Homology modeling and mutation prediction of ACE2 from COVID-19

Abstract

SARS-CoV-2 has become a pandemic in the world. The virus binds to the Angiotensin-Converting Enzyme 2 (ACE2) receptor, which is found in epithelial cells such as in the lungs, to generate the pathology of COVID-19. It is essential to analyze the characteristics of ACE2 in understanding the development of the disease and study potential new drugs. The analysis was carried out using computer simulations to speed up protein analysis that utilized Artificial Intelligence technology, databases, and big data. Homology modeling is a method to exhibit homologous of protein families, hence the model and arrangement of protein sequences modeled are established. This research aims to determine the possibility of mutations in ACE2 by performing the mutation prediction. The result shows reliable homologous modeling with the score of GA341, MPQS, Z-DOPE, and TSVMod NO35 were 1; 1.28252; -0.47; and 0.793, respectively. Moreover, Gene Ontology (GO) analysis describes that ACE2 has a molecular transport function in cells while there are no mutations found occurred in ACE2 analyzed using SIFT and PROVEAN.