Elucidating the Mutational Landscape in Hepatocyte Nuclear Factor 1β (HNF1B) by Computational Approach.

Transcription factors are the major gene-regulatory proteins that recognize specific nucleotide sequences and bind to them. Missense mutations in transcription factors play a significant role in misregulation of gene expression contributing to various diseases and disorders. Understanding their structural and functional impact of the disease-causing mutations becomes prime importance in treating a disease. Commonly associated defect with the mutations of hepatocyte nuclear factor 1 beta (HNF1B) protein, a transcription factor results in maturity-onset diabetes of the young-5 (MODY-5) leading to loss of function. In the study presented, we applied a series of computational strategies to analyze the effect of mutations on protein structure or function in protein-DNA complex. The mutations from publicly available databases were retrieved and subjected to an array of in silico prediction methods. Key implementation of the present study consists of a pipeline drawn using well established in silico prediction methods of different algorithms to explain the biochemical changes impaired upon mutations in the binding sites of protein-DNA complex using HNF1B. Prediction scores obtained from the in silico tools suggested H153N and A241T as the major nsSNPs involved in destabilizing the protein. Further, high-end microscopic computational study, such as molecular dynamics simulations was utilized to relate the structural and functional effects upon mutations. Although, both the mutations exhibited similar structural differences, we observed A241T with higher destabilizing effect on the protein. The presented work is a step toward understanding the genotype-phenotype relationships in transcription factor genes using fast and accurate computational approach.