vladimir V. Bezugolov Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences
  • Igor I. Lyubimov Gurus BioPharm, LLC
  • Andrey V. Golovin Lomonosov Moscow State University I.M. Sechenov First Moscow State Medical University Faculty of Computer Science, Higher School of Economics Apto-Pharm LLC

DOI:

https://doi.org/10.14529/jsfi180411

Abstract

An in silico approach was adopted to identify potential cyclooxygenase inhibitors through molecular docking studies. Four potentially active molecules were generated by fusion of dopamine with ibuprofen or ketorolac derivatives. The binding mode of the considered ligands to cyclooxygenase-1 and cyclooxygenase-2 isoforms was described using Autodock Vina. Preliminary docking to full cyclooxygenase isoforms’ structures was used to determine possible binding sites for the described dopamine-derived ligands. The following more accurate docking iteration to the described binding sites was used to achieve better conformational sampling. Among the studied molecules, IBU-GABA-DA showed preferable binding to cyclooxygenase active site of cyclooxygenase-1, while IBU-DA bound to peroxidase site of cyclooxygenase-1, making these ibuprofen-comprising ligands a base for further research and design of selective cyclooxygenase-1 inhibitors. Keterolac-derived ligands KET-DA and KET-GABA-DA demonstrated binding to both cyclooxygenase isoforms at a side pocket, which does not relate to any known functional site of cyclooxygenases and needs to be further investigated.

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Published

2018-12-28

How to Cite

Maslova, V. D., Reshetnikov, R. V., Bezugolov, V. V., Lyubimov, I. I., & Golovin, A. V. (2018). Supercomputer Simulations of Dopamine-Derived Ligands Complexed with Cyclooxygenases. Supercomputing Frontiers and Innovations, 5(4), 98–102. https://doi.org/10.14529/jsfi180411

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