In Silico Evaluation of Herbicide Synergism to Identify Effective Mixtures for Weed Management in Indonesia
Article Sidebar
Main Article Content
Abstract
This study presents a comprehensive approach to evaluating herbicide synergism through in silico molecular docking analysis combined with physical observations of herbicide mixtures. The research investigated nine commonly used herbicides in Indonesia, examining their potential synergistic and antagonistic interactions when mixed. Molecular docking analysis was performed using PyRx software to evaluate the interactions between herbicide active compounds and their target proteins. The analysis revealed twelve potentially synergistic combinations, with the clomazone-paraquat mixture emerging as the most promising based on both molecular docking results and compliance with Lipinski's rule of five. Physical observations in simulated tank mix conditions validated the computational predictions, showing consistent results with the in silico analysis. The study demonstrated that synergistic combinations maintained ligand interactions with their respective target proteins while showing favorable physicochemical properties for cellular penetration. The integration of computational methods with experimental validation provided valuable insights into the complex interactions between herbicide active compounds and their target proteins. This research establishes a robust framework for evaluating herbicide combinations, potentially leading to more effective and sustainable weed management strategies in agricultural practices.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
G. F. Barbieri et al., “Herbicide mixtures: interactions and modeling,” Advances in Weed Science, vol. 40, no. spe1, pp. 1–25, 2022, doi: 10.51694/advweedsci/2022;40:seventy-five011.
D. Jardim Amorim, A. M. Corrêa Vieira, C. R. Fidelis, J. C. Camilo dos Santos, M. de Almeida Silva, and C. Garcia Borges Demétrio, “Modeling hormesis using multivariate nonlinear regression in plant biology: A comprehensive approach to understanding dose-response relationships,” Science of The Total Environment, vol. 905, p. 167041, 2023, doi: https://doi.org/10.1016/j.scitotenv.2023.167041.
L. V. Madden and P. S. Ojiambo, “The value of generalized linear mixed models for data analysis in the plant sciences,” Frontiers in Horticulture, vol. 3, no. June, pp. 1–26, 2024, doi: 10.3389/fhort.2024.1423462.
F. Ndikuryayo and W.-C. Yang, “New Insights into the Interactions between Herbicides: Trends from Recent Studies,” Journal of Agricultural and Food Chemistry, vol. 71, no. 29, pp. 10970–10981, Jul. 2023, doi: 10.1021/acs.jafc.3c03781.
K. V. Sukhoverkov and J. S. Mylne, “Systematic, small-scale screening with Arabidopsis reveals herbicides synergies that extend to lettuce,” Pest Management Science, vol. 77, no. 11, pp. 4930–4941, 2021, doi: 10.1002/ps.6533.
V. A. S. Ayyadurai and P. Deonikar, “In Silico Modeling and Quantification of Synergistic Effects of Multi-Combination Compounds: Case Study of the Attenuation of Joint Pain Using a Combination of Phytonutrients,” Applied Sciences (Switzerland), vol. 12, no. 19, 2022, doi: 10.3390/app121910013.
S. Akash et al., “A drug design strategy based on molecular docking and molecular dynamics simulations applied to development of inhibitor against triple-negative breast cancer by Scutellarein derivatives,” PLoS ONE, vol. 18, no. 10 October, pp. 1–23, 2023, doi: 10.1371/journal.pone.0283271.
M. El fadili et al., “In-silico screening based on molecular simulations of 3,4-disubstituted pyrrolidine sulfonamides as selective and competitive GlyT1 inhibitors,” Arabian Journal of Chemistry, vol. 16, no. 10, p. 105105, 2023, doi: 10.1016/j.arabjc.2023.105105.
Y. Pan, H. Ren, L. Lan, Y. Li, and T. Huang, “Review of Predicting Synergistic Drug Combinations,” Life, vol. 13, no. 9, pp. 1–18, 2023, doi: 10.3390/life13091878.
C. J. Ononamadu and A. Ibrahim, “Molecular docking and prediction of ADME/drug-likeness properties of potentially active antidiabetic compounds isolated from aqueous-methanol extracts of Gymnema sylvestre and Combretum micranthum,” Biotechnologia, vol. 102, no. 1, pp. 85–99, 2021, doi: 10.5114/bta.2021.103765.
P. Panyatip, N. Nunthaboot, and P. Puthongking, “In Silico ADME, Metabolism Prediction and Hydrolysis Study of Melatonin Derivatives,” International Journal of Tryptophan Research, vol. 13, pp. 0–6, 2020, doi: 10.1177/1178646920978245.
P. Alov et al., “In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development,” International Journal of Molecular Sciences, vol. 23, no. 21, 2022, doi: 10.3390/ijms232113650.
L. Vinhoven, F. Stanke, S. Hafkemeyer, and M. M. Nietert, “Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis,” International Journal of Molecular Sciences, vol. 23, no. 20, 2022, doi: 10.3390/ijms232012351.
J. Rani, A. Bhargav, F. I. Khan, S. Ramachandran, D. Lai, and U. Bajpai, “In silico prediction of natural compounds as potential multi-target inhibitors of structural proteins of SARS-CoV-2,” Journal of Biomolecular Structure and Dynamics, vol. 40, no. 22, pp. 12118–12134, 2022, doi: 10.1080/07391102.2021.1968497.
J. Jumper et al., “Highly accurate protein structure prediction with AlphaFold,” Nature, vol. 596, no. 7873, pp. 583–589, 2021, doi: 10.1038/s41586-021-03819-2.
B. Webb and A. Sali, “Comparative Protein Structure Modeling Using MODELLER.,” Current protocols in bioinformatics, vol. 54, pp. 5.6.1-5.6.37, Jun. 2016, doi: 10.1002/cpbi.3.
S. Dallakyan and A. J. Olson, “Small-molecule library screening by docking with PyRx.,” Methods in molecular biology (Clifton, N.J.), vol. 1263, pp. 243–250, 2015, doi: 10.1007/978-1-4939-2269-7_19.
D. Iqbal et al., “Pharmacophore-Based Screening, Molecular Docking, and Dynamic Simulation of Fungal Metabolites as Inhibitors of Multi-Targets in Neurodegenerative Disorders,” Biomolecules, vol. 13, no. 11, 2023, doi: 10.3390/biom13111613.
K. Schöning-Stierand et al., “ProteinsPlus: a comprehensive collection of web-based molecular modeling tools,” Nucleic Acids Research, vol. 50, no. W1, pp. W611–W615, 2022, doi: 10.1093/nar/gkac305.
C. A. Lipinski, “Lead- and drug-like compounds: the rule-of-five revolution,” Drug Discovery Today: Technologies, vol. 1, no. 4, pp. 337–341, 2004, doi: https://doi.org/10.1016/j.ddtec.2004.11.007.
B. Jayaram, T. Singh, G. Mukherjee, A. Mathur, S. Shekhar, and V. Shekhar, “Sanjeevini: a freely accessible web-server for target directed lead molecule discovery.,” BMC bioinformatics, vol. 13 Suppl 1, no. Suppl 17, 2012, doi: 10.1186/1471-2105-13-S17-S7.
C. Shen, J. Ding, Z. Wang, D. Cao, X. Ding, and T. Hou, “From machine learning to deep learning: Advances in scoring functions for protein–ligand docking,” WIREs Computational Molecular Science, vol. 10, no. 1, p. e1429, Jan. 2020, doi: https://doi.org/10.1002/wcms.1429.
P. Rani, B. K. Rajak, and D. V. Singh, “Physicochemical parameters for design and development of lead herbicide molecules: Is ‘Lipinski’s rule of 5′ appropriate for herbicide discovery?,” Pest Management Science, vol. 79, no. 5, pp. 1931–1943, May 2023, doi: https://doi.org/10.1002/ps.7367.
S. Moon, W. Zhung, and W. Y. Kim, “Toward generalizable structure-based deep learning models for protein–ligand interaction prediction: Challenges and strategies,” WIREs Computational Molecular Science, vol. 14, no. 1, p. e1705, Jan. 2024, doi: https://doi.org/10.1002/wcms.1705.
A. Berestetskiy, “Modern Approaches for the Development of New Herbicides Based on Natural Compounds,” Plants, vol. 12, no. 2, 2023, doi: 10.3390/plants12020234.
H. Nada, A. Elkamhawy, and K. Lee, “Identification of 1H-purine-2, 6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations,” PeerJ, vol. 10, pp. 1–30, 2022, doi: 10.7717/peerj.14120.
P. E. Hulme, “Weed resistance to different herbicide modes of action is driven by agricultural intensification,” Field Crops Research, vol. 292, no. December 2022, p. 108819, 2023, doi: 10.1016/j.fcr.2023.108819.
G. M. Morris et al., “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility,” Journal of Computational Chemistry, vol. 30, no. 16, pp. 2785–2791, Dec. 2009, doi: https://doi.org/10.1002/jcc.21256.
Y. Wang, W. Liu, B. Dong, D. Wang, Y. Nian, and H. Zhou, “Isolation and Identification of Herbicidal Active Compounds from Brassica oleracea L. and Exploration of the Binding Sites of Brassicanate A Sulfoxide,” Plants, vol. 12, no. 13, 2023, doi: 10.3390/plants12132576.
Y. Yi et al., “A general strategy for protein affinity-ligand oriented-immobilization and screening for bioactive compounds,” Journal of Chromatography B, vol. 1218, p. 123591, 2023, doi: https://doi.org/10.1016/j.jchromb.2023.123591.
Y. Fu et al., “Combination of Virtual Screening Protocol by in Silico toward the Discovery of Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors,” Frontiers in Chemistry, vol. 6, no. February, pp. 1–15, 2018, doi: 10.3389/fchem.2018.00014.
Y. Fu et al., “Design, synthesis, and herbicidal activity evaluation of novel aryl-naphthyl methanone derivatives,” Frontiers in Chemistry, vol. 7, no. JAN, pp. 1–10, 2019, doi: 10.3389/fchem.2019.00002.
F. Ndikuryayo, B. Moosavi, W.-C. Yang, and G.-F. Yang, “4-Hydroxyphenylpyruvate Dioxygenase Inhibitors: From Chemical Biology to Agrochemicals,” Journal of Agricultural and Food Chemistry, vol. 65, no. 39, pp. 8523–8537, Oct. 2017, doi: 10.1021/acs.jafc.7b03851.
C. Yang, E. A. Chen, and Y. Zhang, “Protein–Ligand Docking in the Machine-Learning Era,” Molecules, vol. 27, no. 14, pp. 1–24, 2022, doi: 10.3390/molecules27144568.
W. Chen, H. He, J. Wang, J. Wang, and C. E. A. Chang, “Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics,” Physical Chemistry Chemical Physics, vol. 25, no. 3, pp. 2098–2109, 2022, doi: 10.1039/d2cp04584b.
W. Xiao, D. Wang, Z. Shen, S. Li, and H. Li, “Multi-body interactions in molecular docking program devised with key water molecules in protein binding sites,” Molecules, vol. 23, no. 9, 2018, doi: 10.3390/molecules23092321.
E. Zacharioudakis and E. Gavathiotis, “Targeting protein conformations with small molecules to control protein complexes,” Trends in Biochemical Sciences, vol. 47, no. 12, pp. 1023–1037, 2022, doi: 10.1016/j.tibs.2022.07.002.
Y. Chang, B. A. Hawkins, J. J. Du, P. W. Groundwater, D. E. Hibbs, and F. Lai, “A Guide to In Silico Drug Design,” Pharmaceutics, vol. 15, no. 1, 2023, doi: 10.3390/pharmaceutics15010049.
M. Naveed et al., “Comparative analysis among the degradation potential of enzymes obtained from Escherichia coli against the toxicity of sulfur dyes through molecular docking,” Zeitschrift fur Naturforschung - Section C Journal of Biosciences, vol. 79, no. 7–8, pp. 221–234, 2024, doi: 10.1515/znc-2024-0072.
P. C. Agu et al., “Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management,” Scientific Reports, vol. 13, no. 1, pp. 1–18, 2023, doi: 10.1038/s41598-023-40160-2.
B. Fauber, “Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models,” no. Figure 1, pp. 1–23, 2024.
H. Wang, “Prediction of protein-ligand binding affinity via deep learning models,” Briefings in Bioinformatics, vol. 25, no. 2, 2024, doi: 10.1093/bib/bbae081.
A. Dhakal, C. McKay, J. J. Tanner, and J. Cheng, “Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions,” Briefings in Bioinformatics, vol. 23, no. 1, pp. 1–23, 2022, doi: 10.1093/bib/bbab476.
Y. Zhang, S. Li, K. Meng, and S. Sun, “Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction,” Journal of Chemical Information and Modeling, vol. 64, no. 5, pp. 1456–1472, Mar. 2024, doi: 10.1021/acs.jcim.3c01841.
L. Zhao, Y. Zhu, J. Wang, N. Wen, C. Wang, and L. Cheng, “A brief review of protein–ligand interaction prediction,” Computational and Structural Biotechnology Journal, vol. 20, pp. 2831–2838, 2022, doi: 10.1016/j.csbj.2022.06.004.
K. Schöning-Stierand et al., “ProteinsPlus: Interactive analysis of protein–ligand binding interfaces,” Nucleic Acids Research, vol. 48, no. W1, pp. W48–W53, 2020, doi: 10.1093/NAR/GKAA235.
S. Rampogu, G. Lee, J. S. Park, K. W. Lee, and M. O. Kim, “Molecular Docking and Molecular Dynamics Simulations Discover Curcumin Analogue as a Plausible Dual Inhibitor for SARS-CoV-2,” 2022.
T. Sultana, J. Tasnim, W. Hossain, and M. Liton, “Informatics in Medicine Unlocked Physicochemical and toxicological studies of some commonly used triazine-based herbicides ; In-silico approach,” Informatics in Medicine Unlocked, vol. 42, no. September, p. 101378, 2023, doi: 10.1016/j.imu.2023.101378.
Y. S. Low, M. D. Garcia, T. Lonhienne, J. A. Fraser, G. Schenk, and L. W. Guddat, “Triazolopyrimidine herbicides are potent inhibitors of Aspergillus fumigatus acetohydroxyacid synthase and potential antifungal drug leads,” Scientific Reports, pp. 1–12, 2021, doi: 10.1038/s41598-021-00349-9.
G. A. Abdelkader and J.-D. Kim, “Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.,” Current drug targets, Sep. 2024, doi: 10.2174/0113894501330963240905083020.
E. M. Terefe and A. Ghosh, “Molecular Docking, Validation, Dynamics Simulations, and Pharmacokinetic Prediction of Phytochemicals Isolated From Croton dichogamus Against the HIV-1 Reverse Transcriptase,” Bioinformatics and Biology Insights, vol. 16, 2022, doi: 10.1177/11779322221125605.
J. Schiebel et al., “Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes,” Nature Communications, vol. 9, no. 1, p. 3559, 2018, doi: 10.1038/s41467-018-05769-2.
Y. Wang, D. Wang, B. Dong, J. Hao, X. Jia, and H. Zhou, “Potential Candidate Molecule of Photosystem II Inhibitor Herbicide—Brassicanate A Sulfoxide,” International Journal of Molecular Sciences, vol. 25, no. 4, 2024, doi: 10.3390/ijms25042400.
T. A. Gaines et al., “Mechanisms of evolved herbicide resistance,” Journal of Biological Chemistry, vol. 295, no. 30, pp. 10307–10330, 2020, doi: 10.1074/jbc.REV120.013572.
C. J. Meyer, J. K. Norsworthy, and G. R. Kruger, “Antagonism in mixtures of glufosinate + glyphosate and glufosinate + clethodim on grasses,” Weed Technology, vol. 35, no. 1, pp. 12–21, 2021, doi: DOI: 10.1017/wet.2020.49.
O. V. Martin, “Synergistic effects of chemical mixtures: How frequent is rare?,” Current Opinion in Toxicology, vol. 36, no. August, p. 100424, 2023, doi: 10.1016/j.cotox.2023.100424.
R. Busi and H. J. Beckie, “Are herbicide mixtures unaffected by resistance? A case study with Lolium rigidum,” Weed Research, vol. 61, no. 2, pp. 92–99, Apr. 2021, doi: https://doi.org/10.1111/wre.12453.
L. Bianchi et al., “Effects of glyphosate and clethodim alone and in mixture in sourgrass (Digitaria insularis),” Crop Protection, vol. 138, p. 105322, 2020, doi: https://doi.org/10.1016/j.cropro.2020.105322.
F. E. Dayan, “Current Status and Future Prospects in Herbicide Discovery,” Plants, vol. 8, no. 9. 2019. doi: 10.3390/plants8090341.
K. V. Sukhoverkov et al., “Improved herbicide discovery using physico-chemical rules refined by antimalarial library screening,” RSC Advances, vol. 11, no. 15, pp. 8459–8467, 2021, doi: 10.1039/d1ra00914a.