Abstract
Objective: The objective of this study is to construct predictive unbiased structure-based virtual screening (SBVS) protocols to identify potent ligands for estrogen receptor alpha by combining molecular docking, protein-ligand interaction fingerprinting (PLIF), and binary quantitative structure-activity relationship (QSAR) analysis using recursive partition and regression tree method.Methods: Employing the enhanced version of a directory of useful decoys, SBVS protocols using molecular docking simulations, and PLIF were constructed and retrospectively validated. To avoid bias, SMILES format of the compounds was used. The predictive abilities of the SBVS protocols were then compared based on the enrichment factor (EF) and the F-measure values.Results: The SBVS protocols resulted in this research were SBVS_1 (employing docking scores of the best pose on every compound to rank the results and selecting compounds within 1% false positives as positive), SBVS_2 (employing decision tree resulted from the binary QSAR analysis using docking scores and PLIF bitstrings of the best pose of every compound as descriptors), and SBVS_3 (employing decision tree resulted from the binary QSAR analysis using ensemble PLIF of the selected poses from optimized docking score as the cutoff). The EF values of SBVS_1, SBVS_2, and SBVS_3 are 28.315, 576.084, and 713.472, respectively, while their F-measure values are 0.310, 0.573, and 0.769, respectively.Conclusion: Highly predictive unbiased SBVS protocols to identify potent estrogen receptor alpha ligands were constructed. Further application in prospective screening is therefore highly suggested.
Highlights
Molecular interaction fingerprints (IFP) resulted from converting protein-ligand complexes into IFP bitstring were introduced in 2007 by Marcou and Rognan [1]
SBVS_1 used ChemPLP score of the best pose of each screened compound to rank both ligands and decoys in the retrospective virtual screening, and the ChemPLP score of 1% false positives (FP) was used as the cutoff value in the ranked results to predict compounds as P [14,23]
Reported, using the best decision tree resulted from recursive partition and regression tree method (RPART) method, SBVS_rpart [26] could increase significantly the predictive ability of SBVS_chemplp [14], which represents commonly used docking score to rank the results in structure-based virtual screening (SBVS) campaigns [23,35]
Summary
Molecular interaction fingerprints (IFP) resulted from converting protein-ligand complexes into IFP bitstring were introduced in 2007 by Marcou and Rognan [1]. The IFP which is known as the proteinligand IFP (PLIF) has been successfully employed mainly in fragmentbased drug discovery projects [1,2,3,4,5,6]. Inspired from IFP of Marcou and Rognan, an open-source Python implementation of the molecular IFP named PyPLIF was developed [7,8]. Different with the molecular IFP of Marcou and Rognan, PyPLIF uses non-proprietary Open Babel [9] library. Anyone can freely use, modify, and even develop PyPLIF depending on their purposes [7,10,11]. Since the original host of PyPLIF https://code.google.com/ [7] was shut down by Google, PyPLIF was relocated to GitHub (https://github.com/radifar/pyplif)
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