An in-silico analysis predicting the impact of coding single nucleotide polymorphisms (SNPs) in the human multidrug pump ABCG2

Abstract

ATP-Binding Cassette transporter G2 (ABCG2) is a multidrug resistance pump involved in pathophysiology and pharmacoresistance in various neurological, and neurogenerative disorders. Single nucleotide polymorphisms (SNPs) of ABCG2 affecting expression or function may be involved in the pharmacokinetics of its substrate drugs, influencing the bioavailability leading to disease progression. Thus, pathophysiology and pharmacogenetics linked to ABCG2 can be comprehended by understanding the functions of its SNPs. Identification of these SNPs predictive of pharmacoresistance and disease progression is still a major challenge. Here, we have prioritized ABCG2 SNPs for the identification and validation of deleterious/damaging coding SNPs (cSNPs) using an in-silico approach. Out of 965 cSNPs, only 6 were considered to be highly deleterious by five in-silico computational tools. These cSNPs were submitted to MutPred and PMut for further validation followed by subjected to I-Mutant 2.0 for protein stability analysis. We found 5 cSNPs with a DDG score less than −0.5, suggesting a damaging effect. Based on the conservation of amino acid residues, post-translational modification sites, and structural superimposition, rs3116448 (S248A) and rs41282401 (D296H) were predicted as high-risk cSNPs. Molecular dynamics (MD) simulations revealed that the substitution of the residues S248A in the ABCG2 model altered the structural conformation of the protein and may influence the functional activity as per previously reported in vitro studies. Thus, findings from our study, for the first time, identified clinically important ABCG2 variations utilizing a substantial bioinformatics pipeline that not only provides important potential targets in the field of pharmacogenomics but also curtails study costs.