Homology modeling and mutation prediction of ACE2 from COVID-19

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