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 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).AvailabilityPyPLIF is freely available at http://code.google.com/p/pyplif

Increasing the virtual screening quality to identify adrenergic β2 receptor ligands using classification trees

“…The docking poses completed with their ChemPLP scores [26] and PLIF bitstrings [28,29] were obtained from previously published retrospective SBVS campaigns on ADRB2 ligands and decoys [5,18]. This previously published SBVS protocol [18] was used as the reference protocol in this research.…”

“…Istyastono and Setyaningsih [18] has performed SBVS using PLANTS1.2 as the molecular docking software [26,27] and PyPLIF to identify the PLIF bitstrings of each docked pose [28,29]. The constructed SBVS protocol was retrospectively validated using ADRB2 ligands and decoys from DUD-e [5], which consisted of 231 ADRB2 ligands and 15,000 decoys [5,18].…”

Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2

“…The development of methods and computer applications to identify and compare Protein-Ligand Interaction Fingerprints (PLIF) [1][2][3][4][5] and its variances has been of considerable interest since employing such fingerprints was a promising strategy to leverage the wealth of generated data in rational drug design [6]. Together with Structural Interaction Fingerprint (SIFt) and molecular interaction fingerprinting [6], PLIF was categorized as binding site-focused interaction fingerprinting methods [2].…”

“…Mainly for docking purposes, molecular interaction fingerprinting featuring strong and weak hydrogen bonds, ionic and hydrophobic interactions, π-stacking as well as π-cation interactions and metal complexes was developed in 2007 [1][2]. Inspired by this molecular interaction fingerprinting [1], our research group developed a Python implementation of PLIF featuring hydrogen bonds, ionic and hydrophobic interactions and π-stacking under the name PyPLIF [4][5]. Molecular interaction fingerprinting [1] has been expanded recently by encoding the patterns of protein−ligand interactions in fingerprints and graphs that could be applied for post-processing docking poses and search for plausible bioisosteric scaffolds [2][3].…”

“…Binary QSAR approaches, which encode the biological activities as active (1) or inactive/decoy (0) instead of using the actual values [17] were used since several comprehensive studies have reported that there was no correlation between docking scores to biological activity values [27,[32][33]. Virtual screening campaigns to distinguish between potent COX-2 ligands and their decoys [27] by employing molecular docking software PLANTS1.2 [34][35] as the backbone software followed by the PLIF identification software PyPLIF [5] for rescoring the docking results have therefore been performed. The interaction fingerprints and the ChemPLP scores resulted from the SBVS campaigns were subsequently used as descriptors to construct decision trees [19].…”