Diptendu Sarkar¹✉ and Gopal Dev Mandal²
¹Department of Microbiology, Ramakrishna Mission Vidyamandira, Belur Math, Howrah, Pin-711202, West Bengal, India
²Department of Botany, Ramsaday College, College Road, Amta, Howrah, Pin- 711401, West Bengal, India
Received: Apr 22, 2023/ Revised: May 20, 2023/ Accepted: May 29, 2023
(✉) Corresponding Author: diptendu81@gmail.com
Abstract
This research has revealed the molecular docking and dynamics study with few phytomolecules against the human tyrosinase enzyme protein in order to control hyperpigmentation and skin tone in the future. This study set out to find certain phytomolecules that have the capacity to attach to the protein model for the tyrosinase enzyme and block the enzyme’s ability to function. We took into account all nine molecules in total, coupled with a protein model of the tyrosinase enzyme, for docking, with energy ranges between -5.3 and -7.4 Kcal/mol. The greatest lowest binding energy for quercetin was -7.4 Kcal/mol. With a model protein, this molecule displayed a variety of interactions, including Van der Waals, conventional hydrogen bonds, covalent bonds, and carbon hydrogen bonds. In this interaction, 3 hydrogen bonds were discovered. The other compounds, such as kaempferol and chlorogenic acid, also demonstrated correct binding with the model tyrosinase and had -7.2 Kcal/mol energy with 3 and 5 hydrogen bonds, respectively. Quercetin, Kaempferol, and Chlorogenic acid are therefore thought to be far more potent than Benztropine and may be used in further clinical research.
Keywords: Molecular Docking, Molecular Dynamics, Hyperpigmentation, Skin Tone, Binding Energy.
References
Abels, E. R., & Breakefield, X. O. (2016). Introduction to extracellular vesicles: biogenesis, RNA cargo selection, content, release, and uptake. Cellular and molecular neurobiology, 36, 301-312
Anders, E., Koch, R., & Freunscht, P. (1993). Optimization and application of lithium parameters for PM3. Journal of computational chemistry, 14(11), 1301-1312.
Antal Jr, M. J., Leesomboon, T., Mok, W. S., & Richards, G. N. (1991). Mechanism of formation of 2-furaldehyde from D-xylose. Carbohydrate Research, 217, 71-85.
Arooj, M., Sakkiah, S., Kim, S., Arulalapperumal, V., & Lee, K. W. (2013). A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors. PLoS One, 8(4), e63030.
Azam, S. S., & Abbasi, S. W. (2013). Molecular docking studies for the identification of novel melatoninergic inhibitors for acetylserotonin-Omethyltransferase using different docking routines. Theoretical Biology and Medical Modelling, 10(1), 1-16.
Balimane, P. V., Chong, S., & Morrison, R. A. (2000). Current methodologies used for evaluation of intestinal permeability and absorption. Journal of pharmacological and toxicological methods, 44(1), 301-312.
Beaulieu, D., & Ohemeng, K. A. (1999). Patents on bacterial tRNA synthetase inhibitors: January 1996 to March 1999. Expert Opinion on Therapeutic Patents, 9(8), 1021-1028.
Beeching, N. J., Fenech, M., & Houlihan, C. F. (2014). Ebola virus disease. BMJ 349: g7348. Benet, L. Z., & Hoener, B. A. (2002). Changes in plasma protein binding have little clinical relevance. Clinical Pharmacology & Therapeutics, 71(3), 115-121.
Bissantz, C., Folkers, G., & Rognan, D. (2000). Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. Journal of medicinal chemistry, 43(25), 4759-4767.
Braga, E. J., Corpe, B. T., Marinho, M. M., & Marinho, E. S. (2016). Molecular electrostatic potential surface, HOMO–LUMO, and computational analysis of synthetic drug Rilpivirine. Int. J. Sci. Eng. Res, 7(7), 315-319.
Brehm, M. A., Bortell, R., Verma, M., Shultz, L. D., & Greiner, D. L. (2016). Humanized mice in translational immunology. Translational immunology: mechanisms and pharmacological approaches, 285-326.
Castillo-Garit, J. A., Marrero-Ponce, Y., Torrens, F., & García-Domenech, R. (2008). Estimation of ADME properties in drug discovery: predicting Caco-2 cell permeability using atom-based stochastic and non-stochastic linear indices. Journal of pharmaceutical sciences, 97(5), 1946-1976.
Dezani, T. M., Dezani, A. B., da Silva Junior, J. B., & dos Reis Serra, C. H. (2016). Single-Pass Intestinal Perfusion (SPIP) and prediction of fraction absorbed and permeability in humans: A study with antiretroviral drugs. European Journal of Pharmaceutics and Biopharmaceutics, 104, 131-139.
Gasteiger, J., & Marsili, M. (1980). Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron, 36(22), 3219-3228.
Sarkar, D., Ganguly, A. (2022). Molecular Docking Studies with Garlic Phytochemical Constituents To Inhibit The Human EGFR Protein For Lung Cancer Therapy. Int J Pharm Sci.13(2), B1-14 http://dx.doi.org/10.22376/Ijpbs.2022.13.2.b1-14
Irvine, J. D., Takahashi, L., Lockhart, K., Cheong, J., Tolan, J. W., Selick, H. E., & Grove, J. R. (1999). MDCK (Madin–Darby canine kidney) cells: a tool for membrane permeability screening. Journal of pharmaceutical sciences, 88(1), 28-33.
Khabbaz, R., Bell, B. P., Schuchat, A., Ostroff, S. M., Moseley, R., Levitt, A., & Hughes, J. M. (2015). Emerging and reemerging infectious disease threats. Mandell, Douglas, and Bennett’s principles and practice of infectious diseases, 158.
Koes, D. R. (2018). The Pharmit backend: A computer systems approach to enabling interactive online drug discovery. IBM journal of research and development, 62(6), 3-1.
Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., & Olson, A. J. (1998). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry, 19(14), 1639-1662.
Nimlos, M. R., Qian, X., Davis, M., Himmel, M. E., & Johnson, D. K. (2006). Energetics of xylose decomposition as determined using quantum mechanics modeling. The Journal of Physical Chemistry A, 110(42), 11824-11838.
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry, 30(16), 2785-2791.
Mukund, V., Behera, S. K., Alam, A., & Nagaraju, G. P. (2019). Molecular docking analysis of nuclear factor-κB and genistein interaction in the context of breast cancer. Bioinformation, 15(1) 11.
Oso, B. J., Adeoye, A. O., & Olaoye, I. F. (2020). Pharmacoinformatics and hypothetical studies on allicin, curcumin, and gingerol as potential candidates against COVID-19-associated proteases. Journal of Biomolecular Structure and Dynamics, 1-12.
Ostroff, D., McDade, J., LeDuc, J., & Hughes, J. M. (2005). Emerging and re-emerging infectious disease threats. Principles and practice of infectious disease. Philadelphia, Elsevier Churchill Livingstone, 173- 192.
Ramalingam, S., Babu, P. D. S., Periandy, S., & Fereyduni, E. (2011). Vibrational investigation, molecular orbital studies and molecular electrostatic potential map analysis on 3-chlorobenzoic acid using hybrid computational calculations. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 84(1), 210-220.
Roe, D. R., & Cheatham III, T. E. (2013). PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. Journal of chemical theory and computation, 9(7), 3084-3095.
Sarkar, D. (2022). In-silico research to screen various phytochemicals as potential therapeutics againstbeta glucan synthase enzyme from black fungus endangering COVID patients in India. Intern. J. Zool. Invest. 8(1): 320-337 https://doi.org/10.33745/ijzi.2022.v08i01.035
Rothman, R. B., Baumann, M. H., Prisinzano, T. E., & Newman, A. H. (2008). Dopamine transport inhibitors based on GBR12909 and benztropine as potential medications to treat cocaine addiction. Biochemical pharmacology, 75(1), 2-16.
Rougeron, V., Feldmann, H., Grard, G., Becker, S., & Leroy, E. M. (2015). Ebola and Marburg haemorrhagic fever. Journal of Clinical Virology, 64, 111-119.
Sarkar, D. (2021). Molecular Docking study to Identify Potent Fungal Metabolites as Inhibitors against SARS-CoV-2 Main Protease Enzyme. Int J Pharm Sci.12(2), b78-85 http://dx.doi.org/10.22376/ijpbs.2021.12.2.b78-85
Sarkar, D., Maiti, A. K. (2023). Virtual Screening and Molecular Docking Studies with Organosulfur and Flavonoid Compounds of Garlic Targeting the Estrogen Receptor Protein for the Therapy of Breast Cancer. Volume 13, Issue 1, 2023, 49 https://doi.org/10.33263/BRIAC 131.049
Sarkar, D., Maiti, A.K., Rawaf, A., Babu, J. (2022). In silico Approach to Identify Potent Bioactive Compounds as Inhibitors against the Enoylacyl Carrier Protein (acp) Reductase Enzyme of Mycobacterium tuberculosis. Volume 12, Issue 5, 7023 – 7039 https://doi.org/10.33263/BRIAC 125.70237039
Wadapurkar, R. M., Shilpa, M. D., Katti, A. K. S., & Sulochana, M. B. (2018). In silico drug design for Staphylococcus aureus and development of host-pathogen interaction network. Informatics in Medicine Unlocked, 10, 58-70.
Wenthur, C. J., Gentry, P. R., Mathews, T. P., & Lindsley, C. W. (2014). Drugs for allosteric sites on receptors. Annual review of pharmacology and toxicology, 54, 165-184.
Sarkar, D., Ahamed, S. M. (2022). Finding antagonist for the VP24 protein of the Ebola virus to treat infections using molecular docking and molecular dynamics studies. Science Archives, 3(4), 289-300. https://doi.org/10.47587/SA.2022.3408
Sarkar, D. (2022). Molecular docking of putative phytochemicals in aqueous Moringa oleifera leaf extracts with three cytochrome P450 enzyme involved in xenobiotic metabolism. Science Archives, 3(4), 255-262. https://doi.org/10.47587/SA.2022.3403
How to cite this article
Sarkar, D. and Mandal, G. D. (2023). Molecular docking and dynamic study with polyphenolic constituents as inhibitors of human tyrosinase enzyme for hyperpigmentation therapy and skin complexion management. Science Archives, Vol. 4(2), 119-129. https://doi.org/10.47587/SA.2023.4209
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