PyPLIF

UNLABELLED: Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary 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 its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα).
AVAILABILITY: PyPLIF is freely available at http://code.google.com/p/pyplif.

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting.

PyPLIF is an open-source Python-based tool for analyzing interaction fingerprinting (IFP), a method for converting 3D protein-ligand interactions into 1D bitstrings to improve the quality of structure-based virtual screening (SBVS). IFP provides an alternative way to model protein-ligand interactions and allows for examination of interaction specificity in addition to approximate affinity provided by docking score. 

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