A Computational Approach of Anti-diabetic Potential Evaluation of Flower and Seed of Nyctanthes arbor tristis Linn

Yıl 2025, Cilt: 9 Sayı: 1, 1 - 18

Ram Lal Swagat Shrestha Nirmal Parajuli Prabhat Neupane Sujan Dhital Binita Maharjan Timila Shrestha Samjhana Bharati Bishnu Prasad Marasini Jhashanath Adhikari Subin

Öz

Exploring the medicinal significance of bioactive compounds through computational methods is an increasingly practiced approach in contemporary medicinal research. This study aims to assess the antidiabetic potential of compounds extracted from the plant Nyctanthes arbor tristis by evaluating their ability to inhibit the carbohydrate metabolic enzyme α-glucosidase. The research work was conducted through molecular docking calculation, molecular dynamics simulation (MDS), and ADMET prediction techniques. Among the compounds, arbortistoside-C (NAS03), and arbortristoside-D (NAS04) found in the seed of the plant were identified as hit inhibitors of the target protein with docking scores, -9.9 and -9.4 kcal/mol, respectively. The compounds showed a comparable docking score with the drug of diabetes acarbose (-8.6 kcal/mol). Geometrical parameters like radius of gyration, solvent accessibility surface, root mean square deviation, and root mean square fluctuation from MDS supported the stability of the protein-ligand complex. MMPBSA calculations demonstrated the stability and feasibility of the complex with binding free energy changes of -29.06±6.06 and -23.58±8.80 kcal/mol for compounds NAS03 and NAS04, respectively. The ADMET prediction suggested the drug-likeness of the compounds compared with that of the standard drugs. The results could be used in proposing the antidiabetic potential of the two compounds from the plant as a potential inhibitors of α-glucosidase enzyme. Further, in vitro and in vivo experiments on such compounds could be a more reliable path to validate the output of this computational research.

Anahtar Kelimeler

Molecular docking, Glucosidase inhibitors, Molecular dynamics simulation, Binding free energy calculation, ADMET prediction

Kaynakça

• [1] S. Chatterjee, K. Khunti, M. J. Davies. Type 2 diabetes, The Lancet. 389 (2017) 2239–2251.
• [2] M. McGill, L. Blonde, J. C. N. Chan, K. Khunti, F. J. Lavalle, C. J. Bailey. The interdisciplinary team in type 2 diabetes management: Challenges and best practice solutions from real-world scenariosJ. Clin. Transl. Endocrinol. 7 (2017) 21–27
• [3] P. K. Jain, A. Pandey. The wonder of Ayurvedic medicine-Nyctanthes arbortristis, Int. J. Herb. Med. 9 (2016) 9–17.
• [4] M. Haque, N. Sultana, S. Abedin, N. Hossain, S. Kabir. Fatty acid analysis, cytotoxicity, antimicrobial and antioxidant activities of different extracts of the flowers of Nyctanthes arbor-tristis L., Bangladesh J. Sci. Ind. Res. 55 (2020) 207–214.
• [5] K. Priya, D. Ganjewala, Antibacterial Activities and Phytochemical Analysis of Different Plant Parts of Nyctanthes arbor tristis (Linn.), Res. J. Phytochem. 1 (2007) 61–67.
• [6] A. K. Singh, A. Kumar. Medicinal value of the leaves of Nyctanthes arbor tristis : A review, J. Med. Plants Stud. 10 (2022) 205–207.
• [7] S. Pundir, G. Kumar Gautam, S. Zaidi. A Review on Pharmacological Activity of Nyctanthes arbor tristis , Res. J. Pharmacogn. Phytochem. 14 (2022) 69–72.
• [8] N. K. S. M. Dewi, N. Fakhrudin, S. Wahyuono. A comprehensive review on the phytoconstituents and biological activities of Nyctanthes arbor tristis L., J. Appl. Pharm. Sci., 12 (2022) 9–17.
• [9] T. Sana, S. Qayyum, A. Jabeen, B. S. Siddiqui, S. Begum, R. A. Siddiqui, T. B. Hadda. Isolation and characterization of anti-inflammatory and anti-proliferative compound, for B-cell Non-Hodgkin lymphoma, from Nyctanthes arbor tristis Linn., J. Ethnopharmacol. 293 (2022)
• [10] G. C. Terstappen, A. Reggiani. In silico research in drug discovery, Trends Pharmaco. Sci. 22 (2001.
• [11] S. J. Y. Macalino, V. Gosu, S. Hong, S. Choi. Role of computer-aided drug design in modern drug discovery, Arch. Pharm. Res. 38 (2015) 9.
• [12] S. Basnet, M. P. Ghimire, T. R. Lamichhane, R. Adhikari, A. Adhikari. Identification of potential human pancreatic α-amylase inhibitors from natural products by molecular docking, MM/GBSA calculations, MD simulations, and ADMET analysis, PLoS One. 18 (2023) 01–13.
• [13] J. Yi, T. Zhao, Y. Zhang, Y. Tan, X. Han, Y. Tang, G. Chen Isolated compounds from Dracaena angustifolia Roxb and acarbose synergistically/additively inhibit α-glucosidase and α-amylase: an in vitro study, BMC Complement. Med. Ther. 22 (2022) 1–12.
• [14] X. Du, Y. Li, Y. L. Xia, S. M. Ai, J. Liang, P. Sang, X. L. Ji, S. Q. Liu. Insights into protein–ligand interactions: Mechanisms, models, and methods, Int. J. Mole. Sci. 17 (2016).
• [15] L. Martínez. Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis, PLoS One. 10 (2015).
• [16] M. W. El-Saadi, T. Williams-Hart, B. A. Salvatore, E. Mahdavian. Use of in-silico assays to characterize the ADMET profile and identify potential therapeutic targets of fusarochromanone, a novel anti-cancer agent, Silico Pharmacol. 3 (2015).
• [17] S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, E. E. Bolton. PubChem 2023 update, 51 (2022) 1373–1380.
• [18] O. Trott, A. J. Olson. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem. 2009. https://doi.org/10.1002/jcc.21334
• [19] S. Yuan, H. C. S. Chan, Z. Hu. Using PyMOL as a platform for computational drug design, Comput. Mol. Sci. 7 (2017).
• [20] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, P. E. Bourne. The Protein Data Bank, (2000).
• [21] A. Waterhouse, M. Bertoni, S. Bienert, G. Studer, G. Tauriello, R. Gumienny, F. T. Heer, T. A. P. De Beer, C. Rempfer, L. Bordoli, R. Lepore, T. Schwede. SWISS-MODEL: Homology modelling of protein structures and complexes, Nucleic Acids Res. 46 (2018) W296–W303, doi: 10.1093/nar/gky427.
• [22] S. Shaweta, S. Akhil, and G. Utsav, Molecular Docking studies on the Anti-fungal activity of Allium sativum (Garlic) against Mucormycosis (black fungus) by BIOVIA discovery studio visualizer 21.1.0.0, Ann. Antivirals Antiretrovir. (2021) 028–032, doi: 10.17352/aaa.000013.
• [23] J. Agrawal, A. Pal. Nyctanthes arbor tristis Linn - A critical ethnopharmacological review, J. Ethnopharmaco. 146 (2013) 645–658.
• [24] M. M. Rahman, S. K. Roy, M. Husain, M. Shahjahan. Chemical constituents of essential oil of petals and corolla tubes of Nyctanthes arbor tristis linn flower, J. Essent. Oil-Bearing Plants. 14 (2011) 717–721.
• [25] R. Chakraborty, S. Datta(De). A Brief Overview on the Health Benefits of Nyctanthes arbor tristis Linn.-A Wonder of Mother Nature, Indo Glob. J. Pharm. Sci. 12 (2022) 197–204.
• [26] P. Neupane, J. Adhikari Subin, R. Adhikari. Assessment of iridoids and their similar structures as antineoplastic drugs by in silico approach. J. Biomol. Struct. Dyn. (2024) 1–16.
• [27] V. Zoete, M. A. Cuendet, A. Grosdidier, O. Michielin. SwissParam: A fast force field generation tool for small organic molecules, J. Comput. Chem. 32 (2011) 2359–2368.
• [28] A. V. Onufriev, D. A. Case. Generalized Born Implicit Solvent Models for Biomolecules. 2019.
• [29] E. Wang, H. Sun, J. Wang, Z. Wang, H. Liu, J. Z. H. Zhang, T. Hou. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design, Chemical Reviews. 119 (2019) 9478–9508.
• [30] A. A. El-Bindary, A. F. Shoair, A. Z. El-Sonbati, M. A. Diab, E. E. Abdo. Geometrical structure, molecular docking and potentiometric studies of Schiff base ligand, J Mol Liq, 212 (2015) 576–584.
• [31] F. Cheng, W. Li, Y. Zhou, J. Shen, Z. Wu, G. Liu, P. W Lee, Y. Tang. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties, J. Chem. Inf. Model. 52 (2012) 3099–3105.
• [32] I. A. Guedes, C. S. de Magalhães, and L. E. Dardenne, Receptor-ligand molecular docking, Biophys. Rev. 6(2014) 75–87.
• [33] N. Cele, P. Awolade, P. Seboletswe, K. Olofinsan, M. S. Islam, P. Singh. α-Glucosidase and α-Amylase Inhibitory Potentials of Quinoline–1,3,4-oxadiazole Conjugates Bearing 1,2,3-Triazole with Antioxidant Activity, Kinetic Studies, and Computational Validation, Pharmaceuticals. 15 2022.
• [34] R. L. S. Shrestha, R. Panta, B. Maharjan, T. Shrestha, S. Bharati, S. Dhital et al. Molecular docking and ADMET prediction of compounds from Piper longum L. Detected by GC-MS analysis in diabetes management. Mor. J. Chem. 12(2024) 776-798. https://doi.org/10.48317/IMIST.PRSM/morjchem-v12i2.46845
• [35] O. M. H. Salo-Ahen, I. Alanko, R. Bhadane, A. M. J. J. Bonvin, R. V. Honorato, S. Hossain. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. 9 (2020) 71.
• [36] S. Bhaumik, A. Sarkar, S. Debnath, B. Debnath, R. Ghosh, M. E. Zaki, S. A. Al-Hussain. α-Glucosidase inhibitory potential of Oroxylum indicum using molecular docking, molecular dynamics, and in vitro evaluation, Saudi Pharm. J. 32(6) (2024) 102095.
• [37] Y Y. Deswal, S. Asija, A. Dubey, L. Deswal, D. Kumar, D. Kumar Jindal, J. Devi. Cobalt(II), nickel(II), copper(II) and zinc(II) complexes of thiadiazole based Schiff base ligands: Synthesis, structural characterization, DFT, antidiabetic and molecular docking studies, J. Mol. Struct. 1253 (2022).
• [38] O. M. Ogunyemi, G. A. Gyebi, A. Saheed, J. Paul, V. Nwaneri-Chidozie, O. Olorundare, J. Adebayo, M. Koketsu, N. Aljarba, S. Alkahtani, G. E. S. Batiha, C. O. Olaiya. Inhibition mechanism of alpha-amylase, a diabetes target, by a steroidal pregnane and pregnane glycosides derived from Gongronema latifolium Benth, Front. Mol. Biosci. 9 2022.
• [39] P. Neupane, S. Dhital, N. Parajuli, T. Shrestha, S. Bharati, B. Maharjan, J. Adhikari Subin, R. L. S. Shrestha. Exploration of Anti-Diabetic Potential of Rubus ellipticus smith through Molecular Docking, Molecular Dynamics Simulation, and MMPBSA Calculation, J. Nepal Phys. Soc. 9 (2023) 95–105. DOI: https://doi.org/10.3126/jnphyssoc.v9i2.62410
• [40] N. Lolok, S. A.Sumiwi, A. Muhtadi, Y. Susilawati, R. Hendriani, D. S. F. Ramadhan, J. Levita, I. Sahidin. Molecular docking and molecular dynamics studies of bioactive compounds contained in noni fruit (Morinda citrifolia L.) against human pancreatic α-amylase, J. Biomol. Struct. Dyn. 40 (2022) 7091–7098.
• [41] R. L. S. Shrestha, B. Maharjan, T. Shrestha, B. P. Marasini, J. Adhikari Subin. Geometrical and thermodynamic stability of govaniadine scaffold adducts with dopamine receptor D1, Results Chem. 7 2024
• [42] B. Xiong, Y. Wang, Y. Chen, S. Xing, Q. Liao, Y. Chen et al. Strategies for structural modification of small molecules to improve blood–brain barrier penetration: a recent perspective, J. Med. Chem. 64 (2021), 13152-13173.