Pharmacophore-based 3D-QSAR modeling, virtual screening, docking, molecular dynamics and biological evaluation studies for identification of potential inhibitors of alpha-glucosidase.
3D-QSAR
Alpha-glucosidase
Enzyme inhibition
Molecular docking
Molecular dynamics
Journal
Journal of molecular modeling
ISSN: 0948-5023
Titre abrégé: J Mol Model
Pays: Germany
ID NLM: 9806569
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
23
05
2023
accepted:
14
10
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
epublish
Résumé
Alpha-glucosidase enzyme is considered an important therapeutic target for controlling hyperglycemia associated with type 2 diabetes. Novel scaffolds identified as potential alpha-glucosidase inhibitors from the Maybridge library utilizing pharmacophore modeling, molecular docking and biological evaluation are reported in this manuscript. A total of 51 xanthone series scaffolds previously reported as alpha-glucosidase inhibitors were collected and used as training and test sets. These sets were employed to develop and validate a pharmacophore-based 3D-QSAR model with statistically meaningful results using Schrodinger software. The model showed a high F value (F, 80.1) at five component partial least square factors, a high cross-validation coefficient (Q
Identifiants
pubmed: 39476191
doi: 10.1007/s00894-024-06181-y
pii: 10.1007/s00894-024-06181-y
doi:
Substances chimiques
Glycoside Hydrolase Inhibitors
0
alpha-Glucosidases
EC 3.2.1.20
Xanthones
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
389Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Rahim F, Ullah H, Javid MT et al (2015) Synthesis, in vitro evaluation and molecular docking studies of thiazole derivatives as new inhibitors of α-glucosidase. Bioorg Chem 62:15–21. https://doi.org/10.1016/j.bioorg.2015.06.006
doi: 10.1016/j.bioorg.2015.06.006
pubmed: 26162519
Zou Q, Qu K, Luo Y et al (2018) Predicting diabetes mellitus with machine learning techniques. Front Genet 9:1538–1551. https://doi.org/10.3389/fgene.2018.00515
doi: 10.3389/fgene.2018.00515
Saddique FA, Ahmad M, Ashfaq UA et al (2019) Alpha-glucosidase inhibition and molecular docking studies of 1,2-benzothiazine 1,1-dioxide based carbohydrazides. Pak J Pharm Sci 32:2829–2834
pubmed: 32024621
Khalil H (2017) Diabetes microvascular complications—a clinical update. Diabetes Metab Syndr Clin Res Rev 11:S133–S139. https://doi.org/10.1016/j.dsx.2016.12.022
doi: 10.1016/j.dsx.2016.12.022
Singer ME, Dorrance KA, Oxenreiter MM et al (2022) The type 2 diabetes ‘modern preventable pandemic’ and replicable lessons from the COVID-19 crisis. Prev Med Reports 25:101636. https://doi.org/10.1016/j.pmedr.2021.101636
doi: 10.1016/j.pmedr.2021.101636
Abuelizz HA, Iwana NANI, Ahmad R et al (2019) Synthesis, biological activity and molecular docking of new tricyclic series as α-glucosidase inhibitors. BMC Chem 13:1–14. https://doi.org/10.1186/s13065-019-0560-4
doi: 10.1186/s13065-019-0560-4
Laar F (2008) Alpha-glucosidase inhibitors in the early treatment of type 2 diabetes. Vasc Health Risk Manag 4:1189–1195. https://doi.org/10.2147/VHRM.S3119
doi: 10.2147/VHRM.S3119
pubmed: 19337532
pmcid: 2663450
Özil M, Emirik M, Etlik SY et al (2016) A simple and efficient synthesis of novel inhibitors of alpha-glucosidase based on benzimidazole skeleton and molecular docking studies. Bioorg Chem 68:226–235. https://doi.org/10.1016/j.bioorg.2016.08.011
doi: 10.1016/j.bioorg.2016.08.011
pubmed: 27572707
Jadalla BMIS, Moser JJ, Sharma R et al (2022) In vitro alpha-glucosidase and alpha-amylase inhibitory activities and antioxidant capacity of Helichrysum cymosum and Helichrysum pandurifolium Schrank Constituents. Separations 9:190. https://doi.org/10.3390/separations9080190
doi: 10.3390/separations9080190
Gorricho J, Garjón J, Alonso A et al (2017) Use of oral antidiabetic agents and risk of community-acquired pneumonia: a nested case-control study. Br J Clin Pharmacol 83:2034–2044. https://doi.org/10.1111/bcp.13288
doi: 10.1111/bcp.13288
pubmed: 28294379
pmcid: 5555874
Zardecki C, Dutta S, Goodsell DS et al (2016) RCSB Protein Data Bank: a resource for chemical, biochemical, and structural explorations of large and small biomolecules. J Chem Educ 93:569–575. https://doi.org/10.1021/acs.jchemed.5b00404
doi: 10.1021/acs.jchemed.5b00404
Mechchate H, Es-safi I, Louba A et al (2021) In vitro alpha-amylase and alpha-glucosidase inhibitory activity and in vivo antidiabetic activity of Withania frutescens L. foliar extract. Molecules 26:293. https://doi.org/10.3390/molecules26020293
doi: 10.3390/molecules26020293
pubmed: 33430115
pmcid: 7826620
Assefa ST, Yang E, Chae S et al (2019) Alpha glucosidase inhibitory activities of plants with focus on common vegetables. Plants 9:2. https://doi.org/10.3390/plants9010002
doi: 10.3390/plants9010002
pubmed: 31861279
pmcid: 7020213
Alqahtani AS, Hidayathulla S, Rehman MT et al (2019) Alpha-amylase and alpha-glucosidase enzyme inhibition and antioxidant potential of 3-oxolupenal and katononic acid isolated from Nuxia oppositifolia. Biomolecules 10:61. https://doi.org/10.3390/biom10010061
doi: 10.3390/biom10010061
pubmed: 31905962
pmcid: 7022278
Mugaranja KP, Kulal A (2020) Alpha glucosidase inhibition activity of phenolic fraction from Simarouba glauca : an in-vitro, in-silico and kinetic study. Heliyon 6:e04392. https://doi.org/10.1016/j.heliyon.2020.e04392
doi: 10.1016/j.heliyon.2020.e04392
pubmed: 32671273
pmcid: 7350133
Han L, Fang C, Zhu R et al (2017) Inhibitory effect of phloretin on α-glucosidase: kinetics, interaction mechanism and molecular docking. Int J Biol Macromol 95:520–527. https://doi.org/10.1016/j.ijbiomac.2016.11.089
doi: 10.1016/j.ijbiomac.2016.11.089
pubmed: 27894824
Zonoubi AH, CN P, D Visaga Perumal ZA (2019) In silico analysis of active constituents of silymarin as αlpha-glucosidase enzyme inhibitors in type 2 diabetes mellitus. Asian J Pharm Clin Res 9:225–229. https://doi.org/10.22159/ajpcr.2019.v12i9.34460
Fouotsa H, Meli A, Djama C et al (2012) Xanthones inhibitors of a -glucosidase and glycation from Garcinia nobilis. Phytochem Lett 5:236–239. https://doi.org/10.1016/j.phytol.2012.01.002
doi: 10.1016/j.phytol.2012.01.002
Carpéné C, Les F, Cásedas G et al (2019) Engineering and biomedical effects of commercial juices of berries, cherries, and pomegranates with high polyphenol content. In: Non-Alcoholic Beverages. Elsevier, pp 259–283. https://doi.org/10.1016/B978-0-12-815270-6.00009-8
Sigalapalli DK, Yele V, Jupudi S, Shaikh AS, Kadagathur M, Tangellamudi ND, Babu BN (2021) Insights into the pharmacophore-based 3D-QSAR modeling, molecular dynamics simulation studies of certain dihydroxy pyrrolidine/piperidine and aza-flavanone derivatives as α-glucosidase inhibitors. J Mol Struct 1223:129243. https://doi.org/10.1016/j.molstruc.2020.12924321
Zheng X, Zhou S, Zhang C, Wu D, Luo HB, Wu Y (2017) Docking-assisted 3D-QSAR studies on xanthones as α-glucosidase inhibitors. J Mol Model 23:1–2. https://doi.org/10.1007/s00894-017-3438-1
Liu Y, Zou L, Ma L, Chen WH, Wang B, Xu ZL (2006) Synthesis and pharmacological activities of xanthone derivatives as α-glucosidase inhibitors. Bioorg Med Chem 16:5683–5690. https://doi.org/10.1016/j.bmc.2006.04.014
Liu Y, Ma L, Chen WH, Wang B, Xu ZL (2007) Synthesis of xanthone derivatives with extended π-systems as α-glucosidase inhibitors: Insight into the probable binding mode. Bioorg Med Chem 8:2810–2814. https://doi.org/10.1016/j.bmc.2007.02.030
LigPrep, Schrödinger Inc. (2021) New York, NY, USA. https://www.schrodinger.com/products/ligprep
Banks JL, Beard HS, Cao Y et al (2005) Integrated modeling program, applied chemical theory (IMPACT). J Comput Chem 26:1752–1780. https://doi.org/10.1002/jcc.20292
doi: 10.1002/jcc.20292
pubmed: 16211539
pmcid: 2742605
Dixon SL, Smondyrev AM, Rao SN (2006) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67:370–372. https://doi.org/10.1111/j.1747-0285.2006.00384.x
doi: 10.1111/j.1747-0285.2006.00384.x
pubmed: 16784462
Ramachandran R, Piramanyagam S (2017) Pharmacophore modeling, atom based 3D-QSAR and molecular docking approaches to screen C-X-C chemokine receptor type 4 antagonists as microbicides for human immunodeficiency virus-1. VirusDisease 28:272–280. https://doi.org/10.1007/s13337-017-0397-1
doi: 10.1007/s13337-017-0397-1
pubmed: 29291213
pmcid: 5684990
Phases of Drug Development Process, Drug Discovery Process, NorthEast BioLab (2022) https://www.nebiolab.com/drug-discoveryand-development-process . Accessed 17 Jun 2022
Bhagwati S, Siddiqi MI (2020) Identification of potential soluble epoxide hydrolase (sEH) inhibitors by ligand-based pharmacophore model and biological evaluation. J Biomol Struct Dyn 38:4956–4966. https://doi.org/10.1080/07391102.2019.1691659
doi: 10.1080/07391102.2019.1691659
pubmed: 31701805
Palakurti R, Vadrevu R (2017) Pharmacophore based 3D-QSAR modeling, virtual screening and docking for identification of potential inhibitors of β-secretase. Comput Biol Chem 68:107–117. https://doi.org/10.1016/j.compbiolchem.2017.03.001
doi: 10.1016/j.compbiolchem.2017.03.001
pubmed: 28288354
Kirubakaran P, Muthusamy K, Singh KHD, Nagamani S (2012) Ligand-based pharmacophore modeling; atom-based 3D-QSAR analysis and molecular docking studies of phosphoinositide-dependent kinase-1 inhibitors. Indian J Pharm Sci 74:141–151. https://doi.org/10.4103/0250-474X.103846
doi: 10.4103/0250-474X.103846
pubmed: 23325995
pmcid: 3546331
Kandakatla N, Ramakrishnan G, Karthikeyan J, Chekkara R (2014) Pharmacophore modeling, atom based 3D-QSAR and docking studies of chalcone derivatives as tubulin inhibitors. Orient J Chem 30:1083–1098. https://doi.org/10.13005/ojc/300320
doi: 10.13005/ojc/300320
Force field, Schrödinger Inc (2021) https://www.schrodinger.com/sciencearticles/force-field . Accessed 12 Jul 2022
Maybridge, TharmoFisher (2022) https://www.thermofisher.in/chemicals/en/brands/ maybridge.html . Accessed 12 Jul 2022
ThermoFisher (2022) https://www.thermofisher.in/chemicals/en/home.html . Accessed 3 Jun 2022
Kushavah U, Panigrahi L, Ahmed S, Imran M (2022) Ligand - based in silico identification and biological evaluation of potential inhibitors of nicotinamide N - methyltransferase. Mol Divers. https://doi.org/10.1007/s11030-022-10485-7
doi: 10.1007/s11030-022-10485-7
pubmed: 35793051
Protein data bank (1971) Nature New Biol 223:10–38. https://www.rcsb.org . Accessed 12 Jul 2022
Protein Preparation Wizard Schrödinger Inc. (2021) https://www.schrodinger.com/science-articles/protein-preparation-wizard . Accessed 12 Jul 2022
Roig-Zamboni V, Cobucci-Ponzano B, Iacono R, Ferrara MC, Germany S, Bourne Y, Parenti G, Moracci M, Sulzenbacher G (2017) Structure of human lysosomal acid α-glucosidase–a guide for the treatment of Pompe disease. Nat Commun 81:1–0. https://doi.org/10.1038/s41467-017-01263-3
Kato A, Nakagome I, Hata M, Nash RJ, Fleet GW, Natori Y, Yoshimura Y, Adachi I, Hirono S (2020) Strategy for designing selective lysosomal acid α-glucosidase inhibitors: binding orientation and influence on selectivity. Molecules 12:2843. https://doi.org/10.3390/molecules25122843
Abraham MJ, Murtola T, Schulz R et al (2015) Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
doi: 10.1016/j.softx.2015.06.001
Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32:2359–2368. https://doi.org/10.1002/jcc.21816
doi: 10.1002/jcc.21816
pubmed: 21541964
Hammer Ø (2009) GROMACS Reference manual. Palaeontol Electron 1–168. https://manual.gromacs.org/2018.4/manual-2018.4.pdf
Ramesh D, Mohanty AK, De A et al (2022) Uracil derivatives as HIV-1 capsid protein inhibitors: design, in silico, in vitro and cytotoxicity studies. RSC Adv 12:17466–17480. https://doi.org/10.1039/d2ra02450k
doi: 10.1039/d2ra02450k
pubmed: 35765450
pmcid: 9190787
Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. https://doi.org/10.1016/0263-7855(96)00018-5
doi: 10.1016/0263-7855(96)00018-5
pubmed: 8744570
Rahim F, Taha M, Iqbal N et al (2020) Isatin based thiosemicarbazide derivatives as potential inhibitor of α-glucosidase, synthesis and their molecular docking study. J Mol Struct 1222:128922. https://doi.org/10.1016/j.molstruc.2020.128922
doi: 10.1016/j.molstruc.2020.128922
Abdullah MA, Lee YR, Mastuki SN et al (2020) Development of diarylpentadienone analogues as alpha-glucosidase inhibitor: synthesis, in vitro biological and in vivo toxicity evaluations, and molecular docking analysis. Bioorg Chem 104:104277. https://doi.org/10.1016/j.bioorg.2020.104277
doi: 10.1016/j.bioorg.2020.104277
pubmed: 32971414
Sahar M, Ali M, Ashraf M et al (2014) Synthesis of novel indenoquinoxaline derivatives as potent a -glucosidase inhibitors. Bioorg Med Chem 22:1195–1200. https://doi.org/10.1016/j.bmc.2013.12.024
doi: 10.1016/j.bmc.2013.12.024
Javaid K, Saad SM, Rasheed S, Moin ST, Syed N, Fatima I, Salar U, Khan KM, Perveen S, C MI (2015) 2-Arylquinazolin-4(3H)-ones: a new class of a-glucosidase inhibitors. Bioorg Med Chem 23:7417–7421. https://doi.org/10.1016/j.bmc.2015.10.038
doi: 10.1016/j.bmc.2015.10.038
pubmed: 26552899
Zuo J, Ji CL, Xia Y, Li X, Chen JW. (2014) Xanthones as α-glucosidase inhibitors from the antihyperglycemic extract of Securidaca inappendiculata. Pharm Biol 7:898–903. https://doi.org/10.3109/13880209.2013.872673
Hari Narayana Moorthy NS, Ramos MJ, Fernandes PA. (2011) Topological, hydrophobicity, and other descriptors on α-glucosidase inhibition: a QSAR study on xanthone derivatives. J Enzyme Inhib Med Chem 6:755–766. https://doi.org/10.3109/14756366.2010.549089
Ding SM, Lan T, Ye GJ et al (2018) Novel oxazolxanthone derivatives as a new type of α-glucosidase inhibitor: synthesis, activities, inhibitory modes and synergetic effect. Bioorganic Med Chem 26:3370–3378
doi: 10.1016/j.bmc.2018.05.008
Li ZP, Song YH, Uddin Z et al (2018) Xanthones from Cratoxylum cochinchinense, and their kinetic characterization. Bioorg Med Chem 26:737–746. https://doi.org/10.1016/j.bmc.2017.12.043
doi: 10.1016/j.bmc.2017.12.043
pubmed: 29306546
Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594. https://doi.org/10.1021/jm300687e
doi: 10.1021/jm300687e
pubmed: 22716043
pmcid: 3405771
QikProp, Schrödinger Inc. (2021) New York, NY, USA. https://www.schrodinger.com/products/qikprop . Accessed 19 Jul 2022