Identification of Natural Product Inhibitors against Human Nicotinamide N-Methyltransferase (hNNMT): An In Silico and in vitro Approach

Abstract

Human nicotinamide N-methyltransferase (hNNMT) plays a pivotal role in phase-II biotransformation and is classified within the SAM-dependent methyltransferases family. Notably, hNNMT’s overexpression is common in various diseases, including metabolic disorders and various cancer types, which makes it an attractive therapeutic target. The motivation of this research was to examine the potential inhibitory property of flavonoids against hNNMT. A panel of commercially available flavonoids were subjected to molecular docking to assess their binding affinity with hNNMT, followed by in vitro binding assays to validate their inhibitory activity. In-house expression and purification of hNNMT protein were carried out to perform these experiments. The hNNMT activity of these molecules was determined using spectrofluorimetry using quinoline as the substrate. N-methylnicotinamide were used as positive control. Quercetin and morin emerged as potent inhibitors of hNNMT activity by in vitro. Furthermore, comprehensive analyses encompassing physicochemical properties, pharmacokinetics, drug-likeness, and toxicity profiles were conducted for these compounds in silico. A 250 ns molecular dynamics simulation was performed on hNNMT complexes with quercetin and morin to substantiate their inhibitory potential. Morin exhibited the highest potency amidst the polyphenols screened, with an IC₅₀ value of 14.97 ± 0.38 μM followed by Quercetin with 29.95 ± 1.3 μM. This research highlights the significant inhibitory potential of quercetin and morin against hNNMT and warrants further exploration of flavonoids as NNMT inhibitors to improve their potency through structure-based drug design.

Keywords

Flavonoids - hNNMT - Molecular dynamics - Enzyme inhibition

Publikationsverlauf

Eingereicht: 25. Juli 2025

Angenommen nach Revision: 08. September 2025

Accepted Manuscript online:
09. September 2025

Artikel online veröffentlicht:
08. Dezember 2025

• References

• 1 Aksoy S, Szumlanski CL, Weinshilboum RM. J Biol Chem 1994; 269 (20) 14835-14840
• 2 Pissios P. Trends Endocrinol Metab 2017; 28 (05) 340-353
• 3 Cantoni GL. J Biol Chem 1951; 189: 203-216
• 4 Li X-Y, Pi Y-N, Chen Y, Zhu Q, Xia B-R. Front Oncol 2022; 12: 894744
• 5 Riederer M, Erwa W, Zimmermann R, Frank S, Zechner R. Atherosclerosis 2009; 204 (02) 412-417
• 6 Rini J, Szumlanski C, Guerciolini R, Weinshilboum RM. Clin Chim Acta 1990; 186 (03) 359-374
• 7 Yan L, Otterness DM, Craddock TL, Weinshilboum RM. Biochem Pharmacol 1997; 54 (10) 1139-1149
• 8 Akar S, Harmankaya İ, Uğraş S, Çelik Ç. J Gynecol Oncol 2019; 17 (01)
• 9 Bach DH, Kim D, Bae SY. et al. Mol Ther Nucleic Acids 2018; 11: 455-467
• 10 Roessler M, Rollinger W, Mantovani-Endl L. et al. Mol Cell Proteomics 2006; 5: 2092-2101
• 11 Sartini D, Muzzonigro G, Milanese G, Pierella F, Rossi V, Emanuelli M. J Urol 2006; 176: 2248-2254
• 12 Sartini D, Santarelli A, Rossi V. et al. Mol Med 2007; 13: 415-421
• 13 Seta R, Mascitti M, Campagna R. et al. Clin Oral Investig 2019; 23: 829-838
• 14 Shin JH, Park CW, Yoon G, Hong SM, Choi KY. Oncogenesis 2018; 7: 76
• 15 Tomida M, Mikami I, Takeuchi S, Nishimura H, Akiyama H. J Cancer Res Clin Oncol 2009; 135: 1223-1229
• 16 Wang Y, Zeng J, Wu W. et al. Breast Cancer Res 2019; 21: 64
• 17 Xu J, Moatamed F, Caldwell JS. et al. J Clin Endocrinol Metab 2003; 88 (11) 4990-4996
• 18 Fedorowicz A, Mateuszuk Ł, Kopec G. et al. Respir Res 2016; 17 (01) 108
• 19 Jung J, Kim LJY, Wang X. et al. JCI Insight 2017; 2 (10) e90019
• 20 Kraus D, Yang Q, Kong D. et al. Nature 2014; 508 (7495) 258-262
• 21 Parsons RB, Smith M-L, Williams AC, Waring RH, Ramsden DB. J Neuropathol Exp Neurol 2002; 61 (02) 111-124
• 22 Parsons RB, Smith SW, Waring RH, Williams AC, Ramsden DB. Neurosci Lett 2003; 342 (01/02) 13-16
• 23 ten Klooster JP, Sotiriou A, Boeren S, Vaessen S, Vervoort J, Pieters R. Arch Biochem Biophys 2018; 644: 81-92
• 24 Kannt A, Pfenninger A, Teichert L. et al. Diabetologia 2015; 58 (04) 799-808
• 25 Kraus D, Yang Q, Kong D. et al. Nature 2014; 508 (7495) 258-262
• 26 Liu M, Li L, Chu J. et al. J Clin Endocrinol Metab 2015; 100 (08) 3112-3117
• 27 Salek RM, Maguire ML, Bentley E. et al. Physiol Genomics 2007; 29 (02) 99-108
• 28 Kannt A, Rajagopal S, Kadnur SV. et al. Sci Rep 2018; 8: 3660
• 29 Neelakantan H, Brightwell CR, Graber TG. et al. Biochem Pharmacol 2019; 163: 481-492
• 30 Harikrishna AS, Venkitasamy K. J Biomol Struct Dyn 2023; 1-13
• 31 Swaminathan S, Birudukota S, Thakur MK. et al. Protein Data Bank 2018;
• 32 Brylinski M. Methods Mol Biol 2017; 1611: 109-122
• 33 BIOVIA, Dassault Systèmes. Discovery Studio Visualizer, v21.1.0. San Diego: Dassault Systèmes; 2021
• 34 Dallakyan S, Olson AJ. Methods Mol Biol 2015; 1263: 243-250
• 35 Trott O, Olson AJ. J Comput Chem 2010; 31 (02) 455-461
• 36 Shaker B, Yu M-S, Lee J, Lee Y, Jung C, Na D. J Microbiol 2020; 58 (03) 235-244
• 37 Su M, Yang Q, Du Y. et al. J Chem Inf Model 2019; 59 (02) 895-913
• 38 Roopa DL, Shyamsunder K, Karunakar P. et al. J Mol Struct 2023; 1287: 135685
• 39 Macari G, Toti D, Pasquadibisceglie A, Polticelli F. Int J Mol Sci 2020; 21 (24) 9548
• 40 Peng Y, Sartini D, Pozzi V, Wilk D, Emanuelli M, Yee VC. Biochemistry 2011; 50 (36) 7800-7808
• 41 Loring HS, Thompson PR. Biochemistry 2018; 57 (39) 5524-5532
• 42 Neelakantan H, Vance V, Wang H-YL, McHardy SF, Watowich SJ. Biochemistry 2017; 56 (06) 824-832
• 43 Daina A, Michielin O, Zoete V. Sci Rep 2017; 7: 42717
• 44 Raies AB, Bajic VB. WIREs Comput Mol Sci 2016; 6 (02) 147-172
• 45 Banerjee P, Eckert AO, Schrey AK, Preissner R. Nucleic Acids Res 2018; 46 W1 W257-W263
• 46 Martin TM, Harten P, Venkatapathy R, Das S, Young DM. Toxicol Mech Methods 2008; 18 (02/03) 251-266
• 47 Banks JL, Beard HS, Cao Y. et al. J Comput Chem 2005; 26 (16) 1752-1780
• 48 Neshat N, Aaghaz S, Nasir A. et al. J Mol Struct 2024; 1295: 136648