In-Silico Screening of Deleterious SNPs of Gene U2AF1 for their Role in Lung Cancer

Abstract

Lung cancer is another most common cancer worldwide. Various genes are acknowledged which are responsible for imparting the vulnerability to lung cancer. Amongst those genes, U2AF1 is identified as the major risk factor. U2AF1 is known to cause missense mutations in the several types of cancer together with lung adenocarcinoma (LUAD) and myeloid leukemia (AML). It affects the biological pathway such as DNA methylation, X chromosome inactivation and alters the selected 3` splice site motif. In this study, we present in silico screening and molecular dynamic simulation of lung cancer associated deleterious non-synonymous single nucleotide polymorphisms in U2AF1. We have identified three deleterious coding non-synonymous single nucleotide polymorphisms rs371246226 (Q157R), rs17850009 (G94R), rs371769427 (S34F) in U2AF1 using computational tools SIFT, Polyphen2, PANTHER, SNPs&GO, PhD-SNP, MutPred, SNAP, PROVEAN for the sequence based analysis and tools SNPs&GO3D , PANTHER for the structure based analysis. We have performed molecular dynamics simulations to predict the structural effects of these U2AF1 mutations comparative to the wild-type protein. Results from our simulations demonstrated a comprehensive effect of the mutations that could be able to provide foresight for therapeutic methods in lung cancer.