Docking-Guided 3D-QSAR Studies of 4-Aminoquinoline-1,3,5-triazines as Inhibitors for Plasmodium falciparum Dihydrofolate Reductase

Radite Yogaswara(1), Maria Ludya Pulung(2), Sri Hartati Yuliani(3), Enade Perdana Istyastono(4*)

(1) Department of Chemistry Education, Faculty of Education and Teachers Training, University of Papua, Manokwari 98311, Indonesia
(2) Department of Chemistry, Faculty of Mathematics and Natural Science, University of Papua, Manokwari 98311, Indonesia
(3) Faculty of Pharmacy, Sanata Dharma University, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia
(4) Faculty of Pharmacy, Sanata Dharma University, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia
(*) Corresponding Author


Mutations in Plasmodium falciparum dihydrofolate reductase (PfDHFR), together with other mutations, hinder malaria elimination in Southeast Asia due to multiple drug resistance. In this article, molecular docking-guided three-dimensional (3D) quantitative structure-activity relationship (QSAR) analysis of 4-aminoquinoline-1,3,5-triazines as inhibitors for the wild-type (WT) PfDHFR to identify the molecular determinants of the inhibitors binding are presented. Compounds 4-aminoquinoline-1,3,5-triazines were reported promising to be developed as the non-resistant drugs. The 3D-QSAR analysis resulted in the best model with the R2 and Q2 values of 0.881 and 0.773, respectively. By correlating the molecular interaction fields (MIFs) of the best model to the docking pose employed to guide the 3D-QSAR analysis, S108 residue of the WT-PfDHFR was unfortunately recognized as one of the molecular determinants. Since the S108 residue is one of the mutation points of the PfDHFR mutants, the subsequent design strategy should modify the morpholine moiety to avoid the interaction with the S108 residue of the WT-PfDHFR.


Plasmodium falciparum dihydrofolate reductase; 3D-QSAR; molecular docking; multiple drug resistance

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[1] World Health Organization, 2019, World Malaria Report 2019, Geneva.

[2] Yuvaniyama, J., Chitnumsub, P., Kamchonwongpaisan, S., Vanichtanankul, J., Sirawaraporn, W., Taylor, P., Walkinshaw, M.D., and Yuthavong, Y., 2003, Insights into antifolate resistance from malarial DHFR-TS structures, Nat. Struct. Biol., 10 (5), 357–365.

[3] Vanichtanankul, J., Taweechai, S., Yuvaniyama, J., Vilaivan, T., Chitnumsub, P., Kamchonwongpaisan, S., and Yuthavong, Y., 2011, Trypanosomal dihydrofolate reductase reveals natural antifolate resistance, ACS Chem. Biol., 6 (9), 905–911.

[4] Sunduru, N., Sharma, M., Srivastava, K., Rajakumar, S., Puri, S.K., Saxena, J.K., and Chauhan, P.M.S., 2009, Synthesis of oxalamide and triazine derivatives as a novel class of hybrid 4-aminoquinoline with potent antiplasmodial activity, Bioorg. Med. Chem., 17 (17), 6451–6462.

[5] Istyastono, E.P., Nijmeijer, S., Lim, H.D., van de Stolpe, A., Roumen, L., Kooistra, A.J., Vischer, H.F., de Esch, I.J.P., Leurs, R., and de Graaf, C., 2011, 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., 54 (23), 8136–8147.

[6] Hadni, H., Mazigh, M., Charif, E., Bouayad, A., and Elhallaoui, M., 2018, Molecular modeling of antimalarial agents by 3D-QSAR study and molecular docking of two hybrids 4-Aminoquinoline-1,3,5-triazine and 4-Aminoquinoline-oxalamide derivatives with the receptor protein in its both wild and mutant types, Biochem. Res. Int., 2018, 8639173.

[7] Istyastono, E.P., Yuniarti, N., Hariono, M., Yuliani, S.H., and Riswanto, F.D.O., 2017, Binary quantitative structure-activity relationship analysis in retrospective structure based virtual screening campaigns targeting estrogen receptor alpha, Asian J. Pharm. Clin. Res., 10 (12), 206–211.

[8] 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 (6), 1003–1017.

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

[10] 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.

[11] Therneau, T., Atkinson, B., and Ripley, B., 2015, rpart: Recursive partitioning and regression trees, R package version 4.1-9,

[12] 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.

[13] Riswanto, F.D.O., Hariono, M., Yuliani, S.H., and Istyastono, E.P., 2017, Computer-aided design of chalcone derivatives as lead compounds targeting acetylcholinesterase, Indones. J. Pharm., 28 (2), 100–111.

[14] Yuniarti, N., Mungkasi, S., Yuliani, S.H., and Istyastono, E.P., 2019, Development of a graphical user interface application to identify marginal and potent ligands for estrogen receptor alpha, Indones. J. Chem., 19 (2), 531–537.

[15] Tosco, P., and Balle, T., 2011, Open3DQSAR: A new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields, J. Mol. Model., 17 (1), 201–208.

[16] 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.

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

[18] 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.

[19] 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), 33–47.

[20] Neudert, G., and Klebe, G., 2011, fconv: Format conversion, manipulation and feature computation of molecular data, Bioinformatics, 27 (7), 1021–1022.

[21] Yuan, S., Chan, H.S., and Hu, Z., 2017, Using PyMOL as a platform for computational drug design, WIREs Comput. Mol. Sci., 7 (2), e1298.

[22] Krieger, E., and Vriend, G., 2015, New ways to boost molecular dynamics simulations, J. Comput. Chem., 36 (13), 996–1007.

[23] Liu, K., Watanabe, E., and Kokubo, H., 2017, Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations, J. Comput. Aided Mol. Des., 31 (2), 201–211.


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