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

Enade Perdana Istyastono(1*)

(1) Faculty of Pharmacy, Sanata Dharma University
(*) Corresponding Author


Structure-Based Virtual Screening (SBVS) campaigns employing Protein-Ligand Interaction Fingerprints (PLIF) identification have served as a powerful strategy in fragments and ligands identification, both retro- and prospectively. Most of the SBVS campaigns employed PLIF by comparing them to a reference PLIF to calculate the Tanimoto-coefficient. Since the approach was reference dependent, it could lead to a very different discovery path if a different reference was used. In this article, references independent approach, i.e. decision trees construction using docking score and PLIF as the descriptors to increase the predictive ability of the SBVS campaigns in the identification of ligands for cyclooxygenase-2 is presented. The results showed that the binary Quantitative-Structure Activity Relationship (QSAR) analysis could significantly increase the predictive ability of the SBVS campaign. Moreover, the selected decision tree could also pinpoint the molecular determinants of the ligands binding to cyclooxygenase-2.


Binary QSAR; decision tree; Protein-Ligand Interaction Fingerprints (PLIF); Structure-Based Virtual Screening (SBVS)

Full Text:

Full Text PDF


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

[2] Salentin, S., Haupt, V.J., Daminelli, S., and Schroeder, M., 2014, Polypharmacology rescored: Protein-ligand interaction profiles for remote binding site similarity assessment, Prog. Biophys. Mol. Biol., 116 (2-3), 174–186.

[3] Desaphy, J., Raimbaud, E., Ducrot, P., and Rognan, D., 2013, Encoding protein-ligand interaction patterns in fingerprints and graphs., J. Chem. Inf. Model., 53 (3), 623–637.

[4] Radifar, M., Yuniarti, N., and Istyastono, E.P., 2013, PyPLIF-assisted redocking indomethacin-(R)-alpha-ethyl-ethanolamide into cyclooxygenase-1, Indones. J. Chem., 13 (3), 283–286.

[5] Radifar, M., Yuniarti, N., and Istyastono, E.P., 2013, PyPLIF: Python-based protein-ligand interaction fingerprinting, Bioinformation, 9 (6), 325–328.

[6] Deng, Z., Chuaqui, C., and Singh, J., 2004, Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions, J. Med. Chem., 47 (2), 337–344.

[7] de Graaf, C., and Rognan, D., 2009, Customizing G Protein-coupled receptor models for structure-based virtual screening, Curr. Pharm. Des., 15 (35), 4026–4048.

[8] Rognan, D., 2012, Fragment-based approaches and computer-aided drug discovery, Top. Curr. Chem., 317, 201–222.

[9] Sirci, F., Istyastono, E.P., Vischer, H.F., Kooistra, A.J., Nijmeijer, S., Kuijer, M., Wijtmans, M., Mannhold, R., Leurs, R., de Esch, I.J.P., and de Graaf, C., 2012, Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints, J. Chem. Inf. Model., 52 (12), 3308–3324.

[10] Kooistra, A.J., Leurs, R., de Esch, I.J.P., and de Graaf, C., 2014, From three-dimensional GPCR structure to rational ligand discovery, Adv. Exp. Med. Biol., 796,129–157.

[11] de Graaf, C., Kooistra, A.J., Vischer, H.F., Katritch, V., Kuijer, M., Shiroishi, M., Iwata, S., Shimamura, T., Stevens, R.C., de Esch, I.J.P., and Leurs, R., 2011, Crystal structure-based virtual screening for fragment-like ligands of the human histamine H1 receptor, J. Med. Chem., 54 (23), 8195–8206.

[12] Istyastono, E.P., Kooistra, A.J., Vischer, H., Kuijer, M., Roumen, L., Nijmeijer, S., Smits, R., de Esch, I., Leurs, R., and de Graaf, C., 2015, Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor, Med. Chem. Commun., 6, 1003–1017.

[13] Istyastono, E.P., Riswanto, F.D.O., and Yuliani, S.H., 2015, Computer-aided drug repurposing: a cyclooxygenase-2 inhibitor celecoxib as a ligand for estrogen receptor alpha, Indones. J. Chem. 15 (3), 274–280.

[14] Kooistra, A.J., Leurs, R., de Esch, I.J.P., and de Graaf, C., 2015, Structure-based prediction of G-protein-coupled receptor ligand function: a β-adrenoceptor case study, J. Chem. Inf. Model., 55 (5), 1045–1061.

[15] Deng, Z., Chuaqui, C., and Singh, J., 2006, Knowledge-based design of target-focused libraries using protein-ligand interaction constraints, J. Med. Chem., 49 (2), 490–500.

[16] Cannon, E.O., Amini, A., Bender, A., Sternberg, M.J.E., Muggleton, S.H., Glen, R.C., and Mitchell, J.B.O., 2007, 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., 21, 269–280.

[17] Golbraikh, A., Muratov, E., Fourches, D., and Tropsha, A., 2014, Data set modelability by QSAR, J. Chem. Inf. Model., 54 (1), 1–4.

[18] Istyastono, E.P., and Setyaningsih, D., 2015, Construction and retrospective validation of structure-based virtual screening protocols to identify potent ligands for human adrenergic β2 receptor, Indones. J. Pharm., 26 (1), 20–28.

[19] Therneau, T., Atkinson, B., and Ripley, B., 2015, rpart: Recursive Partitioning and Regression Trees. R package version 4.1-9,

[20] Istyastono, E.P., 2015, 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., 8 (6), 21–24.

[21] Penning, T.D., Talley, J.J., Bertenshaw, S.R., Carter, J.S., Collins, P.W., Docter, S., Graneto, M.J., Lee, L.F., Malecha, J.W., Miyashiro, J.M., Rogers, R.S., Rogier, D.J., Yu, S.S., Anderson, G.D., Burton, E.G., Cogburn, J.N., Gregory, S.A, Koboldt, C.M., Perkins, W.E., Seibert, K., Veenhuizen, A.W., Zhang, Y.Y., and Isakson, P.C., 1997, Synthesis and biological evaluation of the 1,5-diarylpyrazole class of cyclooxygenase-2 inhibitors: identification of 4-[5-(4-methylphenyl)-3-(trifluoromethyl)-1H-pyrazol-1-yl]benze nesulfonamide (SC-58635, celecoxib), J. Med. Chem., 40 (9), 1347–1365.

[22] Chakraborti, A.K., Garg, S.K., Kumar, R., Motiwala, H.F., and Jadhavar, P.S., 2010, Progress in COX-2 inhibitors: a journey so far, Curr. Med. Chem., 17 (15), 1563–1593.

[23] Dai, Z., Ma, X., Kang, H., Gao, J., Min, W., Guan, H., Diao, Y., Lu, W., and Wang, X., 2012, Antitumor activity of the selective cyclooxygenase-2 inhibitor, celecoxib, on breast cancer in vitro and in vivo, Cancer Cell Int., 12 (1), 53.

[24] Cianchi, F., Cortesini, C., Schiavone, N., Perna, F., Magnelli, L., Fanti, E., Bani, D., Messerini, L., Fabbroni, V., Perigli, G., Capaccioli, S., and Masini, E., 2005, The role of cyclooxygenase-2 in mediating the effects of histamine on cell proliferation and vascular endothelial growth factor production in colorectal cancer, Clin. Cancer Res., 11 (19), 6807–6815.

[25] Wang, J.L., Limburg, D., Graneto, M.J., Springer, J., Hamper, J.R.B., Liao, S., Pawlitz, J.L., Kurumbail, R.G., Maziasz, T., Talley, J.J., Kiefer, J.R., and Carter, J., 2010, The novel benzopyran class of selective cyclooxygenase-2 inhibitors. Part 2: the second clinical candidate having a shorter and favorable human half-life, Bioorg. Med. Chem. Lett., 20 (23), 7159–7163.

[26] Kurumbail, R., Stevens, A., and Gierse, J., 1996, Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents, Nature, 384 (6610), 644–648.

[27] Mysinger, M.M., Carchia, M., Irwin, J.J., and Shoichet, B.K., 2012, Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking, J. Med. Chem., 55 (14), 6582–6594.

[28] Huang, N., Shoichet, B.K., and Irwin, J.J., 2006, Benchmarking sets for molecular docking, J. Med. Chem.. 49 (23), 6789–6801.

[29] Guo, C.B., Cai, Z.F., Guo, Z.R., Feng, Z.Q., Chu, F.M., and Cheng, G.F., 2006, Design, synthesis and in vitro evaluation of thiazole derivatives of ibuprofen as cyclooxygenase-2 inhibitors, Chin. Chem. Lett., 17 (3), 325–328.

[30] Yuniarti, N., Nugroho, P.A., Asyhar, A., Sardjiman, S., Ikawati, Z., and Istyastono, E.P., 2012, In vitro and in silico studies on curcumin and its analogues as dual inhibitors for cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2), ITB J. Sci., 44A (1), 51–66.

[31] Yuniarti, N, Ikawati, Z., and Istyastono, E.P., 2011, The importance of ARG513 as a hydrogen bond anchor to discover COX-2 inhibitors in a virtual screening campaign, Bioinformation, 6 (4), 164–166.

[32] Chen, Y., 2015, Beware of docking!, Trends Pharmacol. Sci., 36 (2), 78–95.

[33] Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., and Corbeil, C.R., 2008, Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go, Br. J. Pharmacol., 153 (Suppl. 1), S7–S26.

[34] Korb, O., Stützle, T., and Exner, T.E., 2009, Empirical scoring functions for advanced protein-ligand docking with PLANTS, J. Chem. Inf. Model., 49 (1), 84–96.

[35] Korb, O., Stützle, T., and Exner, T.E., 2007, An ant colony optimization approach to flexible protein–ligand docking, Swarm Intell., 1 (2), 115–134.

[36] Istyastono, E.P., 2016, Optimizing structure-based virtual screening protocol to identify phytochemicals as cyclooxygenase-2 inhibitors, Indones. J. Pharm., 27 (3), 163–173.

[37] ten Brink, T., and Exner, T.E., 2009, Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results, J. Chem. Inf. Model., 49 (6), 1535-1546.

[38] O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., and Hutchison, G.R., 2011, Open Babel: An open chemical toolbox, J. Cheminf., 3 (1), 3347.

[39] Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., The R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., and Scrucca, L., 2015 caret: Classification and Regression Training, R package version 6.0-52,

[40] R Core Team, 2016, R: A Language and Environment for Statistical Computing, Vienna,

[41] Setiawati, A., Riswanto, F.D.O., Yuliani, S.H., and Istyastono, E.P., 2014, 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, Indones. J. Chem., 14 (2), 103–108.

[42] Zarghi, A., Ghodsi, R., Azizi, E., Daraie, B., Hedayati, M., and Dadrass, O.G., 2009, Synthesis and biological evaluation of new 4-carboxyl quinoline derivatives as cyclooxygenase-2 inhibitors, Bioorg. Med. Chem., 17 (14), 5312–5317.

[43] Kozak, K.R., Prusakiewicz, J.J., Rowlinson, S.W., Schneider, C., and Marnett, L.J., 2001, Amino acid determinants in cyclooxygenase-2 oxygenation of the endocannabinoid 2-arachidonylglycerol, J. Biol. Chem., 276 (32), 30072–30077.

[44] Wang, J.L., Carter, J., Kiefer, J.R., Kurumbail, R.G., Pawlitz, J.L., Brown, D., Hartmann, S.J., Graneto, M.J., Seibert, K., and Talley, J.J., 2010, The novel benzopyran class of selective cyclooxygenase-2 inhibitors-part I: The first clinical candidate, Bioorg. Med. Chem. Lett., 20 (23), 7155–7158.

[45] Rao, P.N.P., Chen, Q., and Knaus, E.E., 2006, Synthesis and structure-activity relationship studies of 1,3-diarylprop-2-yn-1-ones: Dual inhibitors of cyclooxygenases and lipoxygenases, J. Med. Chem., 49 (5), 1668–1683.

[46] Rao, P., and Knaus, E.E., 2008, Evolution of nonsteroidal anti-inflammatory drugs (NSAIDs): cyclooxygenase (COX) inhibition and beyond, J. Pharm. Pharm. Sci., 11 (2), 81s–110s.

[47] Krüger, D.M., and Evers, A., 2010, Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors, ChemMedChem, 5 (1), 148-158.


Article Metrics

Abstract views : 2464 | views : 2157

Copyright (c) 2017 Indonesian Journal of Chemistry

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


Indonesian Journal of Chemistry (ISSN 1411-9420 / 2460-1578) - Chemistry Department, Universitas Gadjah Mada, Indonesia.

Analytics View The Statistics of Indones. J. Chem.