Computational Investigation of Mannopyranoside Derivatives as Potential Dopamine D2 Inhibitors Using DFT and Molecular Docking Approaches — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Computational Investigation of Mannopyranoside Derivatives as Potential Dopamine D2 Inhibitors Using DFT and Molecular Docking Approaches
Laboratory of Carbohydrate and Nucleoside Chemistry (LCNC), Department of Chemistry, Faculty of Science, University of Chittagong, Chittagong, Bangladesh
,
Laboratory of Carbohydrate and Nucleoside Chemistry (LCNC), Department of Chemistry, Faculty of Science, University of Chittagong, Chittagong, Bangladesh
1 Laboratory of Carbohydrate and Nucleoside Chemistry (LCNC), Department of Chemistry, Faculty of Science, University of Chittagong, Chittagong, Bangladesh
Methyl α-D-mannopyranoside derivatives were investigated to overcome the limited stability and weak receptor-binding affinity of native mannopyranosides. Previously synthesized derivatives were computationally evaluated for their stability, pharmacokinetic properties, and dopamine D2 receptor-binding potential using DFT optimization, ADMET prediction, and molecular docking studies. Density Functional Theory (DFT) geometry optimization provided important molecular descriptors, including HOMO, LUMO, ionization potential, electron affinity, hardness, softness, electronegativity, and electrophilicity indices. Among the studied compounds, derivative 7 exhibited the lowest energy gap (5.3042 eV), indicating higher chemical reactivity, whereas the parent compound (1) showed the highest energy gap (7.4074 eV). Thermodynamic parameters and molecular electrostatic potential (MEP) analyses further explained their chemical stability and reactive behavior. Molecular docking studies demonstrated that compound 7 possessed the strongest binding affinity (?9.7 kcal/mol) toward the dopamine D2 receptor, forming hydrogen bonds and several hydrophobic interactions within the active binding pocket. ADMET predictions suggested favorable pharmacokinetic characteristics for the synthesized derivatives, while PASS analysis indicated several potential biological activities. Overall, this study provides valuable insights into the stability, reactivity, pharmacokinetic behavior, and potential dopamine D2 inhibitory activity of methyl α-D-mannopyranoside derivatives.
Xavier, N.M., Andreana, P.R., Carvalho, I. and von Itzstein, M. (2021) Editorial: Carbohydrate-Based Molecules in Medicinal Chemistry. FrontiersinChemistry, 9, Article 655200. https://doi.org/10.3389/fchem.2021.655200
Prokopová, A., Kéry, V., Stancíková, M., Grimová, J., Capek, P., Sandula, J. and Orviský, E. (1993) Methyl-α-D-Mannopyranoside, Mannooligosaccharides and Yeast Mannans Inhibit Development of Rat Adjuvant Arthritis. JournalofRheumatology, 20, 673-677.
Doğan, M.D., Ataoğlu, H., Ataoğlu, Ö. and Akarsu, E.S. (1999) Polysaccharide Mannan Components of Candida Albicans and Saccharomyces Cerevisiae Cell Wall Produce Fever by Intracerebroventricular Injection in Rats. BrainResearchBulletin, 48, 509-512. https://doi.org/10.1016/s0361-9230(99)00028-3
Tomašić, T., Rabbani, S., Gobec, M., Raščan, I.M., Podlipnik, Č., Ernst, B., etal. (2014) Branched α-D-Mannopyranosides: A New Class of Potent FimH Antagonists. MedChemComm, 5, 1247-1253. https://doi.org/10.1039/c4md00093e
Wang, S., Che, T., Levit, A., Shoichet, B.K., Wacker, D. and Roth, B.L. (2018) Structure of the D2 Dopamine Receptor Bound to the Atypical Antipsychotic Drug Risperidone. Nature, 555, 269-273. https://doi.org/10.1038/nature25758
Pan, L., Cai, C., Liu, C., Liu, D., Li, G., Linhardt, R.J., etal. (2021) Recent Progress and Advanced Technology in Carbohydrate-Based Drug Development. CurrentOpinioninBiotechnology, 69, 191-198. https://doi.org/10.1016/j.copbio.2020.12.023
Kawsar, S.M.A., Takeuchi, T., Kasai, K., Fujii, Y., Matsumoto, R., Yasumitsu, H., etal. (2009) Glycan-Binding Profile of a D-Galactose Binding Lectin Purified from the Annelid, Perinereisnuntia Ver. vallata. ComparativeBiochemistryandPhysiologyPartB: BiochemistryandMolecularBiology, 152, 382-389. https://doi.org/10.1016/j.cbpb.2009.01.009
Kawsar, S.M.A., Matsumoto, R., Fujii, Y., Matsuoka, H., Masuda, N., Chihiro, I., etal. (2011) Cytotoxicity and Glycan-Binding Profile of a D-Galactose-Binding Lectin from the Eggs of a Japanese Sea Hare (Aplysia kurodai). TheProteinJournal, 30, 509-519. https://doi.org/10.1007/s10930-011-9356-7
Chowdhury, S.A., Bhuiyan, M.M.R., Ozeki, Y. and Kawsar, S.M.A. (2016) Simple and Rapid Synthesis of Some Nucleoside Derivatives: Structural and Spectral Characterization. CurrentChemistryLetters, 5, 83-92. https://doi.org/10.5267/j.ccl.2015.12.001
Devi, S.R., Jesmin, S., Rahman, M., Manchur, M.A., Fujii, Y., Ozeki, Y., etal. (2019) Microbial Efficacy and Two Step Synthesis of Uridine Derivatives with Spectral Characterization. ACTAPharmaceuticaSciencia, 57, 47-68. https://doi.org/10.23893/1307-2080.aps.05704
Kabir, A.K.M.S., Kawsar, S.M.A., Bhuiyan, M.M.R., Islam, M.R. and Rahman, M.S. (2004) Biological Evaluation of Some Mannopyranoside Derivatives. BulletinofPure&AppliedSciences, 23, 83-91.
Kawsar, S.M.A. (2014) Regioselective Synthesis, Characterization, and Antimicrobial Activities of Some New Monosaccharide Derivatives. ScientiaPharmaceutica, 82, 1-20. https://doi.org/10.3797/scipharm.1308-03
Bhargava, K., Nath, R., Seth, P.K., Pant, K.K. and Dixit, R.K. (2014) Molecular Docking Studies of D2 Dopamine Receptor with Risperidone Derivatives. Bioinformation, 10, 8-12. https://doi.org/10.6026/97320630010008
Yasmin, F., Amin, M.R., Hosen, M.A., Bulbul, M.Z.H., Dey, S. and Kawsar, S.M.A. (2021) Monosaccharide Derivatives: Synthesis, Antimicrobial, Pass, Antiviral and Molecular Docking Studies Against SARS-CoV-2 MPRO Inhibitors. CelluloseChemistryandTechnology, 55, 477-499. https://doi.org/10.35812/cellulosechemtechnol.2021.55.44
Akter, N., Bourougaa, L., Ouassaf, M., Bhowmic, R.C., Uddin, K.M., Bhat, A.R., etal. (2024) Molecular Docking, ADME-Tox, DFT and Molecular Dynamics Simulation of Butyroyl Glucopyranoside Derivatives against DNA Gyrase Inhibitors as Antimicrobial Agents. JournalofMolecularStructure, 1307, Article ID: 137930. https://doi.org/10.1016/j.molstruc.2024.137930
Cui, T., Altaf, M., Aldarhami, A., Bazaid, A.S., Saeedi, N.H., Alkayyal, A.A., etal. (2023) Dihydropyrimidone Derivatives as Thymidine Phosphorylase Inhibitors: Inhibition Kinetics, Cytotoxicity, and Molecular Docking. Molecules, 28, Article 3634. https://doi.org/10.3390/molecules28083634
Lagunin, A., Stepanchikova, A., Filimonov, D. and Poroikov, V. (2000) PASS: Prediction of Activity Spectra for Biologically Active Substances. Bioinformatics, 16, 747-748. https://doi.org/10.1093/bioinformatics/16.8.747
Kawsar, S.M.A., Almalki, F.A., Hadd, T.B., Laaroussi, H., Khan, M.A.R., Hosen, M.A., etal. (2023) Potential Antifungal Activity of Novel Carbohydrate Derivatives Validated by POM, Molecular Docking and Molecular Dynamic Simulations Analyses. MolecularSimulation, 49, 60-75. https://doi.org/10.1080/08927022.2022.2123948
Beaulieu, J. and Gainetdinov, R.R. (2011) The Physiology, Signaling, and Pharmacology of Dopamine Receptors. PharmacologicalReviews, 63, 182-217. https://doi.org/10.1124/pr.110.002642
Usiello, A., Baik, J., Rougé-Pont, F., Picetti, R., Dierich, A., LeMeur, M., et al. (2000) Distinct Functions of the Two Isoforms of Dopamine D2 Receptors. Nature, 408, 199-203. https://doi.org/10.1038/35041572
Missale, C., Nash, S.R., Robinson, S.W., Jaber, M. and Caron, M.G. (1998) Dopamine Receptors: From Structure to Function. PhysiologicalReviews, 78, 189-225. https://doi.org/10.1152/physrev.1998.78.1.189
Chalkha, M., Chebbac, K., Nour, H., Nakkabi, A., El Moussaoui, A., Tüzün, B., et al. (2024) InVitro and inSilico Evaluation of the Antimicrobial and Antioxidant Activities of Spiropyrazoline Oxindole Congeners. ArabianJournalofChemistry, 17, Article ID: 105465. https://doi.org/10.1016/j.arabjc.2023.105465
Zell, L., Lainer, C., Kollár, J., Temml, V. and Schuster, D. (2022) Identification of Novel Dopamine D2 Receptor Ligands—A Combined inSilico/inVitro Approach. Molecules, 27, Article 4435. https://doi.org/10.3390/molecules27144435
Smith, A. (2008) Design and Synthesis of Carbohydrate Based Derivatives as Anti-microbial Compounds. Ph.D. Thesis, Dublin Institute of Technology.
Zhang, J.Z., Jiang, C. and Han, J. (2024) Salidroside and Its inVivo Metabolite Tyrosol Could Act Directly on Dopamine D2 Receptors: A Study Using RNAseq Combined with Connectivity Map Analysis. bioRxiv. https://doi.org/10.1101/2024.03.03.583234
Ul Islam, A., Serseg, T., Benarous, K., Ahmmed, F. and Kawsar, S.M.A. (2023) Synthesis, Antimicrobial Activity, Molecular Docking and Pharmacophore Analysis of New Propionyl Mannopyranosides. JournalofMolecularStructure, 1292, Article ID: 135999. https://doi.org/10.1016/j.molstruc.2023.135999
Kawsar, S.M.A., Hossain, M.A., Saha, S., Abdallah, E.M., Bhat, A.R., Ahmed, S., etal. (2024) Nucleoside‐based Drug Target with General Antimicrobial Screening and Specific Computational Studies against SARS-CoV-2 Main Protease. ChemistrySelect, 9, e202304774. https://doi.org/10.1002/slct.202304774
Frisch, M.J.E., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A. and Cheeseman, J.R. (2009) Gaussian 09. Gaussian Inc.
Arzine, A., Hadni, H., Boujdi, K., Chebbac, K., Barghady, N., Rhazi, Y., etal. (2024) Efficient Synthesis, Structural Characterization, Antibacterial Assessment, Adme-Tox Analysis, Molecular Docking and Molecular Dynamics Simulations of New Functionalized Isoxazoles. Molecules, 29, Article 3366. https://doi.org/10.3390/molecules29143366
Dallakyan, S. and Olson, A.J. (2015) Small-Molecule Library Screening by Docking with PyRx. In: Hempel, J., Williams, C. and Hong, C., Eds., Chemical Biology, Springer, 243-250. https://doi.org/10.1007/978-1-4939-2269-7_19
Kaplan, W. and Littlejohn, T.G. (2001) Swiss-PDB Viewer (Deep View). BriefingsinBioinformatics, 2, 195-197. https://doi.org/10.1093/bib/2.2.195
Iqbal, D., Alsaweed, M., Jamal, Q.M.S., Asad, M.R., Rizvi, S.M.D., Rizvi, M.R., etal. (2023) Pharmacophore-Based Screening, Molecular Docking, and Dynamic Simulation of Fungal Metabolites as Inhibitors of Multi-Targets in Neurodegenerative Disorders. Biomolecules, 13, Article 1613. https://doi.org/10.3390/biom13111613
Yuan, S., Chan, H.C.S. and Hu, Z. (2017) Using PyMOL as a Platform for Computational Drug Design. WIREs Computational Molecular Science, 7, e1298.
Forli, S., Huey, R., Pique, M.E., Sanner, M.F., Goodsell, D.S. and Olson, A.J. (2016) Computational Protein-Ligand Docking and Virtual Drug Screening with the AutoDock Suite. NatureProtocols, 11, 905-919. https://doi.org/10.1038/nprot.2016.051
Pires, D.E.V., Blundell, T.L. and Ascher, D.B. (2015) pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. JournalofMedicinalChemistry, 58, 4066-4072. https://doi.org/10.1021/acs.jmedchem.5b00104
Ramdani, E.D., Yanuar, A. and Tjandrawinata, R.R. (2019) Comparison of Dopamine D2 Receptor (Homology Model and X-Ray Structure) and Virtual Screening Protocol Validation for the Antagonism Mechanism. Journal of Applied Pharmaceutical Science, 9, 17-22.
Munia, N.S., Hosen, M.A., Azzam, K.M.A., Al-Ghorbani, M., Baashen, M., Hossain, M.K., etal. (2022) Synthesis, Antimicrobial, SAR, PASS, Molecular Docking, Molecular Dynamics and Pharmacokinetics Studies of 5’-O-Uridine Derivatives Bearing Acyl Moieties: POM Study and Identification of the Pharmacophore Sites. Nucleosides, Nucleotides&NucleicAcids, 41, 1036-1083. https://doi.org/10.1080/15257770.2022.2096898
Tegegn, D.F., Belachew, H.Z. and Salau, A.O. (2024) DFT/TDDFT Calculations of Geometry Optimization, Electronic Structure and Spectral Properties of Clevudine and Telbivudine for Treatment of Chronic Hepatitis B. ScientificReports, 14, Article No. 8146. https://doi.org/10.1038/s41598-024-58599-2
Bulbul, M.Z.H., Hosen, M.A., Ferdous, J., Chowdhury, T.S., Misbah, M.M.H. and Kawsar, S.M.A. (2021) DFT Study, Physicochemical, Molecular Docking and AD-MET Predictions of Some Modified Uridine Derivatives. International Journal of New Chemistry, 8, 88-110. https://doi.org/10.22034/ijnc.2020.131337.1124
Lewis, D.F.V., Ioannides, C. and Parke, D.V. (1994) Interaction of Nitriles with P450: Structure-Activity Analysis. Xenobiotica, 24, 401-408. https://doi.org/10.3109/00498259409043243
Mathiasen, A., Helal, H., Balanca, P., Krzywaniak, A., Parviz, A., Hvilshøj, F., Banaszewski, B., Luschi, C. and Fitzgibbon, A.W. (2024) Reducing the Cost of Quantum Chemical Data by Backpropagating Through Density Functional Theory. arXiv: 2402.04030.
Talmaciu, M.M., Bodoki, E. and Oprean, R. (2016) Global Chemical Reactivity Parameters for Several Chiral Beta-Blockers from Density Functional Theory Viewpoint. MedicineandPharmacyReports, 89, 513-518. https://doi.org/10.15386/cjmed-610
Kandemirli, F., Al-sawaff, Z. and Sayıner, H.S. (2025) Quantum Chemical Study on Two Benzimidazole Derivatives. JournalofAmasyaUniversityInstituteofScienceandTechnology, 1, 1-11.
Barman, S. and Sarkar, U. (2025) Prediction of Chemical Reactivity Parameters via Data-Driven Approach. AdvancedTheoryandSimulations, 8, Article ID: 2401517. https://doi.org/10.1002/adts.202401517
Hadigheh Rezvan, V. (2024) Molecular Structure, HOMO-LUMO, and NLO Studies of Some Quinoxaline 1,4-Dioxide Derivatives: Computational (HF and DFT) Analysis. ResultsinChemistry, 7, Article ID: 101437. https://doi.org/10.1016/j.rechem.2024.101437
Kawsar, S.M.A. and Hossain, M.A. (2020) An Optimization and Pharmacokinetic Studies of Some Thymidine Derivatives. TurkishComputationalandTheoreticalChemistry, 4, 59-66. https://doi.org/10.33435/tcandtc.718807
Krishnakumar, V., Keresztury, G., Sundius, T. and Seshadri, S. (2007) Density Functional Theory Study of Vibrational Spectra and Assignment of Fundamental Vibrational Modes of 1-Methyl-4-Piperidone. SpectrochimicaActaPartA: MolecularandBiomolecularSpectroscopy, 68, 845-850. https://doi.org/10.1016/j.saa.2006.12.069
Cohen, N. and Benson, S.W. (1993) Estimation of Heats of Formation of Organic Compounds by Additivity Methods. ChemicalReviews, 93, 2419-2438. https://doi.org/10.1021/cr00023a005
Lafridi, H., Almalki, F.A., Ben Hadda, T., Berredjem, M., Kawsar, S.M.A., Alqahtani, A.M., etal. (2023) InSilico Evaluation of Molecular Interactions between Macrocyclic Inhibitors with the HCV NS3 Protease. Docking and Identification of Antiviral Pharmacophore Site. JournalofBiomolecularStructureandDynamics, 41, 2260-2273. https://doi.org/10.1080/07391102.2022.2029571
Lien, E.J., Guo, Z., Li, R. and Su, C. (1982) Use of Dipole Moment as a Parameter in Drug-Receptor Interaction and Quantitative Structure-Activity Relationship Studies. JournalofPharmaceuticalSciences, 71, 641-655. https://doi.org/10.1002/jps.2600710611
Toriyama, M.Y., Ganose, A.M., Dylla, M., Anand, S., Park, J., Brod, M.K., etal. (2022) How to Analyse a Density of States. MaterialsTodayElectronics, 1, Article ID: 100002. https://doi.org/10.1016/j.mtelec.2022.100002
Maowa, J., Hosen, M.A., Alam, A., Rana, K.M., Fujii, Y. and Ozeki, Y. (2021) Pharmacokinetics and Molecular Docking Studies of Uridine Derivatives as SARS-CoV-2 Mpro Inhibitors. PhysicalChemistryResearch, 9, 385-412. https://doi.org/10.22036/pcr.2021.264541.1869
Foster, J.P. and Weinhold, F. (1980) Natural Hybrid Orbitals. JournaloftheAmericanChemicalSociety, 102, 7211-7218. https://doi.org/10.1021/ja00544a007
Reed, A.E., Curtiss, L.A. and Weinhold, F. (1988) Intermolecular Interactions from a Natural Bond Orbital, Donor-Acceptor Viewpoint. ChemicalReviews, 88, 899-926. https://doi.org/10.1021/cr00088a005
Hong, T., Yin, J., Nie, S. and Xie, M. (2021) Applications of Infrared Spectroscopy in Polysaccharide Structural Analysis: Progress, Challenge and Perspective. FoodChemistry: X, 12, Article ID: 100168. https://doi.org/10.1016/j.fochx.2021.100168
Mulliken, R.S. (1955) Electronic Population Analysis on LCAO-MO Molecular Wave Functions. I. TheJournalofChemicalPhysics, 23, 1833-1840. https://doi.org/10.1063/1.1740588
Filimonov, D.A., Lagunin, A.A., Gloriozova, T.A., Rudik, A.V., Druzhilovskii, D.S., Pogodin, P.V., etal. (2014) Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource. ChemistryofHeterocyclicCompounds, 50, 444-457. https://doi.org/10.1007/s10593-014-1496-1
Agu, P.C., Afiukwa, C.A., Orji, O.U., Ezeh, E.M., Ofoke, I.H., Ogbu, C.O., etal. (2023) Molecular Docking as a Tool for the Discovery of Molecular Targets of Nutraceuticals in Diseases Management. ScientificReports, 13, Article No. 13398. https://doi.org/10.1038/s41598-023-40160-2
Sahoo, R.N., Pattanaik, S., Pattnaik, G., Mallick, S. and Mohapatra, R. (2022) Review on the Use of Molecular Docking as the First Line Tool in Drug Discovery and Development. IndianJournalofPharmaceuticalSciences, 84, 1334-1337. https://doi.org/10.36468/pharmaceutical-sciences.1031
Jorgensen, W.L. (2004) The Many Roles of Computation in Drug Discovery. Science, 303, 1813-1818. https://doi.org/10.1126/science.1096361
Kitchen, D.B., Decornez, H., Furr, J.R. and Bajorath, J. (2004) Docking and Scoring in Virtual Screening for Drug Discovery: Methods and Applications. NatureReviewsDrugDiscovery, 3, 935-949. https://doi.org/10.1038/nrd1549
Ferdous, J., Qais, F.A., Ali, F., Palit, D., Hasan, I. and Kawsar, S.M.A. (2024) FTIR, 1H-/13C-NMR Spectral Characterization, Antimicrobial, Anticancer, Antioxidant, Anti-Inflammatory, PASS, SAR, and inSilico Properties of Methyl α-D-Glucopyranoside Derivatives. ChemicalPhysicsImpact, 9, Article ID: 100753. https://doi.org/10.1016/j.chphi.2024.100753
Langer, T. and Hoffmann, R. (2001) Virtual Screening an Effective Tool for Lead Structure Discovery. CurrentPharmaceuticalDesign, 7, 509-527. https://doi.org/10.2174/1381612013397861
Kayes, M.R., Saha, S., Alanazi, M.M., Ozeki, Y., Pal, D., Hadda, T.B., etal. (2023) Macromolecules: Synthesis, Antimicrobial, POM Analysis and Computational Approaches of Some Glucoside Derivatives Bearing Acyl Moieties. Saudi Pharmaceutical Journal, 31, Article ID: 101804. https://doi.org/10.1016/j.jsps.2023.101804
Alves Lourenço, B.L., Araújo Santos Silva, M.V., de Oliveira, E.B., de Assis Soares, W.R., Góes-Neto, A., Santos, G., et al. (2015) Virtual Screening and Molecular Docking for Arylalkylamine-N-Acetyltransferase (aaNAT) Inhibitors, a Key Enzyme of Aedes (Stegomyia) Aegypti (L.) Metabolism. ComputationalMolecularBioscience, 5, 35-44. https://doi.org/10.4236/cmb.2015.53005
Hossain, M.A., Dewan, P., Kawsar, S.M.A., Dangwal, A., Kalra, K., Kalra, J.M., Ashok, P.K., Parashar, T., Jakhmola, V., Saha, S. and Ansori M.N.A. (2025) Chemical Descriptors, ADMET, Molecular Docking and Molecular Dynamics Simulation of Mannopyranoside Derivatives against Smallpox Virus Proteins. AdvancedJournalofChemistry, SectionA, 8, 1-16. https://doi.org/10.48309/ajca.2025.459071.1531
Kawsar, S.M.A., Hosen, M.A., El Bakri, Y., Ahmad, S., Affi, S.T. and Goumri-Said, S. (2022) InSilico Approach for Potential Antimicrobial Agents through Antiviral, Molecular Docking, Molecular Dynamics, Pharmacokinetic and Bioactivity Predictions of Galactopyranoside Derivatives. ArabJournalofBasicandAppliedSciences, 29, 99-112. https://doi.org/10.1080/25765299.2022.2068275