Mining of molecular insights of CYP2A6 and its variants complex with coumarin (CYP2A6*-coumarin) using molecular dynamics simulation

Abstract

CYP2A6 is a very important enzyme that plays a crucial role in nicotine compounds and is responsible for the metabolism of more than 3% drugs of total metabolized drugs by the CYP family and reported as one of very important pharmacogenes. CYP2A6 is highly polymorphic in nature and reported with more than 40 variants, most of these variants are SNPs originated and population specific. It has been well observed and reported that the presence of these population-specific non-synonymous SNPs in CYP2A6 alters the rate of drug metabolism and as a functional consequence, drugs produce an abnormal response. Though genomics and pharmacogenomics studies are there, very less is known about the structural effects of these SNPs on molecular-interaction and folding of CYP2A6. To fill the knowledge gap, SNPs based four variants, i.e., CYP2A6*2, CYP2A6*18, CYP2A6*21, and CYP2A6*35, which are frequently reported in the South Asian population, were considered for the study. Coumarin (DB04665), a well reported drug, is considered as a model substance, and the effect of all four variants on ‘CYP2A6*-coumarin’ complex was studied. MD simulation-based analysis (at 200 ns) was performed and comparative analysis with respect to wild type ‘CYP2A6-coumarin’ complex was done. Though observation didn’t find any global effect on complete complex but found some crucial minor-local alteration in interaction and folding process. It is assumed that the change due to SNPs in the single amino acid did not bring global change in physiochemical properties of CYP2A6* but caused local-trivial changes which are very crucial for its metabolic activity.

Communicated by Ramaswamy H. Sarma

Keywords:

• CYP2A6
• coumarin
• MD simulation
• pharmacogenomics
• pharmacogenes
• South Asian population
• variable drug response
• very important pharmacogenes

Acknowledgment

Contribution of AY is a part of her Ph.D. research work. Authors are thankful to Prof. B. Jayaram for providing the access of his computational resources of Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) to accomplish this research work.

Disclosure statement

All the authors collectively declare that there is no conflict of interest exists.