A comprehensive in silico investigation into the nsSNPs of Drd2 gene predicts significant functional consequences in dopamine signaling and pharmacotherapy

Abstract

DRD2 is a neuronal cell surface protein involved in brain development and function. Variations in the Drd2 gene have clinical significance since DRD2 is a pharmacotherapeutic target for treating psychiatric disorders like ADHD and schizophrenia. Despite numerous studies on the disease association of single nucleotide polymorphisms (SNPs) in the intronic regions, investigation into the coding regions is surprisingly limited. In this study, we aimed at identifying potential functionally and pharmaco-therapeutically deleterious non-synonymous SNPs of Drd2. A wide array of bioinformatics tools was used to evaluate the impact of nsSNPs on protein structure and functionality. Out of 260 nsSNPs retrieved from the dbSNP database, initially 9 were predicted as deleterious by 15 tools. Upon further assessment of their domain association, conservation profile, homology models and inter-atomic interaction, the mutant F389V was considered as the most impactful. In-depth analysis of F389V through Molecular Docking and Dynamics Simulation revealed a decline in affinity for its native agonist dopamine and an increase in affinity for the antipsychotic drug risperidone. Remarkable alterations in binding interactions and stability of the protein-ligand complex in simulated physiological conditions were also noted. These findings will improve our understanding of the consequence of nsSNPs in disease-susceptibility and therapeutic efficacy.

Investigation of pathogenic germline variants in gastric cancer and development of “GasCanBase” database

“… 32 , 33 , 34 Several computational resources have been developed over the years to predict the impact of deleterious nsSNPs in candidate genes based on sequence conservation, 35 resolved or modeled protein structures, 36 physicochemical properties of polypeptides, and so forth. 16 , 37 In silico approaches were undertaken in numerous studies to identify and evaluate the impact of deleterious nsSNPs in a wide range of genes 38 , 39 , 40 , 41 , 42 , 43 , 44 using computational algorithms. 45 , 46 , 47 , 48 , 49 No functional study by computational algorithms has been conducted so far on GC genes.…”