High-Throughput Virtual Screening of Small Molecule Modulators Against Viral Proteins

Abstract

Virtual screening of large libraries of small molecules against proteins is a computational approach used in drug discovery and molecular biology to identify potential drug candidates or ligands that can bind to a specific target protein. The goal of this process is to predict the ab initio binding affinities and prioritize molecules that have the potential to interact with the target protein and modulate its activity. This is a crucial step in drug development because it can significantly reduce the time and cost associated with experimental compound screening. In this chapter, we provide an overview of structure-based virtual screening, which involves various steps, including curating small molecule libraries and protein structures from chemical and protein databases, preparing and refining structures, conducting high-throughput and automated binding simulations of ligands on receptor proteins using state-of-the-art docking software, scoring and ranking the binding affinities, and analyzing the results. As an example, we use RNA-dependent RNA polymerase (NS5B) enzyme of the hepatitis C virus (HCV) and demonstrate screening of a spectrum of small molecules available at the PubChem database to identify its potential modulators. In general, this process relies on systematic and data-driven methods that leverage swift identification of potential small molecule modulators for specific viral proteins, expediting drug discovery in pharmaceutical research.

Keywords: Chemical library; High-throughput computation; Protein structure; Software; Virtual screening.