Computational predictions suggest that structural similarity in viral polymerases may lead to comparable allosteric binding sites

Abstract

The identification of ligand-binding sites is often the first step in drug targeting and design. To date there are numerous computational tools available to predict ligand binding sites. These tools can guide or mitigate the need for experimental methods to identify binding sites, which often require significant resources and time. Here, we evaluate four ligand-binding site predictor (LBSP) tools for their ability to predict allosteric sites within the Hepatitis C Virus (HCV) polymerase. Our results show that the LISE LBSP is able to identify all three target allosteric sites within the HCV polymerase as well as a known allosteric site in the Coxsackievirus polymerase. LISE was then employed to identify novel binding sites within the polymerases of the Dengue, West Nile, and Foot-and-mouth Disease viruses. Our results suggest that all three viral polymerases have putative sites that share structural or chemical similarities with allosteric pockets of the HCV polymerase. Thus, these binding locations may represent an evolutionarily conserved structural feature of several viral polymerases that could be exploited for the development of small molecule therapeutics.