In-silico screening and analysis of missense SNPs in human CYP3A4/5 affecting drug-enzyme interactions of FDA-approved COVID-19 antiviral drugs

Abstract

Single nucleotide polymorphisms (SNPs) represent the prevailing form of genetic variations observed in the human population. Such variations could alter the encoded enzymes' activities. CYP3A4/5 enzymes are involved in metabolizing drugs, notably antivirals against SARS-CoV-2. In this work, we computationally investigated antiviral-enzyme interactions of CYP3A4/5 genetic variants. We also examined the deleterious impact of 751 missense single nucleotide polymorphisms (SNPs) within the CYP3A4/5 genes. An ensemble of bioinformatics tools, [SIFT, PolyPhen-2, cadd, revel, metaLr, mutation assessor, Panther, SNP&GO, PhD-SNP, SNAP, Meta-SNP, FATHMM, I-Mutant, MuPro, INPS, CONSURF, GPS 5.0, MusiteDeep and NetPhos], identified a total of 94 variants (47 SNPs in CYP3A4, 47 SNPs in CYP3A5) to potentially impact the structural integrity as well as the activity of the CYP3A4/5 enzymes. Molecular docking was done to recognize the structural stability and binding properties of the CYP3A4/5 protein isoforms with 3 FDA-approved antiviral drugs. Our findings indicated that the CYP3A4 gene variants; R418T, I335T and R130P and the CYP3A5 gene variants; I335T, L133P and R130Q are considered the most deleterious missense SNPs. These mutants potentially affect drug-enzyme binding and hence may alter therapeutic response. Cataloguing deleterious SNPs is essential for personalized gene-based pharmacotherapy.