Structural and functional analysis of KIT gene encoding receptor tyrosine kinase and its interaction with sunitinib and HDAC inhibitors: An in silico approach

Abstract

KIT is a growth factor receptor, important for normal germ cell migration and development. The malfunction of KIT gene results in constitutive activation of the tyrosine kinase activity of c-KIT which is believed to be the major oncogenic event in stomach, small intestine mastocytosis, acute leukemias, melanomas and colon tumors. The genetics of these diseases could be better understood by knowing the functional relevance of their SNP variation. In this study, a computational analysis to detect the most deleterious non-synonymous SNPs of KIT gene was performed and investigated its binding affinity to native and predicted mutant protein structure (D816V) with sunitinib and HDAC (Trichostatin A and Panobinostat) inhibitors was investigated. Out of 1,288 SNPs retrieved from dbSNP database against KIT gene, 11 non-synonymous SNPs were detected to be damaging and deleterious by SIFT, PolyPhen andI-Mutant2.0 servers. Further, we modeled the mutant protein based on the deleterious nsSNP (rsl 21913507) and showed that the mutation from Aspartic acid to Valine at 816 position exhibit greatest impact on stability. The RMSD values of mutant and native structures are found to be 0.40 and 1.9 Å, respectively. Furthermore, the binding affinity of sunitinib and HDAC inhibitors were compared with native and mutant protein. In this regard, it was found that trichostatin A has a high binding efficacy towards the mutant protein with a binding energy of -35.274 kcal mol-1, as compared to the native structure which has a binding energy of -25.996 kcal mol-1. Also, the FastSNP tool suggested that 3 SNPs found to affect protein splicing site and splicing regulation. From present results, it was clear that the non-synonymous SNP rsl21913507 (D816V) could be the most deleterious SNP for KIT gene and HDAC inhibitors can serve as a best drug for the mutant protein. © 2012 Asian Network for Scientific Information.