Computational Chemistry in Plant Biology: Unraveling the Molecular Basis of Plant-Drug Interactions and Biochemical Pathways
Main Article Content
Keywords
Computational chemistry, Plant biology, Plant-drug interactions, Biochemical pathways, Drug discovery
Abstract
Computational chemistry plays a crucial role in advancing our understanding of plant biology by unraveling the molecular basis of plant-drug interactions and biochemical pathways. This paper provides an overview of the applications of computational chemistry methods in plant biology research, focusing on the analysis of plant-drug interactions and biochemical pathways. In the introduction, the importance of understanding plant biology and chemistry is highlighted, emphasizing the significance of studying plant-drug interactions and biochemical pathways. Computational chemistry methods offer powerful tools to investigate these complex processes, providing insights that are challenging to obtain through traditional experimental approaches. The first section explores plant-drug interactions, discussing their relevance and showcasing case studies where computational chemistry techniques such as molecular docking and molecular dynamics simulations have been employed to study these interactions. Examples of successful applications of computational chemistry in elucidating plant-drug interactions demonstrate the potential of these methods in advancing our knowledge in this area. In the second section, the focus shifts to biochemical pathways in plants. The significance of understanding these pathways is discussed, along with descriptions of computational chemistry methods such as quantum mechanics and molecular mechanics that are used to study biochemical pathways. Case studies illustrating the application of computational chemistry approaches in unraveling biochemical pathways in plants further underscore the utility of these methods. The final section delves into the applications and future directions of computational chemistry in plant biology, highlighting the potential for drug discovery and crop improvement. Future research directions, as well as challenges and limitations associated with computational chemistry approaches in plant biology, are also addressed. Overall, this paper underscores the valuable contributions of computational chemistry in elucidating plant-drug interactions and biochemical pathways, paving the way for innovative research and applications in plant biology.
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Liu, W. and Stewart, C. (2015). Plant synthetic biology. Trends in Plant Science, 20(5), 309-317. https://doi.org/10.1016/j.tplants.2015.02.004
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Fasinu, P., Bouic, P., & Rosenkranz, B. (2012). An overview of the evidence and mechanisms of herb–drug interactions. Frontiers in Pharmacology, 3. https://doi.org/10.3389/fphar.2012.00069
Fugh‐Berman, A. and Ernst, E. (2001). Herb–drug interactions: review and assessment of report reliability. British Journal of Clinical Pharmacology, 52(5), 587-595. https://doi.org/10.1046/j.0306-5251.2001.01469.x
Pezzani, R., Salehi, B., Vitalini, S., Iriti, M., Zúñiga, F., Sharifi‐Rad, J., … & Martins, N. (2019). Synergistic effects of plant derivatives and conventional chemotherapeutic agents: an update on the cancer perspective. Medicina, 55(4), 110. https://doi.org/10.3390/medicina55040110
Wink, M. (2012). Medicinal plants: a source of anti-parasitic secondary metabolites. Molecules, 17(11), 12771-12791. https://doi.org/10.3390/molecules171112771
Borah, K., Bora, K., Mallik, S., & Zhao, Z. (2022). Potential therapeutic agents on alzheimer's disease through molecular docking and molecular dynamics simulation study of plant‐based compounds. Chemistry & Biodiversity, 20(1). https://doi.org/10.1002/cbdv.202200684
Kanagavalli, U., Deboral, E., Lakshmipriya, M., Sadiq, A., & Priya, A. (2021). In silico molecular docking of anthraquinone identified from boerhavia diffusa linn against bax and bcl-2 gene. Journal of Pharmaceutical Research International, 352-359. https://doi.org/10.9734/jpri/2021/v33i57a34006
Nouadi, B., Ezaouine, A., Messal, M., Blaghen, M., Bennis, F., & Chegdani, F. (2021). Prediction of anti-covid 19 therapeutic power of medicinal moroccan plants using molecular docking. Bioinformatics and Biology Insights, 15, 117793222110091. https://doi.org/10.1177/11779322211009199
Poustforoosh, A., Hashemipour, H., Tüzün, B., Azadpour, M., Faramarz, S., Pardakhty, A., … & Nematollahi, M. (2022). The impact of d614g mutation of sars-cov-2 on the efficacy of anti-viral drugs: a comparative molecular docking and molecular dynamics study. Current Microbiology, 79(8). https://doi.org/10.1007/s00284-022-02921-6
Riswoko, A. and Helianti, I. (2022). Inhibition of sars-cov-2 proteases by medicinal plant bioactive constituents: molecular docking simulation. Iop Conference Series Earth and Environmental Science, 976(1), 012054. https://doi.org/10.1088/1755-1315/976/1/012054
Roy, M., Swargiary, A., & Daimari, M. (2022). Gas chromatography-mass spectrometry analysis and antihyperglycemic property of lindernia crustacea (l.) f. muell.. IJPS, 84(3). https://doi.org/10.36468/pharmaceutical-sciences.972
Taherkhani, A., Khodadadi, P., Samie, L., Azadian, Z., & Bayat, Z. (2023). Flavonoids as strong inhibitors of mapk3: a computational drug discovery approach. International Journal of Analytical Chemistry, 2023, 1-16. https://doi.org/10.1155/2023/8899240
Wani, T., Bakheit, A., Al-Majed, A., Altwaijry, N., Baquaysh, A., Aljuraisy, A., … & Zargar, S. (2021). Binding and drug displacement study of colchicine and bovine serum albumin in presence of azithromycin using multispectroscopic techniques and molecular dynamic simulation. Journal of Molecular Liquids, 333, 115934. https://doi.org/10.1016/j.molliq.2021.115934
Fauteux, F., Rémus-Borel, W., Menzies, J., & Bélanger, R. (2005). Silicon and plant disease resistance against pathogenic fungi. Fems Microbiology Letters, 249(1), 1-6. https://doi.org/10.1016/j.femsle.2005.06.034
Mithöfer, A. and Boland, W. (2012). Plant defense against herbivores: chemical aspects. Annual Review of Plant Biology, 63(1), 431-450. https://doi.org/10.1146/annurev-arplant-042110-103854
Nisbet, R., Fisher, R., Nimmo, R., Bendall, D., Crill, P., Gallego‐Sala, A., … & Nisbet, E. (2009). Emission of methane from plants. Proceedings of the Royal Society B Biological Sciences, 276(1660), 1347-1354. https://doi.org/10.1098/rspb.2008.1731
Sugawara, S., Hishiyama, S., Jikumaru, Y., Hanada, A., Nishimura, T., Koshiba, T., … & Kasahara, H. (2009). Biochemical analyses of indole-3-acetaldoxime-dependent auxin biosynthesis in arabidopsis. Proceedings of the National Academy of Sciences, 106(13), 5430-5435. https://doi.org/10.1073/pnas.0811226106
Marchi, S., Zanella, M., Pinton, P., Crafa, S., & Boniolo, G. (2021). Mitopaths: a new logically-framed tool for visualizing multiple mitochondrial pathways. Iscience, 24(4), 102324. https://doi.org/10.1016/j.isci.2021.102324
Panitchayangkoon, G., Hayes, D., Fransted, K., Caram, J., Harel, E., Wen, J., … & Engel, G. (2010). Long-lived quantum coherence in photosynthetic complexes at physiological temperature. Proceedings of the National Academy of Sciences, 107(29), 12766-12770. https://doi.org/10.1073/pnas.1005484107
Tao, P. and Schlegel, H. (2010). A toolkit to assist oniom calculations. Journal of Computational Chemistry, 31(12), 2363-2369. https://doi.org/10.1002/jcc.21524
Marchi, S., Zanella, M., Pinton, P., Crafa, S., & Boniolo, G. (2021). Mitopaths: a new logically-framed tool for visualizing multiple mitochondrial pathways. Iscience, 24(4), 102324. https://doi.org/10.1016/j.isci.2021.102324
Panitchayangkoon, G., Hayes, D., Fransted, K., Caram, J., Harel, E., Wen, J., … & Engel, G. (2010). Long-lived quantum coherence in photosynthetic complexes at physiological temperature. Proceedings of the National Academy of Sciences, 107(29), 12766-12770. https://doi.org/10.1073/pnas.1005484107
Tao, P. and Schlegel, H. (2010). A toolkit to assist oniom calculations. Journal of Computational Chemistry, 31(12), 2363-2369. https://doi.org/10.1002/jcc.21524
Sato, H., Saito, K., & Yamazaki, M. (2019). Acceleration of mechanistic investigation of plant secondary metabolism based on computational chemistry. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.00802
Torii, K., Hagihara, S., Uchida, N., & Takahashi, K. (2018). Harnessing synthetic chemistry to probe and hijack auxin signaling. New Phytologist, 220(2), 417-424. https://doi.org/10.1111/nph.15337
Galvis, C., Méndez, L., & Kouznetsov, V. (2013). Cantharidin‐based small molecules as potential therapeutic agents. Chemical Biology & Drug Design, 82(5), 477-499. https://doi.org/10.1111/cbdd.12180
Li, G. and Lou, H. (2017). Strategies to diversify natural products for drug discovery. Medicinal Research Reviews, 38(4), 1255-1294. https://doi.org/10.1002/med.21474
Macalino, S., Gosu, V., Hong, S., & Choi, S. (2015). Role of computer-aided drug design in modern drug discovery. Archives of Pharmacal Research, 38(9), 1686-1701. https://doi.org/10.1007/s12272-015-0640-
Thomford, N., Senthebane, D., Rowe, A., Munro, D., Seele, P., Maroyi, A., … & Dzobo, K. (2018). Natural products for drug discovery in the 21st century: innovations for novel drug discovery. International Journal of Molecular Sciences, 19(6), 1578. https://doi.org/10.3390/ijms19061578
Verpoorte, R. and Alfermann, A. (2000). Metabolic engineering of plant secondary metabolism.. https://doi.org/10.1007/978-94-015-9423-3
Barnum, C., Endelman, B., & Shih, P. (2021). Utilizing plant synthetic biology to improve human health and wellness. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.691462
Küken, A. and Nikoloski, Z. (2019). Computational approaches to design and test plant synthetic metabolic pathways. Plant Physiology, 179(3), 894-906. https://doi.org/10.1104/pp.18.01273
Nørskov, J., Abild‐Pedersen, F., Studt, F., & Bligaard, T. (2011). Density functional theory in surface chemistry and catalysis. Proceedings of the National Academy of Sciences, 108(3), 937-943. https://doi.org/10.1073/pnas.1006652108
O'Sullivan, A., Henrick, B., Dixon, B., Zivkovic, A., Smilowitz, J., Lemay, D., … & Schaefer, S. (2017). 21st century toolkit for optimizing population health through precision nutrition. Critical Reviews in Food Science and Nutrition, 58(17), 3004-3015. https://doi.org/10.1080/10408398.2017.1348335
Barnum, C., Endelman, B., & Shih, P. (2021). Utilizing plant synthetic biology to improve human health and wellness. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.691462
DeNies, M., Liu, A., & Schnell, S. (2020). Are the biomedical sciences ready for synthetic biology?. Biomolecular Concepts, 11(1), 23-31. https://doi.org/10.1515/bmc-2020-0003
Küken, A. and Nikoloski, Z. (2019). Computational approaches to design and test plant synthetic metabolic pathways. Plant Physiology, 179(3), 894-906. https://doi.org/10.1104/pp.18.01273
McCarthy, D. and Medford, J. (2020). Quantitative and predictive genetic parts for plant synthetic biology. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.512526
Mohammadinejad, R., Karimi, S., Iravani, S., & Varma, R. (2016). Plant-derived nanostructures: types and applications. Green Chemistry, 18(1), 20-52. https://doi.org/10.1039/c5gc01403d
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May 18, 2024
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Rabia Rehman1, Sidra Amjad2, Syed Ali Raza Shah3, Noreen Ashfaq4, Iram Saba5, Naila Riaz6, Kazim Ali7, Muhammad Ishtiaq8. (2024). Computational Chemistry in Plant Biology: Unraveling the Molecular Basis of Plant-Drug Interactions and Biochemical Pathways. Journal of Population Therapeutics and Clinical Pharmacology, 31(5), 257-270. https://doi.org/10.53555/jptcp.v31i5.6269
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Rabia Rehman1, Sidra Amjad2, Syed Ali Raza Shah3, Noreen Ashfaq4, Iram Saba5, Naila Riaz6, Kazim Ali7, Muhammad Ishtiaq8. (2024). Computational Chemistry in Plant Biology: Unraveling the Molecular Basis of Plant-Drug Interactions and Biochemical Pathways. Journal of Population Therapeutics and Clinical Pharmacology, 31(5), 257-270. https://doi.org/10.53555/jptcp.v31i5.6269