Research Article
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Year 2025, Volume: 29 Issue: 2, 790 - 805
https://doi.org/10.12991/jrespharm.1666359

Abstract

References

  • [1] Rosenthal PJ. Malaria in 2022: Challenges and Progress. Am J Trop Med Hyg. 2022; 106(6): 1565–1567. https://doi.org/10.4269/AJTMH.22-0128
  • [2] World Health Organization. World Malaria Report 2022.
  • https://www.mmv.org/newsroom/news-resources-search/world-malaria-report-2022?gclid=CjwKCAiArfauBhApEiwAeoB7qA6Q9Va01OF0B6gGWENAb9rsmOPmSPlRmH6lzSGoDI6_aR_otcoEaxoC3G4QAvD_BwE (accessed November 15, 2023).
  • [3] Shibeshi MA, Kifle ZD, Atnafie SA. Antimalarial drug resistance and novel targets for antimalarial drug discovery. Infect Drug Resist. 2020; 13: 4047–4060. https://doi.org/10.2147/IDR.S279433
  • [4] Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem. 2021; 224: 113705. https://doi.org/10.1016/J.EJMECH.2021.113705
  • [5] Surabhi S, Singh B. Computer Aided Drug Design: An Overview. J Drug Deliv Ther. 2018; 8(5): 504–509. https://doi.org/10.22270/JDDT.V8I5.1894
  • [6] Muhammed MT, Aki-Yalcin E. Molecular docking: Principles, advances, and ıts applications in drug discovery. Lett Drug Des Discov. 2022; 21(3): 480–495. https://doi.org/10.2174/1570180819666220922103109
  • [7] Singh IV, Mishra S. Molecular docking analysis of pyrimethamine derivatives with Plasmodium falciparum dihydrofolate reductase. Bioinformation. 2018; 14(05): 232–235. https://doi.org/10.6026/97320630014232
  • [8] Hodoameda P. P. falciparum and ıts molecular markers of resistance to antimalarial drugs. In: Tyagi RK. (Eds). Plasmodium Species and Drug Resistance. IntechOpen, Inc., United Kingdonm, 2021, pp.1-21.
  • [9] Kreutzfeld O, Tumwebaze PK, Byaruhanga O, Katairo T, Okitwi M, Orena S, Rasmussen SA, Legac J, Conrad MD, Nsobya SL, Aydemir O, Bailey JA, Duffey M, Cooper RA, Rosenthal PJ. Decreased susceptibility to dihydrofolate reductase ınhibitors associated with genetic polymorphisms in Ugandan Plasmodium falciparum ısolates. J Infect Dis. 2022; 225(4): 696–704. https://doi.org/10.1093/INFDIS/JIAB435
  • [10] Posayapisit N, Pengon J, Prommana P, Shoram M, Yuthavong Y, Uthaipibull C, Kamchonwongpaisan S, Jupatanakul N. Transgenic pyrimethamine-resistant Plasmodium falciparum reveals transmission-blocking potency of P218, a novel antifolate candidate drug. Int J Parasitol. 2021; 51(8): 635–642. https://doi.org/10.1016/J.IJPARA.2020.12.002
  • [11] Rastelli G, Sirawaraporn W, Sompornpisut P, Vilaivan T, Kamchonwongpaisan S, Quarrell R, Lowe G, Thebtaranonth Y, Yuthavong Y. Interaction of pyrimethamine, cycloguanil, WR99210 and their analogues with Plasmodium falciparum dihydrofolate reductase: Structural basis of antifolate resistance. Bioorg Med Chem. 2000; 8(5): 1117–1128. https://doi.org/10.1016/S0968-0896(00)00022-5
  • [12] Tarnchompoo B, Chitnumsub P, Jaruwat A, Shaw PJ, Vanichtanankul J, Poen S, Rattanajak R, Wongsombat C, Tonsomboon A, Decharuangsilp S, Anukunwithaya T, Arwon U, Kamchonwongpaisan S, Yuthavong Y. Hybrid ınhibitors of malarial dihydrofolate reductase with dual binding modes that can forestall resistance. ACS Med Chem Lett. 2018; 9(12): 1235–1240. https://doi.org/10.1021/acsmedchemlett.8b00389
  • [13] Borkakoty B, Sarma K, Parida P, Prakash A, Mohapatra P, Mahanta J. In silico screening of antifolate based novel ınhibitors from Brucea mollis Wall. ex kurz against quadruple mutant drug resistant PfDHFR. Comb Chem High Throughput Screen. 2014; 17(8): 681–693. https://doi.org/10.2174/1386207317666140521153305
  • [14] Da Silva RSFL, Olanda CG, Fokoue HH, Sant’Anna CMR. Virtual screening techniques in drug discovery: Review and recent applications. Curr Top Med Chem. 2019; 19(19): 1751–1767. https://doi.org/10.2174/1568026619666190816101948
  • [15] Wang Z, Sun H, Shen C, Hu X, Gao J, Li D, Cao D, Hou T. Combined strategies in structure-based virtual screening. Phys Chem Chem Phys. 2020; 22(6): 3149–3159. https://doi.org/10.1039/C9CP06303J
  • [16] Kamchonwongpaisan S, Charoensetakul N, Srisuwannaket C, Taweechai S, Rattanajak R, Vanichtanankul J, Vitsupakorn D, Arwon U, Thongpanchang C, Tarnchompoo B, Vilaivan T, Yuthavong Y. Flexible diaminodihydrotriazine inhibitors of Plasmodium falciparum dihydrofolate reductase: Binding strengths, modes of binding and their antimalarial activities. Eur J Med Chem. 2020;195: 112263. https://doi.org/10.1016/J.EJMECH.2020.112263
  • [17] Somandi K, Seanego TD, Dlamini T, Maree M, de Koning CB, Vanichtanankul J, Rattanajak R, Saeyang T, Yuthavong Y, Kamchonwongpaisan S, Rousseau AL. Molecular docking studies, synthesis and biological evaluation of substituted pyrimidine-2,4-diamines as ınhibitors of Plasmodium falciparum dihydrofolate reductase. ChemMedChem. 2022; 17(22): e202200418. https://doi.org/10.1002/CMDC.202200418
  • [18] Opo FADM, Rahman MM, Ahammad F, Ahmed I, Bhuiyan MA, Asiri AM. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci Rep. 2021; 18(11): 4049. https://doi.org/10.1038/s41598-021-83626-x
  • [19] Kyei LK, Gasu EN, Ampomah GB, Mensah JO, Borquaye LS. An in silico study of the ınteractions of alkaloids from Cryptolepis sanguinolenta with Plasmodium falciparum dihydrofolate reductase and dihydroorotate dehydrogenase. J Chem. 2022; 2022: 1–26. https://doi.org/10.1155/2022/5314179
  • [20] Durán-Iturbide NA, Díaz-Eufracio BI, Medina-Franco JL. In silico ADME/Tox profiling of natural products: A focus on BIOFACQUIM. ACS Omega. 2020; 5(26): 16076–1684. https://doi.org/10.1021/acsomega.0c01581
  • [21] Azzam KM Al. SwissADME and pkCSM Webservers Predictors: an integrated online platform for accurate and comprehensive predictions for ın silico ADMET properties of artemisinin and its derivatives. Complex Use Miner Resour. 2023; 325(2): 14–21. https://doi.org/10.31643/2023/6445.13
  • [22] Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using Graph-Based Signatures. J Med Chem. 2015; 58(9): 4066–4072. https://pubs.acs.org/doi/10.1021/acs.jmedchem.5b00104
  • [23] Purwanto BT, Hardjono S, Widiandani T, Nasyanka AL, Siswanto I. In silico study and ADMET prediction of N-(4-fluorophenylcarbamothioyl)benzamide derivatives as cytotoxic agents. J Hunan Univ Sci. 2021; 48(2): 78–85.
  • [24] Yeni Y, Rachmania RA. The prediction of pharmacokinetic properties of compounds in Hemigraphis alternata (Burm.F.) T. Ander leaves using pkCSM. Indones J Chem. 2022; (4): 1081–1089. https://doi.org/10.22146/ijc.73117
  • [25] Walters WP. Going further than Lipinski’s Rule in drug design. Expert Opin Drug Discov. 2012; 7(2): 99–107. https://doi.org/10.1517/17460441.2012.648612
  • [26] Jia CY, Li JY, Hao GF, Yang GF. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov Today. 2020; 25(1): 248–258. https://doi.org/10.1016/j.drudis.2019.10.014
  • [27] Taidi L, Maurady A, Britel MR. Molecular docking study and molecular dynamic simulation of human cyclooxygenase-2 (COX-2) with selected eutypoids. J Biomol Struct Dyn. 2022; 40(3): 1189–1204. https://doi.org/10.1080/07391102.2020.1823884
  • [28] Mosquera-Yuqui F, Lopez-Guerra N, Moncayo-Palacio EA. Targeting the 3CLpro and RdRp of SARS-CoV-2 with phytochemicals from medicinal plants of The Andean Region: Molecular docking and molecular dynamics simulations. J Biomol Struct Dyn. 2022; 40(5): 2010–2023. https://doi.org/10.1080/07391102.2020.1835716
  • [29] Menéndez CA, Accordino SR, Gerbino DC, Appignanesi GA. Hydrogen bond dynamic propensity studies for protein binding and drug design. PLoS One. 2016;11(10):e0165767. https://doi.org/10.1371/journal.pone.0165767
  • [30] El-Shamy NT, Alkoud AM, Hussein RK, Ibrahim MA, Alhamzani AG, Abou-Krisha MM. DFT, ADMET and molecular docking ınvestigations for the antimicrobial activity of 6,6’-Diamino-1,1’,3,3’-tetramethyl-5,5’-(4-chlorobenzylidene)bis[pyrimidine-2,4(1H,3H)-dione]. Molecules. 2022; 27(3): 620. https://doi.org/10.3390/molecules27030620
  • [31] Tsuneda T, Song JW, Suzuki S, Hirao K. On Koopmans’ Theorem in Density Functional Theory. J Chem Phys. 2010;133(17):174101. https://doi.org/10.1063/1.3491272
  • [32] Giordano D, Biancaniello C, Argenio MA, Facchiano A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals (Basel). 2022;15(5):646. https://doi.org/10.3390/ph15050646
  • [33] Imrie F, Bradley AR, Deane CM. Generating property-matched decoy molecules using deep learning. Bioinformatics. 2021; 37(15): 2134–2141. https://dx.doi.org/10.1093/bioinformatics/btab080
  • [34] Heinzke AL, Zdrazil B, Leeson PD, Young RJ, Pahl A, Waldmann H, Leach AR. A compound-target pairs dataset: differences between drugs, clinical candidates and other bioactive compounds. Sci Data. 2024;11(1):1160. https://doi.org/10.1038/s41597-024-03582-9.
  • [35] Naz S, Farooq U, Khan S, Sarwar R, Mabkhot YN, Saeed M, Alsayari A, Muhsinah AB, Ul-Haq Z. Pharmacophore model-based virtual screening, docking, biological evaluation and molecular dynamics simulations for inhibitors discovery against α-tryptophan synthase from Mycobacterium tuberculosis. J Biomol Struct Dyn. 2021;39(2):610-620. https://doi.org/10.1080/07391102.2020.1715259
  • [36] Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key topics in molecular docking for drug design. Int J Mol Sci. 2019;20(18):4574. https://doi.org/10.3390/IJMS20184574
  • [37] Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: Fast, Flexible, and free. J Comput Chem. 2005; 26(16): 1701–1718. https://doi.org/10.3390/IJMS20184574
  • [38] Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: A Fast Force Field Generation Tool for Small Organic Molecules. J Comput Chem. 2011; 32(11): 2359–2368. https://doi.org/10.1002/JCC.21816
  • [39] Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD. CHARMM general force field: A Force field for drug-like molecules compatible with The CHARMM All-atom additive biological force fields. J Comput Chem. 2010; 31(4): 671–690. https://doi.org/10.1002/JCC.21367
  • [40] Boonstra S, Onck PR, Van Der Giessen E. CHARMM TIP3P Water model suppresses peptide folding by solvating the unfolded state. J Phys Chem B. 2016; 120(15): 3692–3698. https://doi.org/10.1021/acs.jpcb.6b01316
  • [41] Luo J, Xue ZQ, Liu WM, Wu JL, Yang ZQ. Koopmans’ Theorem for large molecular systems within density functional theory. J Phys Chem A. 2006; 110(43): 12005–12009. https://doi.org/10.1021/JP063669M

New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study

Year 2025, Volume: 29 Issue: 2, 790 - 805
https://doi.org/10.12991/jrespharm.1666359

Abstract

Genetic mutations in the dihydrofolate reductase-thymidylate synthase (PfDHFR-TS) enzyme of the malaria parasite Plasmodium falciparum have been shown to reduce the effectiveness of several approved antimalarial drugs. This phenomenon has become a new challenge to the malaria control and treatment sector. As a result, the goal of this study is to identify the best compound with the potential to be an antimalarial agent by employing a good model pharmacophore generated by mutated PfDHFR as a receptor. Using a computational chemistry method, a structurebased pharmacophore was utilized to analyze 165,878 compounds from the zinc database. Subsequently, these compounds were further examined by molecular docking. Furthermore, the ADMET properties, including absorption, distribution, metabolism, excretion, and toxicity, of these drug-candidates have been assessed. Furthermore, molecular dynamic simulations were employed to investigate the stability of the compound-receptor complex, while DFT investigations were utilized to examine the electronic characteristics of these compounds. Overall findings indicated that the substance C431 (ZINC257280996) may be a potent PfDHFR-TS inhibitor due to its favorable binding energy of -42.26 kJ/mol, molecular dynamics simulations' indication of its stability, and its advantageous pharmacokinetic characteristics along with its non-toxic nature. The findings suggested that compound C431 could be a promising antimalarial candidate. Furthermore, this research provides guidance for improving the structure of compound C431 for future synthesis and verifies its antimalarial efficacy in vitro.

References

  • [1] Rosenthal PJ. Malaria in 2022: Challenges and Progress. Am J Trop Med Hyg. 2022; 106(6): 1565–1567. https://doi.org/10.4269/AJTMH.22-0128
  • [2] World Health Organization. World Malaria Report 2022.
  • https://www.mmv.org/newsroom/news-resources-search/world-malaria-report-2022?gclid=CjwKCAiArfauBhApEiwAeoB7qA6Q9Va01OF0B6gGWENAb9rsmOPmSPlRmH6lzSGoDI6_aR_otcoEaxoC3G4QAvD_BwE (accessed November 15, 2023).
  • [3] Shibeshi MA, Kifle ZD, Atnafie SA. Antimalarial drug resistance and novel targets for antimalarial drug discovery. Infect Drug Resist. 2020; 13: 4047–4060. https://doi.org/10.2147/IDR.S279433
  • [4] Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem. 2021; 224: 113705. https://doi.org/10.1016/J.EJMECH.2021.113705
  • [5] Surabhi S, Singh B. Computer Aided Drug Design: An Overview. J Drug Deliv Ther. 2018; 8(5): 504–509. https://doi.org/10.22270/JDDT.V8I5.1894
  • [6] Muhammed MT, Aki-Yalcin E. Molecular docking: Principles, advances, and ıts applications in drug discovery. Lett Drug Des Discov. 2022; 21(3): 480–495. https://doi.org/10.2174/1570180819666220922103109
  • [7] Singh IV, Mishra S. Molecular docking analysis of pyrimethamine derivatives with Plasmodium falciparum dihydrofolate reductase. Bioinformation. 2018; 14(05): 232–235. https://doi.org/10.6026/97320630014232
  • [8] Hodoameda P. P. falciparum and ıts molecular markers of resistance to antimalarial drugs. In: Tyagi RK. (Eds). Plasmodium Species and Drug Resistance. IntechOpen, Inc., United Kingdonm, 2021, pp.1-21.
  • [9] Kreutzfeld O, Tumwebaze PK, Byaruhanga O, Katairo T, Okitwi M, Orena S, Rasmussen SA, Legac J, Conrad MD, Nsobya SL, Aydemir O, Bailey JA, Duffey M, Cooper RA, Rosenthal PJ. Decreased susceptibility to dihydrofolate reductase ınhibitors associated with genetic polymorphisms in Ugandan Plasmodium falciparum ısolates. J Infect Dis. 2022; 225(4): 696–704. https://doi.org/10.1093/INFDIS/JIAB435
  • [10] Posayapisit N, Pengon J, Prommana P, Shoram M, Yuthavong Y, Uthaipibull C, Kamchonwongpaisan S, Jupatanakul N. Transgenic pyrimethamine-resistant Plasmodium falciparum reveals transmission-blocking potency of P218, a novel antifolate candidate drug. Int J Parasitol. 2021; 51(8): 635–642. https://doi.org/10.1016/J.IJPARA.2020.12.002
  • [11] Rastelli G, Sirawaraporn W, Sompornpisut P, Vilaivan T, Kamchonwongpaisan S, Quarrell R, Lowe G, Thebtaranonth Y, Yuthavong Y. Interaction of pyrimethamine, cycloguanil, WR99210 and their analogues with Plasmodium falciparum dihydrofolate reductase: Structural basis of antifolate resistance. Bioorg Med Chem. 2000; 8(5): 1117–1128. https://doi.org/10.1016/S0968-0896(00)00022-5
  • [12] Tarnchompoo B, Chitnumsub P, Jaruwat A, Shaw PJ, Vanichtanankul J, Poen S, Rattanajak R, Wongsombat C, Tonsomboon A, Decharuangsilp S, Anukunwithaya T, Arwon U, Kamchonwongpaisan S, Yuthavong Y. Hybrid ınhibitors of malarial dihydrofolate reductase with dual binding modes that can forestall resistance. ACS Med Chem Lett. 2018; 9(12): 1235–1240. https://doi.org/10.1021/acsmedchemlett.8b00389
  • [13] Borkakoty B, Sarma K, Parida P, Prakash A, Mohapatra P, Mahanta J. In silico screening of antifolate based novel ınhibitors from Brucea mollis Wall. ex kurz against quadruple mutant drug resistant PfDHFR. Comb Chem High Throughput Screen. 2014; 17(8): 681–693. https://doi.org/10.2174/1386207317666140521153305
  • [14] Da Silva RSFL, Olanda CG, Fokoue HH, Sant’Anna CMR. Virtual screening techniques in drug discovery: Review and recent applications. Curr Top Med Chem. 2019; 19(19): 1751–1767. https://doi.org/10.2174/1568026619666190816101948
  • [15] Wang Z, Sun H, Shen C, Hu X, Gao J, Li D, Cao D, Hou T. Combined strategies in structure-based virtual screening. Phys Chem Chem Phys. 2020; 22(6): 3149–3159. https://doi.org/10.1039/C9CP06303J
  • [16] Kamchonwongpaisan S, Charoensetakul N, Srisuwannaket C, Taweechai S, Rattanajak R, Vanichtanankul J, Vitsupakorn D, Arwon U, Thongpanchang C, Tarnchompoo B, Vilaivan T, Yuthavong Y. Flexible diaminodihydrotriazine inhibitors of Plasmodium falciparum dihydrofolate reductase: Binding strengths, modes of binding and their antimalarial activities. Eur J Med Chem. 2020;195: 112263. https://doi.org/10.1016/J.EJMECH.2020.112263
  • [17] Somandi K, Seanego TD, Dlamini T, Maree M, de Koning CB, Vanichtanankul J, Rattanajak R, Saeyang T, Yuthavong Y, Kamchonwongpaisan S, Rousseau AL. Molecular docking studies, synthesis and biological evaluation of substituted pyrimidine-2,4-diamines as ınhibitors of Plasmodium falciparum dihydrofolate reductase. ChemMedChem. 2022; 17(22): e202200418. https://doi.org/10.1002/CMDC.202200418
  • [18] Opo FADM, Rahman MM, Ahammad F, Ahmed I, Bhuiyan MA, Asiri AM. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci Rep. 2021; 18(11): 4049. https://doi.org/10.1038/s41598-021-83626-x
  • [19] Kyei LK, Gasu EN, Ampomah GB, Mensah JO, Borquaye LS. An in silico study of the ınteractions of alkaloids from Cryptolepis sanguinolenta with Plasmodium falciparum dihydrofolate reductase and dihydroorotate dehydrogenase. J Chem. 2022; 2022: 1–26. https://doi.org/10.1155/2022/5314179
  • [20] Durán-Iturbide NA, Díaz-Eufracio BI, Medina-Franco JL. In silico ADME/Tox profiling of natural products: A focus on BIOFACQUIM. ACS Omega. 2020; 5(26): 16076–1684. https://doi.org/10.1021/acsomega.0c01581
  • [21] Azzam KM Al. SwissADME and pkCSM Webservers Predictors: an integrated online platform for accurate and comprehensive predictions for ın silico ADMET properties of artemisinin and its derivatives. Complex Use Miner Resour. 2023; 325(2): 14–21. https://doi.org/10.31643/2023/6445.13
  • [22] Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using Graph-Based Signatures. J Med Chem. 2015; 58(9): 4066–4072. https://pubs.acs.org/doi/10.1021/acs.jmedchem.5b00104
  • [23] Purwanto BT, Hardjono S, Widiandani T, Nasyanka AL, Siswanto I. In silico study and ADMET prediction of N-(4-fluorophenylcarbamothioyl)benzamide derivatives as cytotoxic agents. J Hunan Univ Sci. 2021; 48(2): 78–85.
  • [24] Yeni Y, Rachmania RA. The prediction of pharmacokinetic properties of compounds in Hemigraphis alternata (Burm.F.) T. Ander leaves using pkCSM. Indones J Chem. 2022; (4): 1081–1089. https://doi.org/10.22146/ijc.73117
  • [25] Walters WP. Going further than Lipinski’s Rule in drug design. Expert Opin Drug Discov. 2012; 7(2): 99–107. https://doi.org/10.1517/17460441.2012.648612
  • [26] Jia CY, Li JY, Hao GF, Yang GF. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov Today. 2020; 25(1): 248–258. https://doi.org/10.1016/j.drudis.2019.10.014
  • [27] Taidi L, Maurady A, Britel MR. Molecular docking study and molecular dynamic simulation of human cyclooxygenase-2 (COX-2) with selected eutypoids. J Biomol Struct Dyn. 2022; 40(3): 1189–1204. https://doi.org/10.1080/07391102.2020.1823884
  • [28] Mosquera-Yuqui F, Lopez-Guerra N, Moncayo-Palacio EA. Targeting the 3CLpro and RdRp of SARS-CoV-2 with phytochemicals from medicinal plants of The Andean Region: Molecular docking and molecular dynamics simulations. J Biomol Struct Dyn. 2022; 40(5): 2010–2023. https://doi.org/10.1080/07391102.2020.1835716
  • [29] Menéndez CA, Accordino SR, Gerbino DC, Appignanesi GA. Hydrogen bond dynamic propensity studies for protein binding and drug design. PLoS One. 2016;11(10):e0165767. https://doi.org/10.1371/journal.pone.0165767
  • [30] El-Shamy NT, Alkoud AM, Hussein RK, Ibrahim MA, Alhamzani AG, Abou-Krisha MM. DFT, ADMET and molecular docking ınvestigations for the antimicrobial activity of 6,6’-Diamino-1,1’,3,3’-tetramethyl-5,5’-(4-chlorobenzylidene)bis[pyrimidine-2,4(1H,3H)-dione]. Molecules. 2022; 27(3): 620. https://doi.org/10.3390/molecules27030620
  • [31] Tsuneda T, Song JW, Suzuki S, Hirao K. On Koopmans’ Theorem in Density Functional Theory. J Chem Phys. 2010;133(17):174101. https://doi.org/10.1063/1.3491272
  • [32] Giordano D, Biancaniello C, Argenio MA, Facchiano A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals (Basel). 2022;15(5):646. https://doi.org/10.3390/ph15050646
  • [33] Imrie F, Bradley AR, Deane CM. Generating property-matched decoy molecules using deep learning. Bioinformatics. 2021; 37(15): 2134–2141. https://dx.doi.org/10.1093/bioinformatics/btab080
  • [34] Heinzke AL, Zdrazil B, Leeson PD, Young RJ, Pahl A, Waldmann H, Leach AR. A compound-target pairs dataset: differences between drugs, clinical candidates and other bioactive compounds. Sci Data. 2024;11(1):1160. https://doi.org/10.1038/s41597-024-03582-9.
  • [35] Naz S, Farooq U, Khan S, Sarwar R, Mabkhot YN, Saeed M, Alsayari A, Muhsinah AB, Ul-Haq Z. Pharmacophore model-based virtual screening, docking, biological evaluation and molecular dynamics simulations for inhibitors discovery against α-tryptophan synthase from Mycobacterium tuberculosis. J Biomol Struct Dyn. 2021;39(2):610-620. https://doi.org/10.1080/07391102.2020.1715259
  • [36] Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key topics in molecular docking for drug design. Int J Mol Sci. 2019;20(18):4574. https://doi.org/10.3390/IJMS20184574
  • [37] Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: Fast, Flexible, and free. J Comput Chem. 2005; 26(16): 1701–1718. https://doi.org/10.3390/IJMS20184574
  • [38] Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: A Fast Force Field Generation Tool for Small Organic Molecules. J Comput Chem. 2011; 32(11): 2359–2368. https://doi.org/10.1002/JCC.21816
  • [39] Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD. CHARMM general force field: A Force field for drug-like molecules compatible with The CHARMM All-atom additive biological force fields. J Comput Chem. 2010; 31(4): 671–690. https://doi.org/10.1002/JCC.21367
  • [40] Boonstra S, Onck PR, Van Der Giessen E. CHARMM TIP3P Water model suppresses peptide folding by solvating the unfolded state. J Phys Chem B. 2016; 120(15): 3692–3698. https://doi.org/10.1021/acs.jpcb.6b01316
  • [41] Luo J, Xue ZQ, Liu WM, Wu JL, Yang ZQ. Koopmans’ Theorem for large molecular systems within density functional theory. J Phys Chem A. 2006; 110(43): 12005–12009. https://doi.org/10.1021/JP063669M
There are 42 citations in total.

Details

Primary Language English
Subjects Pharmacology and Pharmaceutical Sciences (Other)
Journal Section Articles
Authors

Putra Jiwamurwa Pama Tjitda

Sree Vaneesa Nagalingam This is me

Febri Odel Nitbani

Dominus Mbunga This is me

Oemeria Sitta Subadra This is me

Syahira Binti Mohd Abdul Wahab This is me

Maria Hilaria This is me

Ibrahim Abdullah This is me

Mariana Oni Betan This is me

Publication Date
Submission Date February 27, 2024
Acceptance Date June 3, 2024
Published in Issue Year 2025 Volume: 29 Issue: 2

Cite

APA Tjitda, P. J. P., Nagalingam, S. V., Nitbani, F. O., Mbunga, D., et al. (n.d.). New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study. Journal of Research in Pharmacy, 29(2), 790-805. https://doi.org/10.12991/jrespharm.1666359
AMA Tjitda PJP, Nagalingam SV, Nitbani FO, Mbunga D, Subadra OS, Wahab SBMA, Hilaria M, Abdullah I, Betan MO. New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study. J. Res. Pharm. 29(2):790-805. doi:10.12991/jrespharm.1666359
Chicago Tjitda, Putra Jiwamurwa Pama, Sree Vaneesa Nagalingam, Febri Odel Nitbani, Dominus Mbunga, Oemeria Sitta Subadra, Syahira Binti Mohd Abdul Wahab, Maria Hilaria, Ibrahim Abdullah, and Mariana Oni Betan. “New Inhibitor Quadrupole Mutant PfDHFR As Antimalaria Candidate: Structure-Based Pharmacophore, Molecular Docking, ADMET, Molecular Dynamic, and Chemical Quantum Study”. Journal of Research in Pharmacy 29, no. 2 n.d.: 790-805. https://doi.org/10.12991/jrespharm.1666359.
EndNote Tjitda PJP, Nagalingam SV, Nitbani FO, Mbunga D, Subadra OS, Wahab SBMA, Hilaria M, Abdullah I, Betan MO New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study. Journal of Research in Pharmacy 29 2 790–805.
IEEE P. J. P. Tjitda, S. V. Nagalingam, F. O. Nitbani, D. Mbunga, O. S. Subadra, S. B. M. A. Wahab, M. Hilaria, I. Abdullah, and M. O. Betan, “New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study”, J. Res. Pharm., vol. 29, no. 2, pp. 790–805, doi: 10.12991/jrespharm.1666359.
ISNAD Tjitda, Putra Jiwamurwa Pama et al. “New Inhibitor Quadrupole Mutant PfDHFR As Antimalaria Candidate: Structure-Based Pharmacophore, Molecular Docking, ADMET, Molecular Dynamic, and Chemical Quantum Study”. Journal of Research in Pharmacy 29/2 (n.d.), 790-805. https://doi.org/10.12991/jrespharm.1666359.
JAMA Tjitda PJP, Nagalingam SV, Nitbani FO, Mbunga D, Subadra OS, Wahab SBMA, Hilaria M, Abdullah I, Betan MO. New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study. J. Res. Pharm.;29:790–805.
MLA Tjitda, Putra Jiwamurwa Pama et al. “New Inhibitor Quadrupole Mutant PfDHFR As Antimalaria Candidate: Structure-Based Pharmacophore, Molecular Docking, ADMET, Molecular Dynamic, and Chemical Quantum Study”. Journal of Research in Pharmacy, vol. 29, no. 2, pp. 790-05, doi:10.12991/jrespharm.1666359.
Vancouver Tjitda PJP, Nagalingam SV, Nitbani FO, Mbunga D, Subadra OS, Wahab SBMA, Hilaria M, Abdullah I, Betan MO. New inhibitor quadrupole mutant PfDHFR as antimalaria candidate: Structure-based pharmacophore, molecular docking, ADMET, molecular dynamic, and chemical quantum study. J. Res. Pharm. 29(2):790-805.