Computational method in COVID-19: Revelation of preliminary mutations of RdRp of SARS CoV-2 that build new horizons for therapeutic development

Abstract

COVID-19 struck the population with fear of infection with this non treatable disease. This is a consecutive infection of SARS superfamily infection after SARS and MERS infection that was occurred in 2003 and 2014 respectively. WHO named this virus as SARS CoV-2 and the disease caused by this infectious virus was introduced as COVID-19. The virus infects 1,000,889 humans all over the world out of which 210,244 recoveries and 51,371 deaths had been reported till 02nd April 2020. Herein, we are using various computational methods such as EASE-MM, PROVEAN, iSTABLE, STRUM, DUET, SDM, DynaMut and MAESTROweb to identify the effects of protein structure stability of NSP12 protein. We have identified 12-point mutation where the deleterious effect is in "high-confidence". Further analysis of these high-confidence point mutations demonstrates that the mutation in W107, W159 and F636 with Glycine has a highly deleterious effect on the structural stability of NSP12. This analysis provides a detailed understanding of the structural changes of NSP12 and the effect of different point mutations on structural stability variations. As NSP12 gene is an important part of RNA dependent RNA polymerase (RdRp) which is a part of active machinery for translation, a point mutation in the various residue of this gene might provide some crucial information about its functioning. We are dealing with the situation where we urgently required medication or vaccine to combat COVID-19. Therefore, this study for the first time provides essential molecular information about NSP12 gene and its significance in designing new therapeutic against COVID-19.