Molecular docking, molecular dynamics simulation, and MM/PBSA analysis of ginger phytocompounds as a potential inhibitor of AcrB for treating multidrug-resistant Klebsiella pneumoniae infections

Reference

• Akintobi, O., Onoh, C., Ogele, J., Idowu, A., Ojo, O., & Okonko, I. (2013). Antimicrobial activity of Zingiber officinale (ginger) extract against some selected pathogenic bacteria. Nature and Science, 11(1), 7–15.
   Google Scholar
• Akullo, J. O., Kiage, B., Nakimbugwe, D., & Kinyuru, J. (2022). Effect of aqueous and organic solvent extraction on in-vitro antimicrobial activity of two varieties of fresh ginger (Zingiber officinale) and garlic (Allium sativum). Heliyon, 8(9), e10457. https://doi.org/10.1016/j.heliyon.2022.e10457
   PubMed Web of Science ®Google Scholar
• Aparna, V., Dineshkumar, K., Mohanalakshmi, N., Velmurugan, D., & Hopper, W. (2014). Identification of natural compound inhibitors for multidrug efflux pumps of Escherichia coli and Pseudomonas aeruginosa using in silico high-throughput virtual screening and in vitro validation. PloS One, 9(7), e101840. https://doi.org/10.1371/journal.pone.0101840
   PubMed Web of Science ®Google Scholar
• Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A., Pereira, J. H., Rodrigues, A. V., van Dijk, A. A., Ebrecht, A. C., … Baker, D. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science (New York, N.Y.), 373(6557), 871–876. https://doi.org/10.1126/science.abj8754
   PubMed Web of Science ®Google Scholar
• Ballén, V., Gabasa, Y., Ratia, C., Ortega, R., Tejero, M., & Soto, S. (2021). Antibiotic resistance and virulence profiles of Klebsiella pneumoniae strains isolated from different clinical sources. Frontiers in Cellular and Infection Microbiology, 11, 738223. https://doi.org/10.3389/fcimb.2021.738223
   PubMed Web of Science ®Google Scholar
• Barbieri, R., Coppo, E., Marchese, A., Daglia, M., Sobarzo-Sánchez, E., Nabavi, S. F., & Nabavi, S. M. (2017). Phytochemicals for human disease: An update on plant-derived compounds antibacterial activity. Microbiological Research, 196, 44–68. https://doi.org/10.1016/j.micres.2016.12.003
   PubMed Web of Science ®Google Scholar
• Behera, D. U., Gaur, M., Sahoo, M., Subudhi, E., & Subudhi, B. B. (2024). Development of pharmacophore models for AcrB protein and the identification of potential adjuvant candidates for overcoming efflux-mediated colistin resistance. RSC Medicinal Chemistry, https://doi.org/10.1039/D3MD00483J
   Google Scholar
• Dey, S., Gaur, M., Sahoo, R. K., Das, A., Jain, B., Pati, S., & Subudhi, E. (2020). Genomic characterization of XDR Klebsiella pneumoniae ST147 co-resistant to carbapenem and colistin—The first report in India. Journal of Global Antimicrobial Resistance, 22, 54–56. https://doi.org/10.1016/j.jgar.2020.05.005
   PubMed Web of Science ®Google Scholar
• Fair, R. J., & Tor, Y. (2014). Antibiotics and bacterial resistance in the 21st century. Perspectives in Medicinal Chemistry, 6, 25–64. https://doi.org/10.4137/PMC.S14459
   PubMedGoogle Scholar
• Filimonov, D. A., Rudik, A. V., Dmitriev, A. V., & Poroikov, V. V. (2020). Computer-aided estimation of biological activity profiles of drug-like compounds taking into account their metabolism in human body. International Journal of Molecular Sciences, 21(20), 7492. https://doi.org/10.3390/ijms21207492
   PubMed Web of Science ®Google Scholar
• Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., Shaw, D. E., Francis, P., & Shenkin, P. S. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7), 1739–1749. https://doi.org/10.1021/jm0306430
   PubMed Web of Science ®Google Scholar
• Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein − ligand complexes. Journal of Medicinal Chemistry, 49(21), 6177–6196. https://doi.org/10.1021/jm051256o
   PubMed Web of Science ®Google Scholar
• Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
   PubMed Web of Science ®Google Scholar
• Ghafoor, K., Al Juhaimi, F., Özcan, M. M., Uslu, N., Babiker, E. E., & Mohamed Ahmed, I. A. (2020). Total phenolics, total carotenoids, individual phenolics and antioxidant activity of ginger (Zingiber officinale) rhizome as affected by drying methods. LWT, 126, 109354. https://doi.org/10.1016/j.lwt.2020.109354
   Web of Science ®Google Scholar
• Huang, N., Kalyanaraman, C., Irwin, J. J., & Jacobson, M. P. (2006). Physics-based scoring of protein − ligand complexes: Enrichment of known inhibitors in large-scale virtual screening. Journal of Chemical Information and Modeling, 46(1), 243–253. https://doi.org/10.1021/ci0502855
   PubMed Web of Science ®Google Scholar
• Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J. F., Honig, B., Shaw, D. E., & Friesner, R. A. (2004). A hierarchical approach to all-atom protein loop prediction. Proteins, 55(2), 351–367. https://doi.org/10.1002/prot.10613
   PubMed Web of Science ®Google Scholar
• Jain, N., Sk, M. F., Mishra, A., Kar, P., & Kumar, A. (2022). Identification of novel efflux pump inhibitors for Neisseria gonorrhoeae via multiple ligand-based pharmacophores, e-pharmacophore, molecular docking, density functional theory, and molecular dynamics approaches. Computational Biology and Chemistry, 98, 107682. https://doi.org/10.1016/j.compbiolchem.2022.107682
   PubMed Web of Science ®Google Scholar
• Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996). Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. Journal of the American Chemical Society, 118(45), 11225–11236. https://doi.org/10.1021/ja9621760
   Web of Science ®Google Scholar
• Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., & Jorgensen, W. L. (2001). Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. The Journal of Physical Chemistry B, 105(28), 6474–6487. https://doi.org/10.1021/jp003919d
   Web of Science ®Google Scholar
• Kar, B., Dehury, B., Singh, M. K., Pati, S., & Bhattacharya, D. (2022). Identification of phytocompounds as newer antiviral drugs against COVID-19 through molecular docking and simulation based study. Journal of Molecular Graphics & Modelling, 114, 108192. https://doi.org/10.1016/j.jmgm.2022.108192
   PubMed Web of Science ®Google Scholar
• Kobylka, J., Kuth, M. S., Müller, R. T., Geertsma, E. R., & Pos, K. M. (2020). AcrB: A mean, keen, drug efflux machine. Annals of the New York Academy of Sciences, 1459(1), 38–68. https://doi.org/10.1111/nyas.14239
   PubMed Web of Science ®Google Scholar
• Kuhn, B., & Kollman, P. A. (2000). Binding of a diverse set of ligands to avidin and streptavidin: An accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. Journal of Medicinal Chemistry, 43(20), 3786–3791. https://doi.org/10.1021/jm000241h
   PubMed Web of Science ®Google Scholar
• Li, Y., Cross, T. S., & Dörr, T. (2022). Analysis of AcrB in Klebsiella pneumoniae reveals natural variants promoting enhanced multidrug resistance. Research in Microbiology, 173(3), 103901. https://doi.org/10.1016/j.resmic.2021.103901
   PubMed Web of Science ®Google Scholar
• Lokhande, K. B., Ballav, S., Thosar, N., Swamy, K. V., & Basu, S. (2020). Exploring conformational changes of PPAR-Ɣ complexed with novel kaempferol, quercetin, and resveratrol derivatives to understand binding mode assessment: A small-molecule checkmate to cancer therapy. Journal of Molecular Modeling, 26(9), 242. https://doi.org/10.1007/s00894-020-04488-0
   PubMed Web of Science ®Google Scholar
• Lokhande, K. B., Pawar, S. V., Madkaiker, S., Nawani, N., Venkateswara, S. K., & Ghosh, P. (2023). High throughput virtual screening and molecular dynamics simulation analysis of phytomolecules against BfmR of Acinetobacter baumannii: Anti-virulent drug development campaign. Journal of Biomolecular Structure & Dynamics, 41(7), 2698–2712. https://doi.org/10.1080/07391102.2022.2038271
   PubMed Web of Science ®Google Scholar
• Macalalad, M. A. B., & Gonzales, A. A. (2022). In-silico screening and identification of phytochemicals from Centella asiatica as potential inhibitors of sodium-glucose co-transporter 2 for treating diabetes. Journal of Biomolecular Structure & Dynamics, 40(22), 12221–12238. https://doi.org/10.1080/07391102.2021.1969282
   PubMed Web of Science ®Google Scholar
• Manasa, B., Manandhar, S., Hari, G., Priya, K., Kumar B, H., & Pai, K. S. R. (2021). Virtual structure-based docking, WaterMap, and molecular dynamics guided identification of the potential natural compounds as inhibitors of protein-tyrosine phosphatase 1B. Journal of Molecular Structure, 1226, 129396. https://doi.org/10.1016/j.molstruc.2020.129396
   Web of Science ®Google Scholar
• Miller, B. R., McGee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., & Roitberg, A. E. (2012). MMPBSA.py: An efficient program for end-state free energy calculations. Journal of Chemical Theory and Computation, 8(9), 3314–3321. https://doi.org/10.1021/ct300418h
   PubMed Web of Science ®Google Scholar
• Miller, E. B., Murphy, R. B., Sindhikara, D., Borrelli, K. W., Grisewood, M. J., Ranalli, F., Dixon, S. L., Jerome, S., Boyles, N. A., Day, T., Ghanakota, P., Mondal, S., Rafi, S. B., Troast, D. M., Abel, R., & Friesner, R. A. (2021). Reliable and accurate solution to the induced fit docking problem for protein–ligand binding. Journal of Chemical Theory and Computation, 17(4), 2630–2639. https://doi.org/10.1021/acs.jctc.1c00136
   PubMed Web of Science ®Google Scholar
• Mirza, S. B., Salmas, R. E., Fatmi, M. Q., & Durdagi, S. (2016). Virtual screening of eighteen million compounds against dengue virus: Combined molecular docking and molecular dynamics simulations study. Journal of Molecular Graphics & Modelling, 66, 99–107. https://doi.org/10.1016/j.jmgm.2016.03.008
   PubMed Web of Science ®Google Scholar
• Nada, H., Lee, K., Gotina, L., Pae, A. N., & Elkamhawy, A. (2022). Identification of novel discoidin domain receptor 1 (DDR1) inhibitors using E-pharmacophore modeling, structure-based virtual screening, molecular dynamics simulation and MM-GBSA approaches. Computers in Biology and Medicine, 142, 105217. https://doi.org/10.1016/j.compbiomed.2022.105217
   PubMed Web of Science ®Google Scholar
• Ni, R. T., Onishi, M., Mizusawa, M., Kitagawa, R., Kishino, T., Matsubara, F., Tsuchiya, T., Kuroda, T., & Ogawa, W. (2020). The role of RND-type efflux pumps in multidrug-resistant mutants of Klebsiella pneumoniae. Scientific Reports, 10(1), 10876. https://doi.org/10.1038/s41598-020-67820-x
   PubMed Web of Science ®Google Scholar
• Obied, H. N., Al-Zobaidy, M. A. H., & Hindi, N. K. K. (2018). An in vitro study of anti-bacterial, anti-adherence, anti-biofilm and anti-motility activities of the aqueous extracts of fresh and powdered onion (Allium cepa) and onion oil. Journal of Pharmaceutical Sciences and Research, 10(6), 1573–1578.
   Google Scholar
• Pacios, O., Fernández-García, L., Bleriot, I., Blasco, L., Ambroa, A., López, M., Ortiz-Cartagena, C., González de Aledo, M., Fernández-Cuenca, F., Oteo-Iglesias, J., Pascual, Á., Martínez-Martínez, L., & Tomás, M. (2022). Adaptation of clinical isolates of Klebsiella pneumoniae to the combination of niclosamide with the efflux pump inhibitor phenyl-arginine-β-naphthylamide (PaβN): Co-resistance to antimicrobials. The Journal of Antimicrobial Chemotherapy, 77(5), 1272–1281. https://doi.org/10.1093/jac/dkac044
   PubMed Web of Science ®Google Scholar
• Parihar, A., Ahmed, S. S., Sharma, P., Choudhary, N. K., Akter, F., Ali, M. A., Sonia, Z. F., & Khan, R. (2022). Plant-based bioactive molecules for targeting of endoribonuclease using steered molecular dynamic simulation approach: A highly conserved therapeutic target against variants of SARS-CoV-2. Molecular Simulation, 49(12), 1267–1279. https://doi.org/10.1080/08927022.2022.2113811
   Web of Science ®Google Scholar
• Park, M., Bae, J., & Lee, D. S. (2008). Antibacterial activity of [10]-gingerol and [12]-gingerol isolated from ginger rhizome against periodontal bacteria. Phytotherapy Research: PTR, 22(11), 1446–1449. https://doi.org/10.1002/ptr.2473
   PubMed Web of Science ®Google Scholar
• Pattar, S. V., Adhoni, S. A., Kamanavalli, C. M., & Kumbar, S. S. (2020). In silico molecular docking studies and MM/GBSA analysis of coumarin-carbonodithioate hybrid derivatives divulge the anticancer potential against breast cancer. Beni-Suef University Journal of Basic and Applied Sciences, 9(1), 36. https://doi.org/10.1186/s43088-020-00059-7
   Google Scholar
• Petretto, E., Campomanes, P., & Vanni, S. (2023). Development of a coarse-grained model for surface-functionalized gold nanoparticles: Towards an accurate description of their aggregation behavior. Soft Matter, 19(18), 3290–3300. https://doi.org/10.1039/D3SM00094J
   PubMed Web of Science ®Google Scholar
• Pilz, T., Melzer, F., Dahlmann, R., & Hopmann, C. (2023). Calculation of the macroscopic mechanical properties of polyamide 6.6 underwater influence using molecular dynamics simulations, hyperelastic material modeling of the amorphous fraction, and homogenization schemes. Polymers for Advanced Technologies, 34(12), 3722–3734. https://doi.org/10.1002/pat.6172
   Web of Science ®Google Scholar
• Pos, K. M. (2009). Drug transport mechanism of the AcrB efflux pump. Biochimica et Biophysica Acta, 1794(5), 782–793. https://doi.org/10.1016/j.bbapap.2008.12.015
   PubMed Web of Science ®Google Scholar
• Raj, D. S., Kottaisamy, C. P. D., Hopper, W., & Sankaran, U. (2021). Identification of immucillin analogue natural compounds to inhibit Helicobacter pylori MTAN through high throughput virtual screening and molecular dynamics simulation. In Silico Pharmacology, 9(1), 22. https://doi.org/10.1007/s40203-021-00081-2
   PubMedGoogle Scholar
• Rathinavel, T., Palanisamy, M., Palanisamy, S., Subramanian, A., & Thangaswamy, S. (2020). Phytochemical 6-gingerol—A promising drug of choice for COVID-19. International Journal of Advanced Science and Engineering, 06(04), 1482–1489. https://doi.org/10.29294/IJASE.6.4.2020.1482-1489
   Google Scholar
• Rout, M., Mishra, S., Dey, S., Singh, M. K., Dehury, B., & Pati, S. (2023). Exploiting the potential of natural polyphenols as antivirals against monkeypox envelope protein F13 using machine learning and all-atoms MD simulations. Computers in Biology and Medicine, 162, 107116. https://doi.org/10.1016/j.compbiomed.2023.107116
   PubMedGoogle Scholar
• Sahoo, M., Dey, S., Sahoo, S., Das, A., Ray, A., Nayak, S., & Subudhi, E. (2023). MLP (multi-layer perceptron) and RBF (radial basis function) neural network approach for estimating and optimizing 6-gingerol content in Zingiber officinale Rosc. in different agro-climatic conditions. Industrial Crops and Products, 198, 116658. https://doi.org/10.1016/j.indcrop.2023.116658
   Web of Science ®Google Scholar
• Sahu, A., Gaur, M., Mahanandia, N. C., Subudhi, E., Swain, R. P., & Subudhi, B. B. (2023). Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs against them: An in silico approach. Computers in Biology and Medicine, 161, 106971. https://doi.org/10.1016/j.compbiomed.2023.106971
   PubMed Web of Science ®Google Scholar
• Samadi, M., & Moshfegh, A. Z. (2022). Recent developments of electrospinning-based photocatalysts in degradation of organic pollutants: principles and strategies. ACS Omega, 7(50), 45867–45881. https://doi.org/10.1021/acsomega.2c05624
   PubMed Web of Science ®Google Scholar
• Samadi, M., Moradinazar, M., Khosravy, T., Soleimani, D., Jahangiri, P., & Kamari, N. (2022). A systematic review and meta-analysis of preclinical and clinical studies on the efficacy of ginger for the treatment of fatty liver disease. Phytotherapy Research: PTR, 36(3), 1182–1193. https://doi.org/10.1002/ptr.7390
   PubMed Web of Science ®Google Scholar
• Samreen, Qais, F. A., Ahmad, I. (2022). In silico screening and in vitro validation of phytocompounds as multidrug efflux pump inhibitor against E. coli. Journal of Biomolecular Structure & Dynamics, 6, 2189–2201, 41 https://doi.org/10.1080/07391102.2022.2029564
   PubMed Web of Science ®Google Scholar
• Shaheen, A., Afridi, W. A., Mahboob, S., Sana, M., Zeeshan, N., Ismat, F., Mirza, O., Iqbal, M., & Rahman, M. (2019). Reserpine is the new addition into the repertoire of AcrB efflux pump inhibitors. Molecular Biology, 53(4), 596–605. https://doi.org/10.1134/S0026893319040113
   Web of Science ®Google Scholar
• Sherman, W., Beard, H. S., & Farid, R. (2006a). Use of an induced fit receptor structure in virtual screening. Chemical Biology & Drug Design, 67(1), 83–84. https://doi.org/10.1111/j.1747-0285.2005.00327.x
   PubMed Web of Science ®Google Scholar
• Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A., & Farid, R. (2006b). Novel procedure for modeling ligand/receptor induced fit effects. Journal of Medicinal Chemistry, 49(2), 534–553. https://doi.org/10.1021/jm050540c
   PubMed Web of Science ®Google Scholar
• Silva, L., Carrion, L. L., von Groll, A., Costa, S. S., Junqueira, E., Ramos, D. F., Cantos, J., Seus, V. R., Couto, I., Fernandes, L. D S., Bonacorso, H. G., Martins, M. A. P., Zanatta, N., Viveiros, M., Machado, K. S., & Almeida da Silva, P. E. (2017). In vitro and in silico analysis of the efficiency of tetrahydropyridines as drug efflux inhibitors in Escherichia coli. International Journal of Antimicrobial Agents, 49(3), 308–314. https://doi.org/10.1016/j.ijantimicag.2016.11.024
   PubMed Web of Science ®Google Scholar
• Silvester, R., Madhavan, A., Kokkat, A., Parolla, A., Adarsh, B. M., Harikrishnan, M., & Abdulla, M. H. (2022). Global surveillance of antimicrobial resistance and hypervirulence in Klebsiella pneumoniae from LMICs: An in-silico approach. The Science of the Total Environment, 802, 149859. https://doi.org/10.1016/j.scitotenv.2021.149859
   PubMed Web of Science ®Google Scholar
• Sirous, H., Campiani, G., Calderone, V., & Brogi, S. (2021). Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening. Computers in Biology and Medicine, 137, 104808. https://doi.org/10.1016/j.compbiomed.2021.104808
   PubMed Web of Science ®Google Scholar
• Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021). gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS. Journal of Chemical Theory and Computation, 17(10), 6281–6291. https://doi.org/10.1021/acs.jctc.1c00645
   PubMed Web of Science ®Google Scholar
• Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., & Mackerell, A. D. (2009). CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry, 31(4), 671–690. https://doi.org/10.1002/jcc.21367
   Web of Science ®Google Scholar
• Verma, P., Maurya, P., Tiwari, M., & Tiwari, V. (2019). In-silico interaction studies suggest RND efflux pump mediates polymyxin resistance in Acinetobacter baumannii. Journal of Biomolecular Structure & Dynamics, 37(1), 95–103. https://doi.org/10.1080/07391102.2017.1418680
   PubMed Web of Science ®Google Scholar
• Vilar, S., Ferino, G., Phatak, S. S., Berk, B., Cavasotto, C. N., & Costanzi, S. (2011). Docking-based virtual screening for ligands of G protein-coupled receptors: Not only crystal structures but also in silico models. Journal of Molecular Graphics & Modelling, 29(5), 614–623. https://doi.org/10.1016/j.jmgm.2010.11.005
   PubMed Web of Science ®Google Scholar
• Wang, B., Li, R., Lu, Z., & Huang, Y. (2020). Does comorbidity increase the risk of patients with COVID-19: Evidence from meta-analysis. Aging, 12(7), 6049–6057. https://doi.org/10.18632/aging.103000
   PubMedGoogle Scholar
• Zhang, Z., Morgan, C. E., Bonomo, R. A., & Yu, E. W. (2023). Cryo-EM structures of the Klebsiella pneumoniae AcrB multidrug efflux pump. mBio, 14(3), e00659-23. https://doi.org/10.1128/mbio.00659-23
   PubMed Web of Science ®Google Scholar