Computational drug discovery of an inhibitor of APOBEC3B as a treatment for epithelial cancers

Abstract

Cancer is one of the leading causes of death in the U.S., and tumorous cancers such as cervical, lung, breast, and ovarian cancers are the most common types. APOBEC3B is a nonessential cytidine deaminase found in humans and theorized to defend against viral infection. However, overexpression of APOBEC3B is linked to cancer in humans, which makes APOBEC3B a potential cancer treatment target through competitive inhibition for several tumorous cancers. Computational studies can help reveal a small molecule inhibitor using high-throughput virtual screening of millions of candidates with relatively little cost. This study aims to narrow the field of potential APOBEC3B inhibition candidates for future in vitro assays and provide an effective scaffold for drug design studies. Another goal of this project is to provide critical amino acid targets in the active site for future drug design studies. This study simulated 7.8 million drug candidates using high-throughput virtual screening and further processed the top scoring 241 molecules from AutoDock Vina, DOCK 6, and de novo design. Using virtual screening, de novo design, and molecular dynamics simulations, a competitive inhibitor candidate was discovered with an average binding free energy score of −46.03 kcal/mol, more than 10 kcal/mol better than the substrate control (dCMP). These results indicate that this molecule (or a structural derivative) may be an effective inhibitor of APOBEC3B and prevent host genome mutagenesis resulting from protein overexpression. Another important finding is the confirmation of essential amino acid targets, such as Tyr250 and Gln213 within the active site of APOBEC3B. Therefore, study used novel computational methods to provide a theoretical scaffold for future drug design studies that may prove useful as a treatment for epithelial cancers. Communicated by Ramaswamy H. Sarma