Genotype-protein phenotype characterization of NOD2 and IL23R missense variants associated with inflammatory bowel disease: A paradigm from molecular modelling, dynamics, and docking simulations

Inflammatory bowel disease (IBD) is a gastrointestinal disease with an underlying contribution of genetic, microbial, environment, immunity factors. The coding region risk markers identified by IBD genome wide association studies have not been well characterized at protein phenotype level. Therefore, this study is conducted to characterize the role of NOD2 (Arg675Trp and Gly908Arg) and IL23R (Gly149Arg and Arg381Gln) missense variants on the structural and functional features of corresponding proteins. Thus, we used different variant pathogenicity assays, molecular modelling, secondary structure, stability, molecular dynamics, and molecular docking analysis methods. Our findings suggest that SIFT, Polyphen, GREP++, PhyloP, SiPhy and REVEL methods are very sensitive in determining pathogenicity of NOD2 and IL23R missense variants. We have also noticed that all the tested missense variants could potentially alter secondary (α-helices, β-strands, and coils) and tertiary (residue level deviations) structural features. Moreover, our molecular dynamics (MD) simulation findings have simulated that NOD2 (Arg675Trp and Gly908Arg) and IL23R (Gly149Arg and Arg381Gln) variants creates rigid local structures comprising the protein flexibility and conformations. These predictions are corroborated by molecular docking results, where we noticed that NOD2 and IL23R missense variants induce molecular interaction deformities with RIPK2 and JAK2 ligand molecules, respectively. These functional alterations could potentially alter the signal transduction pathway cascade involved in inflammation and autoimmunity. Drug library searches and findings from docking studies have identified the inhibitory effects of Tacrolimus and Celecoxib drugs on NOD2 and IL23R variant forms, underlining their potential to contribute to personalized medicine for IBD. The present study supports the utilization of computational methods as primary filters (pre-in vitro and in vivo) in studying the disease potential mutations in the context of genptype-protein phenotype characteristics.