Suveena Rai,
Shubham Wanarase,
- Student, Department of Bioinformatics, BioNome, Bengaluru, Karnataka, India
- Student, Department of Bioinformatics, BioNome, Bengaluru, Karnataka, India
Abstract
Objective: Alzheimer’s disease, an age-related progressive neurological condition, arises due to the accumulation of amyloid-beta protein within the brain. In this study, an attempt was made to explore the potential of natural compounds derived from Ginkgo, known for their diverse medicinal properties, in the prevention of the disorder by employing molecular docking techniques, conducting drug-likeness prediction assessments, and performing ADME analysis. Methods: Amyloid beta protein was retrieved from the PDB database. The ligands present on the leaf of the Ginkgo biloba plant were chosen based on previously existing studies in this domain. The compounds that had the potential to disrupt docking interactions and the ligands exhibiting weak binding affinity were eliminated. Subsequently, docking calculations were conducted using the PyRx tool. To assess the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, as well as druglikeness, an ADME and drug-likeness analysis was carried out employing the Swiss-ADME and ADMET Lab web server. Results: According to the docking results, Amentoflavone, Ginkgetin, Sciadopitysin, Bilobetin, Isoginkgetin, Kaempferol, and 6-Hydroxykynurenic acid had the highest binding affinities. Moreover, analysis of the ADMET profile and drug-likeness prediction revealed that Kaempferol and 6-Hydroxykynurenic acid were safe and possessed drug-like properties among these seven compounds. Conclusion: The present study suggests that Kaempferol and 6- Hydroxykynurenic acid have specific binding affinity and they could be effective against the amyloid beta protein. Also, these compounds can be used in therapeutic strategies against Alzheimer’s disease.
Keywords: Amyloid-beta protein, alzheimer’s disease, ginko biloba, molecular docking, ADME
[This article belongs to International Journal of Cell Biology and Cellular Functions (ijcbcf)]
Suveena Rai, Shubham Wanarase. In Silico Analysis and Docking Study of the Active Phyto Compounds of Ginkgo Biloba Against Alzheimer’s Amyloid-Beta Protein. International Journal of Cell Biology and Cellular Functions. 2023; 01(02):-.
Suveena Rai, Shubham Wanarase. In Silico Analysis and Docking Study of the Active Phyto Compounds of Ginkgo Biloba Against Alzheimer’s Amyloid-Beta Protein. International Journal of Cell Biology and Cellular Functions. 2023; 01(02):-. Available from: https://journals.stmjournals.com/ijcbcf/article=2023/view=117757
References
- Cheng, P. N., Liu, C., Zhao, M., Eisenberg, D., & Nowick, J. S. (2012). Amyloid β-sheet mimics antagonize protein aggregation and reduce amyloid toxicity. Nature chemistry, 4(11), 927–933.
- Chiti, F., & Dobson, C. M. (2006). Protein misfolding, functional amyloid, and human disease. Annual review of biochemistry, 75, 333–366.
- Aguzzi, A., & O’Connor, T. (2010). Protein aggregation diseases: pathogenicity and therapeutic perspectives. Nature reviews. Drug discovery, 9(3), 237–248.
- Bartolini, M., & Andrisano, V. (2010). Strategies for the inhibition of protein aggregation in human diseases. Chembiochem: a European journal of chemical biology, 11(8), 1018–1035.
- Oh, S. J., Lee, N., Nam, K. R., Kang, K. J., Han, S. J., Lee, K. C., Lee, Y. J., & Choi, J. Y. (2022). Amyloid pathology induces dysfunction of systemic neurotransmission in aged APPswe/PS2 mice. In Frontiers in Neuroscience (Vol. 16). Frontiers Media SA.
- Stone, J., Johnstone, D. M., Mitrofanis, J., & O’Rourke, M. (2015). The mechanical cause of age-related dementia (Alzheimer’s disease): the brain is destroyed by the pulse. Journal of Alzheimer’s disease : JAD, 44(2), 355–373.
- Hardy, J., & Selkoe, D. J. (2002). The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics. In Science (Vol. 297, Issue 5580, pp. 353–356). American Association for the Advancement of Science (AAAS).
- Greenwald, J., & Riek, R. (2010). Biology of amyloid: structure, function, and regulation. Structure (London, England: 1993), 18(10), 1244–1260.
- Eichner, T., & Radford, S. E. (2011). A diversity of assembly mechanisms of a generic amyloid fold. Molecular cell, 43(1), 8–18.
- Diociaiuti, M., Bonanni, R., Cariati, I., Frank, C., & D’Arcangelo, G. (2021). Amyloid Prefibrillar Oligomers: The Surprising Commonalities in Their Structure and Activity. International journal of molecular sciences, 22(12), 6435.
- De-Paula, V. J., Radanovic, M., Diniz, B. S., & Forlenza, O. V. (2012). Alzheimer’s Disease. In Protein Aggregation and Fibrillogenesis in Cerebral and Systemic Amyloid Disease (pp. 329–352).
- Arai, H., Ishiguro, K., Ohno, H., Moriyama, M., Itoh, N., Okamura, N., Matsui, T., Morikawa, Y., Horikawa, E., Kohno, H., Sasaki, H., & Imahori, K. (2000). CSF phosphorylated tau protein and mild cognitive impairment: a prospective study. Experimental neurology, 166(1), 201–203.
- Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., ChĂ©telat, G., Teunissen, C. E., Cummings, J., & van der Flier, W. M. (2021). Alzheimer’s disease. Lancet (London, England), 397(10284), 1577–1590.
- Buée, L., Bussière, T., Buée-Scherrer, V., Delacourte, A., & Hof, P. R. (2000). Tau protein isoforms, phosphorylation and role in neurodegenerative disorders. Brain research. Brain research reviews, 33(1), 95–130.
- Shahidi, S, Ghahremanitamadon, F., Soleimani Asl, S., Komaki, A., Afshar, S., & Hashemi-Firouzi, N. (2021). Electrophysiological, Behavioral and Molecular Study of Vitamin E and Ginkgo biloba in a Rat Model of Alzheimer’s Disease. Research Journal of Pharmacognosy, 8(1).
- Tatulian S. A. (2022). Challenges and hopes for Alzheimer’s disease. Drug discovery today, 27(4), 1027–1043.
- Song, C., Shi, J., Zhang, P., Zhang, Y., Xu, J., Zhao, L., Zhang, R., Wang, H., & Chen, H. (2022). Immunotherapy for Alzheimer’s disease: targeting β-amyloid and beyond. In Translational Neurodegeneration (Vol. 11, Issue 1). Springer Science and Business Media LLC.
- Rowley, P. A., Samsonov, A. A., Betthauser, T. J., Pirasteh, A., Johnson, S. C., & Eisenmenger, L. B. (2020). Amyloid and Tau PET Imaging of Alzheimer Disease and Other Neurodegenerative Conditions. Seminars in ultrasound, CT, and MR, 41(6), 572–583.
- Blasko, I., Jungwirth, S., Jellinger, K., Kemmler, G., Krampla, W., Weissgram, S., Wichart, I., Tragl, K. H., Hinterhuber, H., & Fischer, P. (2008). Effects of medications on plasma amyloid beta (Abeta) 42: longitudinal data from the VITA cohort. Journal of psychiatric research, 42(11), 946–955.
- Birks, J., & Grimley Evans, J. (2009). Ginkgo biloba for cognitive impairment and dementia. The Cochrane database of systematic reviews, (1), CD003120.
- Canevelli, M., Adali, N., Kelaiditi, E., Cantet, C., Ousset, P. J., Cesari, M., & ICTUS/DSA Group (2014). Effects of Ginkgo biloba supplementation in Alzheimer’s disease patients receiving cholinesterase inhibitors: data from the ICTUS study. Phytomedicine : international journal of phytotherapy and phytopharmacology, 21(6), 888–892.
- Diniz, W. J., & Canduri, F. (2017). REVIEW-ARTICLE Bioinformatics: an overview and its applications. Genetics and molecular research : GMR, 16(1), 10.4238/gmr16019645.
- Jongejan, A., de Graaf, C., Vermeulen, N. P., Leurs, R., & de Esch, I. J. (2005). The role and application of in silico docking in chemical genomics research. Methods in molecular biology (Clifton, N.J.), 310, 63–91.
- McConkey, B.J., Sobolev, V., & Edelman, M. (2002). The performance of current methods in ligand-protein docking. Current Science, 83, 845-856.
- Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of computer-aided molecular design, 27(3), 221–234.
- Zhao, M., Liu, C., Cheng, P. N., Eisenberg, D., & Nowick, J. S. (2012). AIIGLMV segment from Alzheimer’s Amyloid-Beta displayed on 54-membered macrocycle scaffold. Worldwide Protein Data Bank.
- Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The Protein Data Bank. Nucleic acids research, 28(1), 235–242.
- Hollingsworth, S. A., & Karplus, P. A. (2010). A fresh look at the Ramachandran plot and the occurrence of standard structures in proteins. Biomolecular concepts, 1(3-4), 271–283.
- Mannige, R. V., Kundu, J., & Whitelam, S. (2016). The Ramachandran Number: An Order Parameter for Protein Geometry. PloS one, 11(8), e0160023.
- Anderson, R. J., Weng, Z., Campbell, R. K., & Jiang, X. (2005). Main-chain conformational tendencies of amino acids. In Proteins: Structure, Function, and Bioinformatics (Vol. 60, Issue 4, pp. 679–689). Wiley.
- Carugo, O., & Djinović-Carugo, K. (2013). A proteomic Ramachandran plot (PRplot). Amino acids, 44(2), 781–790.
- Sheik, S. S., Sundararajan, P., Hussain, A. S., & Sekar, K. (2002). Ramachandran plot on the web. Bioinformatics (Oxford, England), 18(11), 1548–1549.
- Williams, C. J., Headd, J. J., Moriarty, N. W., Prisant, M. G., Videau, L. L., Deis, L. N., Verma, V., Keedy, D. A., Hintze, B. J., Chen, V. B., Jain, S., Lewis, S. M., Arendall, W. B., 3rd, Snoeyink, J., Adams, P. D., Lovell, S. C., Richardson, J. S., & Richardson, D. C. (2018). MolProbity: More and better reference data for improved all-atom structure validation. Protein science: a publication of the Protein Society, 27(1), 293–315.
- Chen, V. B., Arendall, W. B., 3rd, Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S., & Richardson, D. C. (2010). MolProbity: all-atom structure validation for macromolecular crystallography. Acta crystallographica. Section D, Biological crystallography, 66(Pt 1), 12–21.
- Mohanraj, K., Karthikeyan, B. S., Vivek-Ananth, R. P., Chand, R. P. B., Aparna, S. R., Mangalapandi, P., & Samal, A. (2018). IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics. Scientific reports, 8(1), 4329.
- Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2021). PubChem in 2021: new data content and improved web interfaces. Nucleic acids research, 49(D1), D1388–D1395.
- Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., & Bryant, S. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. In Nucleic Acids Research (Vol. 37, Issue Web Server, pp. W623–W633). Oxford University Press (OUP).
- O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of cheminformatics, 3, 33.
- Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455–461.
- Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nature protocols, 11(5), 905–919.
- Dallakyan, S., & Olson, A. J. (2015). Small-molecule library screening by docking with PyRx. Methods in molecular biology (Clifton, N.J.), 1263, 243–250.
- van de Waterbeemd, H., & Gifford, E. (2003). ADMET in silico modelling: towards prediction paradise?. Nature reviews. Drug discovery, 2(3), 192–204.
- Wikimedia Foundation. (2022, May 14). ADME. Wikipedia. Retrieved December 23, 2022, from https://en.wikipedia.org/wiki/ADME
- Balani, S. K., Miwa, G. T., Gan, L. S., Wu, J. T., & Lee, F. W. (2005). Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection. Current topics in medicinal chemistry, 5(11), 1033–1038.
- Pires, D. E. V., Kaminskas, L. M., & Ascher, D. B. (2018). Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand. Methods in molecular biology (Clifton, N.J.), 1762, 271–284.
- Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic acids research, 49(W1), W5–W14.
- Lagorce, D., Bouslama, L., Becot, J., Miteva, M. A., & Villoutreix, B. O. (2017). FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics (Oxford, England), 33(22), 3658–3660.
- Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the Rule of 5 and drugability. Advanced drug delivery reviews, 101, 89–98.
- Singh S. S. (2006). Preclinical pharmacokinetics: an approach towards safer and efficacious drugs. Current drug metabolism, 7(2), 165–182.
- Daina, A., & Zoete, V. (2016). A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem, 11(11), 1117–1121.
- National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5318569, Isoginkgetin. Retrieved December 23, 2022 from https://pubchem.ncbi.
nih.gov/compound/Isoginkgetin. - National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5271805, Ginkgetin. Retrieved December 23, 2022 from https://pubchem.ncbi.nlm.nih.gov/
compound/Ginkgetin. - National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5281696, Sciadopitysin. Retrieved December 23, 2022 from https://pubchem.ncbi.nlm.nih.gov/
compound/Sciadopitysin. - National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5281600, Amentoflavone. Retrieved December 23, 2022 from https://pubchem.ncbi.n
nih.gov/compound/Amentoflavone. - National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5315459, Bilobetin. Retrieved December 23, 2022 from https://pubchem.ncbi.nlm.nih.gov/
compound/Bilobetin. - National Center for Biotechnology Information (2023). PubChem Compound Summary for CID 440752, 6-Hydroxykynurenic acid. Retrieved December 23, 2022 from https://pubchem.ncbi.
nih.gov/compound/6-Hydroxykynurenic-acid. - National Center for Biotechnology Information (2023). PubChem Compound Summary for CID 5280863, Kaempferol. Retrieved December 23, 2022 from https://pubchem.ncbi.nlm.nih.gov/
compound/Kaempferol. - Alexander, N., Woetzel, N., & Meiler, J. (2011). Bcl::Cluster: A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System. In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE.
- Rigsby, R. E., & Parker, A. B. (2016). Using the PyMOL application to reinforce visual understanding of protein structure. Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology, 44(5), 433–437.
- Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports, 7, 42717.
- Klafki, H.-W., Staufenbiel, M., Kornhuber, J., & Wiltfang, J. (2006). Therapeutic approaches to Alzheimer’s disease. In Brain (Vol. 129, Issue 11, pp. 2840–2855). Oxford University Press (OUP). https://doi.org/10.1093/brain/awl280
- Han, B. H., Cofell, B., Everhart, E., Humpal, C., Kang, S. S., Lee, S. K., & Kim-Han, J. S. (2022). Amentoflavone Promotes Cellular Uptake and Degradation of Amyloid-Beta in Neuronal Cells. International journal of molecular sciences, 23(11), 5885.
- Zeng, Y. Q., Wang, Y. J., & Zhou, X. F. (2016). Ginkgetin Ameliorates Neuropathological Changes in APP/PS1 Transgenical Mice Model. The journal of prevention of Alzheimer’s disease, 3(1), 24–29.
- Gu, Q., Li, Y., Chen, Y., Yao, P., & Ou, T. (2013). Sciadopitysin: active component fromTaxus chinensisfor anti-Alzheimer’s disease. In Natural Product Research (Vol. 27, Issue 22, pp. 2157–2160). Informa UK Limited.
- Sirimangkalakitti, N., Juliawaty, L. D., Hakim, E. H., Waliana, I., Saito, N., Koyama, K., & Kinoshita, K. (2019). Naturally occurring biflavonoids with amyloid β aggregation inhibitory activity for development of anti-Alzheimer agents. Bioorganic & medicinal chemistry letters, 29(15), 1994–1997.
- Kobayashi, H., Murata, M., Kawanishi, S., & Oikawa, S. (2020). Polyphenols with Anti-Amyloid β Aggregation Show Potential Risk of Toxicity Via Pro-Oxidant Properties. In International Journal of Molecular Sciences (Vol. 21, Issue 10, p. 3561). MDPI AG.
- Zhang, N., Xu, H., Wang, Y., Yao, Y., Liu, G., Lei, X., Sun, H., Wu, X., & Li, J. (2020). Protective mechanism of kaempferol against Aβ25-35-mediated apoptosis of pheochromocytoma (PC-12) cells through the ER/ERK/MAPK signalling pathway. Archives of medical science : AMS, 17(2), 406–416.
Volume | 01 |
Issue | 02 |
Received | 04/06/2023 |
Accepted | 14/08/2023 |
Published | 13/09/2023 |