Enade Perdana Istyastono, Nunung Yuniarti, Maywan Hariono, Sri Hartati Yuliani, Florentinus Dika Octa Riswanto



 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.


Estrogen receptor alpha, Structure-based virtual screening, Recursive partition and regression tree, Molecular docking, Protein-ligand interaction fingerprinting.

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Marcou G, Rognan D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 2007;47(1):195-207.

Desaphy J, Raimbaud E, Ducrot P. Rognan encoding protein-ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 2013;53(3):623-37.

Istyastono EP, Kooistra AJ, Vischer H, Kuijer M, Roumen L, Nijmeijer S, et al. Structure-based virtual screening for fragment-like ligands of the g protein-coupled histamine H4 receptor. Med Chem Commun 2015;6:1003-17.

Sirci F, Istyastono EP, Vischer HF, Kooistra AJ, Nijmeijer S, Kuijer M, et al. Virtual fragment screening: Discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. J Chem Inf Model 2012;52(12):3308-24.

de Graaf C, Kooistra AJ, Vischer HF, Katritch V, Kuijer M, Shiroishi M, et al. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. J Med Chem 2011;54(23):8195-206.

Siegal G, Ab E, Schultz J. Integration of fragment screening and library design. Drug Discov Today 2007;12(23-24):1032-9.

Radifar M, Yuniarti N, Istyastono EP. PyPLIF: Python-based protein-ligand interaction fingerprinting. Bioinformation 2013;9(6):325-8.

Radifar M, Yuniarti N, Istyastono EP. PyPLIF-assisted redocking indomethacin-(R)-alpha-ethyl-ethanolamide into cyclooxygenase-1. Indones J Chem 2013;13:283-6.

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011;3:33.

Salentin S, Haupt VJ, Daminelli S, Schroeder M. Polypharmacology rescored: Protein-ligand interaction profiles for remote binding site similarity assessment. Prog Biophys Mol Biol 2014;116(2-3):174-86.

Zhao Z, Xie L, Xie L, Bourne PE. Delineation of polypharmacology across the human structural kinome using a functional site interaction fingerprint approach. J Med Chem 2016;59(9):4326-41.

Rognan D. Fragment-based approaches and computer-aided drug discovery. Top Curr Chem 2012;317:201-22.

Istyastono EP, Riswanto FD, Yuliani SH. Computer-aided drug repurposing: A cyclooxygenase-2 inhibitor celecoxib as a ligand for estrogen receptor alpha. Indones J Chem 2015;15:274-80.

Setiawati A, Riswanto FD, Yuliani SH, Istyastono EP. Retrospective validation of a structure-based virtual screening protocol to identify ligands for estrogen receptor alpha and its application to identify the alpha-mangostin binding pose. Indo J Chem 2014;14:103-8.

Istyastono EP, Nurrochmad A, Yuniarti N. Structure-based virtual screening campaigns on curcuminoids as potent ligands for histone deacetylase-2. Orient J Chem 2016;32:275-82.

Kooistra AJ, Leurs R, de Esch IJ, de Graaf C. Structure-based prediction of G-protein-coupled receptor ligand function: A ß-adrenoceptor case study. J Chem Inf Model 2015;55(5):1045-61.

Koshland DE. The key-lock theory and the induced fit theory. Angew Chem Int Ed Engl 1994;33:2375-8.

Stoddard BL, Koshland DE Jr. Prediction of the structure of a receptor-protein complex using a binary docking method. Nature


Istyastono EP, Nijmeijer S, Lim HD, van de Stolpe A, Roumen L, Kooistra AJ, et al. Molecular determinants of ligand binding modes in the histamine H4 receptor: Linking ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) models to in silico guided receptor mutagenesis studies. J Med Chem 2011;54:8136-47.

Wang A, Stout CD, Zhang Q, Johnson EF. Contributions of ionic interactions and protein dynamics to cytochrome P450 2D6 (CYP2D6) substrate and inhibitor binding. J Biol Chem 2015;290(8):5092-104.

Korb O, Stützle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 2009;49(1):84-96.

Ten Brink T, Exner TE. Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. J Chem Inf Model 2009;49(6):1535-46.

Mysinger MM, Carchia M, Irwin JJ, Shoichet BK. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J Med Chem 2012;55(14):6582-94.

Therneau T, Atkinson B, Ripley B. Rpart: Recursive Partitioning and Regression Trees, R Package Version 4.1-9; 2015. Available from: http://www.CRAN.R–

Istyastono EP, Yuniarti N. Construction of three dimensional structures of phytoestrogens converted from smiles string representations for simulations using PLANTS docking software. Tradit Med J 2016;21:69-76.

Istyastono EP. Employing recursive partition and regression tree method to increase the quality of structure-based virtual screening in the estrogen receptor alpha ligands identification. Asian J Pharm Clin Res 2015;8:21-4.

Korb O, Stützle T, Exner TE. An ant colony optimization approach to flexible protein-ligand docking. Proc IEEE Swarm Intell Symp 2007;1:115-34.

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2016. Available from: http://www.R–

Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, et al. Caret: Classification and Regression Training. R Package Version 6.0-52; 2015. Available from: http://www.CRAN.R–

Anita Y, Radifar M, Kardono LB, Hanafi M, Istyastono EP. Structure-based design of eugenol analogs as potential estrogen receptor antagonists. Bioinformation 2012;8(19):901-6.

Cannon EO, Amini A, Bender A, Sternberg MJ, Muggleton SH, Glen RC, et al. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des 2007;21(5):269-80.

Smits RA, Adami M, Istyastono EP, Zuiderveld OP, van Dam CM, de Kanter FJ, et al. Synthesis and QSAR of quinazoline sulfonamides as highly potent human histamine H4 receptor inverse agonists. J Med Chem 2010;53(6):2390-400.

Lim HD, Istyastono EP, van de Stolpe A, Romeo G, Gobbi S, Schepers M, et al. Clobenpropit analogs as dual activity ligands for the histamine H3 and H4 receptors: Synthesis, pharmacological evaluation, and cross-target QSAR studies. Bioorg Med Chem 2009;17(11):3987-94.

Cappel D, Dixon SL, Sherman W, Duan J. Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling. J Comput Aided Mol Des 2015;29(2):165-82.

Kooistra AJ, Leurs R, de Esch IJ, de Graaf C. From three-dimensional GPCR structure to rational ligand discovery. Adv Exp Med Biol 2014;796:129-57.

Istyastono EP. Optimizing structure-based virtual screening protocol to identify phytochemicals as cyclooxygenase-2 inhibitors. Indones J Pharm 2016;27:163-73.

Gabel J, Desaphy J, Rognan D. Beware of machine learning-based scoring functions on the danger of developing black boxes. J Chem Inf Model 2014;54(10):2807-15.

Shiau AK, Barstad D, Loria PM, Cheng L, Kushner PJ, Agard DA, et al. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 1998;95(7):927-37.

Desta Z, Ward BA, Soukhova NV, Flockhart DA. Comprehensive evaluation of tamoxifen sequential biotransformation by the human cytochrome P450 system in vitro: Prominent roles for CYP3A and CYP2D6. J Pharmacol Exp Ther 2004;310(3):1062-75.

Setiawati A, Riswanto FO, Yuliani SH, Istyastono EP. Anticancer activity of mangosteen pericarp dry extract against MCF-7 breast cancer cell line though estrogen receptor-α. Indones J Pharm 2014;25:119-24.

de Kloe GE, Bailey D, Leurs R, de Esch IJ. Transforming fragments into candidates: Small becomes big in medicinal chemistry. Drug Discov Today 2009;14(13-14):630-46.

Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: A free tool to discover chemistry for biology. J Chem Inf Model 2012;52(7):1757-68.

Sterling T, Irwin JJ. ZINC 15-ligand discovery for everyone. J Chem Inf Model 2015;55(11):2324-37.

Irwin JJ, Shoichet BK. ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005;45(1):177-82.

Kolb P, Rosenbaum DM, Irwin JJ, Fung JJ, Kobilka BK, Shoichet BK. Structure-based discovery of beta2-adrenergic receptor ligands. Proc Natl Acad Sci U S A 2009;106(16):6843-8.

Andrews SP, Brown GA, Christopher JA. Structure-based and fragment-based GPCR drug discovery. Chem Med Chem 2014;9(2):256-75.

Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov 2015;14(2):111-29.

Elgadir MA, Salama M, Adam A. Anti-breast cancer from various natural sources, review. Int J Pharm Pharm Sci 2015;7:44-7.

Riswanto FD, Hariono M, Yuliani SH, Istyastono EP. Computer-aided design of chalcone derivatives as lead compounds targeting acetylcholinesterase. Indonesian J Pharm 2017;28:100-11.

Arora S, Agarwal S, Singhal S. Anticancer activities of thiosemicarbazides/thiosemicarbazones: A review. Int J Pharm Pharm Sci 2014;6:34-41.

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Pharmaceutical Sciences


Estrogen receptor alpha, Structure-based virtual screening, Recursive partition and regression tree, Molecular docking, Protein-ligand interaction fingerprinting.





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Asian Journal of Pharmaceutical and Clinical Research
Vol 10 Issue 12 December 2017 Page: 206-211

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Authors & Affiliations

Enade Perdana Istyastono
Faculty of Pharmacy Sanata Dharma University

Nunung Yuniarti
Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada

Maywan Hariono
Faculty of Pharmacy Sanata Dharma University

Sri Hartati Yuliani
Faculty of Pharmacy Sanata Dharma University

Florentinus Dika Octa Riswanto
Faculty of Pharmacy Sanata Dharma University

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