PHARMACOINFORMATICS ANALYSIS OF MORUS MACROURA FOR DRUG DISCOVERY AND DEVELOPMENT

PURNAWAN PONTANA PUTRA; AIYI ASNAWI; FARIZA HAMDAYUNI; ARFAN; LA ODE AMAN

doi:10.22159/ijap.2024.v16s1.26

Authors

PURNAWAN PONTANA PUTRA Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0001-9466-4569
AIYI ASNAWI Faculty of Pharmacy, Bhakti Kencana University, Bandung-40614, Indonesia https://orcid.org/0000-0002-8179-0520
FARIZA HAMDAYUNI Bachelor Program, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0009-0004-3442-0756
ARFAN Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Halu Oleo, Kendari-93132, Indonesia https://orcid.org/0000-0003-3004-7101
LA ODE AMAN Department of Chemistry, Faculty of Sciences and Mathematics, Universitas Negeri Gorontalo, Gorontalo-96128, Indonesia https://orcid.org/0000-0003-4478-6423

DOI:

https://doi.org/10.22159/ijap.2024.v16s1.26

Keywords:

Pharmacoinformatics, Morus macroura, Protein-protein interactions, Deep Learning docking, Molecular dynamics

Abstract

Objective: Pharmacoinformatics is an innovative approach rapidly evolving in pharmaceutical research and drug development. This study focuses on analysing Morus macroura, a plant species with untapped pharmacological potential. This investigation aims to leverage pharmacoinformatics techniques to unveil the hidden potential of Morus macroura in drug discovery and development.

Methods: The study includes analyses of protein-protein interactions, deep learning docking, adsorption tests, distribution, metabolism, excretion, molecular dynamics simulations and free energy calculation using Molecular Mechanics Generalized Born Surface Area (MMGBSA).

Results: Nine active compounds were identified in Morus macroura, namely Andalasin A, Guangsangon K, Guangsangon L, Guangsangon M, Guangsangon N, Macrourone C, Mulberrofuran G, Mulberrofuran K, and Mulberroside C. These compounds exhibit protein-protein interaction activities against a cytochrome P450 monooxygenase that catalyses the conversion of C19 androgens. These plant compounds influence aromatase excess syndrome, deficiency, and ovarian dysgenesis. Regarding drug-likeness, Mulberroside C and Macrourone C demonstrated good absorption potential by adhering to Lipinski's rule of five. Deep learning docking simulations yielded affinity results of-9.62 kcal/mol for Guangsangon M,-10.44 kcal/mol for Macrourone C, and-10.99 kcal/mol for Guangsangon L. Subsequent molecular dynamics simulations indicated that Guangsangon L and Macrourone C remained stable during a 100 ns simulation.

Conclusion: Morus macroura interacts with important proteins, particularly CYP19A1, which might influence health conditions like aromatase excess syndrome and ovarian dysgenesis. These findings provide potential paths for addressing specific health issues and advancing drug development. Molecular dynamics simulations indicated that Guangsangon L and Macrourone C remained stable during simulation.

Downloads

Download data is not yet available.

References

Nyola N, Jeyablan G, Kumawat M, Sharma R, Singh G, Kalra N. Pharmacoinformatics: a tool for drug discovery. Am J PharmTech Res. 2012;2(23).

Salama RM, Darwish SF, El Shaffei I, Elmongy NF, Fahmy NM, Afifi MS. Morus macroura Miq. Fruit extract protects against acetic acid-induced ulcerative colitis in rats: novel mechanistic insights on its impact on miRNA-223 and on the TNFα/NFκB/NLRP3 inflammatory axis. Food Chem Toxicol. 2022;165. doi: 10.1016/j.fct.2022.113146.

Dai SJ, Mi ZM, Ma ZB, Li S, Chen RY, Yu DQ. Bioactive diels-alder type adducts from the stem bark of morus macroura. Planta Med. 2004;70(8):758-63. doi: 10.1055/s-2004-827208, PMID 15368673.

El-Hawary SS, Sayed AM, Issa MY, Ebrahim HS, Alaaeldin R, Elrehany MA. Anti-Alzheimer chemical constituents of morus macroura Miq: chemical profiling, in silico and in vitro investigations. Food Funct. 2021;12(17):8078-89. doi: 10.1039/D1FO01177D.

Syah YM, Achmad SA, Ghisalberti EL, Hakim EH, Iman MZN, Makmur L. Andalasin A, a new stilbene dimer from Morus macroura. Fitoterapia. 2000;71(6):630-5. doi: 10.1016/s0367-326x(00)00221-5, PMID 11077168.

Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):D590-5. doi: 10.1093/nar/gky962, PMID 30321428.

Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: online mendelian inheritance in man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43:D789-98. doi: 10.1093/nar/gku1205, PMID 25428349.

Patel DA, Patel AC, Nolan WC, Huang G, Romero AG, Charlton N. High-throughput screening normalized to biological response: application to antiviral drug discovery. J Biomol Screen. 2014;19(1):119-30. doi: 10.1177/1087057113496848, PMID 23860224.

Daina A, Michielin O, Zoete V. Swiss ADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. doi: 10.1038/srep42717, PMID 28256516.

Ghosh D, Griswold J, Erman M, Pangborn W. Structural basis for androgen specificity and oestrogen synthesis in human aromatase. Nature. 2009;457(7226):219-23. doi: 10.1038/nature07614.

Bisong E. Google colaboratory. Build Mach Learn Deep Learn Model Google Cloud Platf; 2019. p. 59-64.

McNutt AT, Francoeur P, Aggarwal R, Masuda T, Meli R, Ragoza M. GNINA 1.0: molecular docking with deep learning. J Cheminform. 2021;13(1):43. doi: 10.1186/s13321-021-00522-2, PMID 34108002.

Rahim F, Putra PP, Ismed F, Putra AE, Lucida H, Molecular Dynamics. Docking and prediction of absorption, distribution, metabolism and excretion of lycopene as protein inhibitor of Bcl2 and DNMT1. Trop J Nat Prod Res. 2023;7:3439-44.

Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im W. Charmm-gui supports the amber force fields. J Chem Phys. 2020;153(3):035103. doi: 10.1063/5.0012280, PMID 32716185.

Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, De Groot BL. Charmm36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods. 2017;14(1):71-3. doi: 10.1038/nmeth.4067, PMID 27819658.

Mark P, Nilsson L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A. 2001;105(43):9954-60. doi: 10.1021/jp003020w.

Handayani D, Aminah I, Pontana Putra P, Eka Putra A, Arbain D, Satriawan H. The depsidones from marine sponge-derived fungus aspergillus unguis IB151 as an anti-MRSA agent: molecular docking, pharmacokinetics analysis, and molecular dynamic simulation studies. Saudi Pharm J. 2023;31(9):101744. doi: 10.1016/j.jsps.2023.101744, PMID 37649676.

Abraham MJ, Murtola T, Schulz R, Pall S, Smith JC, Hess B. Gromacs: high-performance molecular simulations through multi-level parallelism from laptops to supercomputers. Software X. 2015;1-2:19-25. doi: 10.1016/j.softx.2015.06.001.

Valdes Tresanco MS, Valdes Tresanco ME, Valiente PA, Moreno E. gmx_MMPBSA: a new tool to perform end-state free energy calculations with gromacs. J Chem Theory Comput. 2021;17(10):6281-91. doi: 10.1021/acs.jctc.1c00645, PMID 34586825.

Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638-46. doi: 10.1093/nar/gkac1000, PMID 36370105.

Ritchie TJ, Macdonald SJF, Peace S, Pickett SD, Luscombe CN. Increasing small molecule drug developability in sub-optimal chemical space. Med Chem Comm. 2013;4(4):673-80. doi: 10.1039/c3md00003f.

Ottaviani G, Gosling DJ, Patissier C, Rodde S, Zhou L, Faller B. What is modulating solubility in simulated intestinal fluids? Eur J Pharm Sci. 2010;41(3-4):452-7. doi: 10.1016/j.ejps.2010.07.012, PMID 20656026.

Vinarov Z, Abdallah M, Agundez JAG, Allegaert K, Basit AW, Braeckmans M. Impact of gastrointestinal tract variability on oral drug absorption and pharmacokinetics: an UNGAP review. Eur J Pharm Sci. 2021;162:105812. doi: 10.1016/j.ejps.2021.105812, PMID 33753215.

Hollenberg PF. Characteristics and common properties of inhibitors, inducers, and activators of CYP enzymes. Drug Metab Rev. 2002 Jan;34(1-2):17-35. doi: 10.1081/dmr-120001387, PMID 11996009.

Kirchmair J, Goller AH, Lang D, Kunze J, Testa B, Wilson ID. Predicting drug metabolism: experiment and/or computation? Nat Rev Drug Discov. 2015;14(6):387-404. doi: 10.1038/nrd4581.

Mermer A, Vakal S. Pyrazine-chromene-3-carbohydrazide conjugates: molecular docking and ADMET predictions on dual-acting compounds against SARS-CoV-2 Mpro and RdRp. JRP 2021;25(6)(25(6)):953-66. doi: 10.29228/jrp.92.

Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5):449-61. doi: 10.1517/17460441.2015.1032936, PMID 25835573.