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PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting



Muhammad Radifar1, Nunung Yuniarti1, 2 & Enade Perdana Istyastono1, 3, 4*



1Molecular Modeling Center “MOLMOD.ORG”, Yogyakarta, Indonesia; 2Laboratory of Pharmacology and Toxicology, Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Gadjah Mada University, Yogyakarta, Indonesia; 3Center for Environmental Studies, Sanata Dharma University (CESSDU), Yogyakarta, Indonesia; 4Pharmaceutical Technology Laboratory, Faculty of Pharmacy, Sanata Dharma University, Yogyakarta, Indonesia.


Email; *Corresponding author


Article Type




Received March 11, 2013; Accepted March 11, 2013; Published March 19, 2013



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).





Radifar et al. Bioinformation 9(6): 325-328 (2013)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.