PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting

Abstract

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of theactual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interactionfingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examineprotein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is amethod to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings aresubsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. Thesecomparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are eitherproprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm.Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and itsapplication to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα).