Identifying the Interaction of Vancomycin With Novel pH-Responsive Lipids as Antibacterial Biomaterials Via Accelerated Molecular Dynamics and Binding Free Energy Calculations

  • Shaimaa Ahmed
  • Suresh B. Vepuri
  • Mahantesh Jadhav
  • Rahul S. Kalhapure
  • Thirumala Govender
Original Paper
  • 139 Downloads

Abstract

Nano-drug delivery systems have proven to be an efficient formulation tool to overcome the challenges with current antibiotics therapy and resistance. A series of pH-responsive lipid molecules were designed and synthesized for future liposomal formulation as a nano-drug delivery system for vancomycin at the infection site. The structures of these lipids differ from each other in respect of hydrocarbon tails: Lipid1, 2, 3 and 4 have stearic, oleic, linoleic, and linolenic acid hydrocarbon chains, respectively. The impact of variation in the hydrocarbon chain in the lipid structure on drug encapsulation and release profile, as well as mode of drug interaction, was investigated using molecular modeling analyses. A wide range of computational tools, including accelerated molecular dynamics, normal molecular dynamics, binding free energy calculations and principle component analysis, were applied to provide comprehensive insight into the interaction landscape between vancomycin and the designed lipid molecules. Interestingly, both MM-GBSA and MM-PBSA binding affinity calculations using normal molecular dynamics and accelerated molecular dynamics trajectories showed a very consistent trend, where the order of binding affinity towards vancomycin was lipid4 > lipid1 > lipid2 > lipid3. From both normal molecular dynamics and accelerated molecular dynamics, the interaction of lipid3 with vancomycin is demonstrated to be the weakest (∆Gbinding = −2.17 and −11.57, for normal molecular dynamics and accelerated molecular dynamics, respectively) when compared to other complexes. We believe that the degree of unsaturation of the hydrocarbon chain in the lipid molecules may impact on the overall conformational behavior, interaction mode and encapsulation (wrapping) of the lipid molecules around the vancomycin molecule. This thorough computational analysis prior to the experimental investigation is a valuable approach to guide for predicting the encapsulation ability, drug release and further development of novel liposome-based pH-responsive nano-drug delivery system with refined structural and chemical features of potential lipid molecule for formulation development.

Keywords

pH-responsive lipids Liposomes Nano-drug delivery systems (NDDS) Vancomycin Molecular dynamics Binding affinity calculations 

Notes

Acknowledgements

The authors are thankful to National Research Foundation of South Africa, University of KwaZulu-Natal and UKZN Nano Platform for financial support and Carrin Martin for editing.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. 1.
    Klevens, R. M., Edwards, J. R., Richards, C. L., Horan, T. C., Gaynes, R. P., Pollock, D. A., & Cardo, D. M. (2007). Estimating health care-associated infections and deaths in U.S. Hospitals, Public Health Reports 122(2):160–166.Google Scholar
  2. 2.
    Weigel, L. M., Clewell, D. B., Gill, S. R., Clark, N. C., McDougal, L. K., Flannagan, S. E., & Tenover, F. C. (2003). Genetic analysis of a high-level vancomycin-resistant isolate of Staphylococcus aureus. Science, 302(5650), 1569–71. doi: 10.1126/science.1090956.CrossRefPubMedGoogle Scholar
  3. 3.
    Zaidi, A. K., Huskins, W. C., Thaver, D., Bhutta, Z. A., Abbas, Z., & Goldmann, D. A. (2005). Hospital-acquired neonatal infections in developing countries. The Lancet, 365(9465), 1175–1188. doi: 10.1016/S0140-6736(05)71881-X.CrossRefGoogle Scholar
  4. 4.
    Kardas, P. (2002). Patient compliance with antibiotic treatment for respiratory tract infections. Journal of Antimicrobial Chemotherapy, 49(6), 897–903. doi: 10.1093/jac/dkf046.CrossRefPubMedGoogle Scholar
  5. 5.
    Baker-Austin, C., Wright, M. S., Stepanauskas, R., & McArthur, J. V. (2006). Co-selection of antibiotic and metal resistance. Trends in Microbiology, 14(4), 176–182. doi: 10.1016/j.tim.2006.02.006.CrossRefPubMedGoogle Scholar
  6. 6.
    Huh, A. J., & Kwon, Y. J. (2011). “Nanoantibiotics”: A new paradigm for treating infectious diseases using nanomaterials in the antibiotics resistant era. Journal of Controlled Release, 156(2), 128–145. doi: 10.1016/j.jconrel.2011.07.002.CrossRefPubMedGoogle Scholar
  7. 7.
    Pandey, R., & Khuller, G. K. (2005). Antitubercular inhaled therapy: Opportunities, progress and challenges. Journal of Antimicrobial Chemotherapy, 55(4), 430–435. doi: 10.1093/jac/dki027.CrossRefPubMedGoogle Scholar
  8. 8.
    Pelgrift, R. Y., & Friedman, A. J. (2013). Nanotechnology as a therapeutic tool to combat microbial resistance. Advanced Drug Delivery Reviews, 65(13–14), 1803–1815. doi: 10.1016/j.addr.2013.07.011.CrossRefPubMedGoogle Scholar
  9. 9.
    Kalhapure, R. S., Suleman, N., Mocktar, C., Seedat, N., & Govender, T. (2014). Nanoengineered drug delivery systems for enhancing antibiotic therapy. Journal of Pharmaceutical Sciences. doi: 10.1002/jps.24298.
  10. 10.
    Zhu, X., Radovic-Moreno, A. F., Wu, J., Langer, R., & Shi, J. (2014). Nanomedicine in the management of microbial infection – Overview and perspectives. Nano Today, 9(4), 478–498. doi: 10.1016/j.nantod.2014.06.003.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Radovic-Moreno, A. F., Lu, T. K., Puscasu, V. A., Yoon, C. J., Langer, R., & Farokhzad, O. C. (2012). Surface charge-switching polymeric nanoparticles for bacterial cell wall-targeted delivery of antibiotics. ACS Nano, 6(5), 4279–4287. doi: 10.1021/nn3008383.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Kalhapure, R. S., Mocktar, C., Sikwal, D. R., Sonawane, S. J., Kathiravan, M. K., Skelton, A., & Govender, T. (2014). Ion pairing with linoleic acid simultaneously enhances encapsulation efficiency and antibacterial activity of vancomycin in solid lipid nanoparticles. Colloids and Surfaces. B, Biointerfaces, 117, 303–11. doi: 10.1016/j.colsurfb.2014.02.045.CrossRefPubMedGoogle Scholar
  13. 13.
    Kashi, T. S. J., Eskandarion, S., Esfandyari-Manesh, M., Marashi, S. M. A., Samadi, N., & Fatemi, S. M., et al. (2012). Improved drug loading and antibacterial activity of minocycline-loaded PLGA nanoparticles prepared by solid/oil/water ion pairing method. International Journal of Nanomedicine, 7, 221–234. doi: 10.2147/IJN.S27709.PubMedPubMedCentralGoogle Scholar
  14. 14.
    UK Patent Application GB1614120.2. (n.d.).Google Scholar
  15. 15.
    Lyne, P. D., Lamb, M. L., & Saeh, J. C. (2006). Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. Journal of Medicinal Chemistry, 49(16), 4805–4808. doi: 10.1021/jm060522a.CrossRefPubMedGoogle Scholar
  16. 16.
    Greenidge, P. a, Kramer, C., Mozziconacci, J.-C., & Sherman, W. (2014). Improving docking results via reranking of ensembles of ligand poses in multiple x-ray protein conformations with MM-GBSA. Journal of Chemical Information and Modeling. doi: 10.1021/ci5003735
  17. 17.
    Amadei, A., Linssen, A. B. M., & Berendsen, H. J. C. (1993). Essential dynamics of proteins. Proteins: Structure, Function, and Genetics, 17(4), 412–425. doi: 10.1002/prot.340170408.CrossRefGoogle Scholar
  18. 18.
    Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. doi: 10.1002/jcc.20084.CrossRefPubMedGoogle Scholar
  19. 19.
    Marvinsketch: https://www.chemaxon.com/products/marvin/marvinsketch/, accessed 23 Apr 2016.
  20. 20.
    Maestro: https://www.schrodinger.com/maestro/, accessed 13 Apr 2016.
  21. 21.
    Case, D. A., Berryman, J. T., Betz, R. M., Cerutti, D. S., Cheatham, T. E., Darden, T. A., & Kollman, P. A. (2015). AMBER 2015. San Francisco: University of California. University of California, San Francisco.Google Scholar
  22. 22.
    Götz, A. W., Williamson, M. J., Xu, D., Poole, D., Le Grand, S., & Walker, R. C. (2012). Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. Journal of Chemical Theory and Computation, 8(5), 1542–1555. doi: 10.1021/ct200909j.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Dupradeau, F.-Y., Pigache, A., Zaffran, T., Savineau, C., Lelong, R., Grivel, N., & Cieplak, P. (2010). The R.E.D. tools: Advances in RESP and ESP charge derivation and force field library building. Physical Chemistry Chemical Physics, 12(28), 7821–7839. doi: 10.1039/c0cp00111b.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics. doi: 10.1063/1.445869.Google Scholar
  25. 25.
    Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. The Journal of Chemical Physics, 103(19), 8577 doi: 10.1063/1.470117.CrossRefGoogle Scholar
  26. 26.
    Ryckaert, J.-P., Ciccotti, G., & Berendsen, H. J. C. (1977). Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Computational Physics, 23(3), 327–341. doi: 10.1016/0021-9991(77)90098-5.CrossRefGoogle Scholar
  27. 27.
    Grant, B. J., Rodrigues, A. P. C., ElSawy, K. M., McCammon, J. A., & Caves, L. S. D. (2006). Bio3d: An R package for the comparative analysis of protein structures. Bioinformatics, 22(21), 2695–2696. doi: 10.1093/bioinformatics/btl461.CrossRefPubMedGoogle Scholar
  28. 28.
    Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 27–28. 38.CrossRefGoogle Scholar
  29. 29.
    Laskowski, R. A., & Swindells, M. B. (2011). LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling, 51(10), 2778–2786. doi: 10.1021/ci200227u.CrossRefPubMedGoogle Scholar
  30. 30.
    Schrodinger LLC. (2015). The PyMOL Molecular Graphics System, Version 1.8.Google Scholar
  31. 31.
    Hou, T., Wang, J., Li, Y., & Wang, W. (2010). Assessing the performance of the MM/PBSA and MM/GBSA Methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of Chemical Information and Modeling, 51, 69–82. doi: 10.1021/ci100275a.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Cocco, S., Monasson, R., & Weigt, M. (2013). From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction. PLoS Computational Biology, 9(8), e1003176. doi: 10.1371/journal.pcbi.1003176.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Singh, R., & Lillard, J. W. (2009). Nanoparticle-based targeted drug delivery. Experimental and Molecular Pathology, 86(3), 215–23. doi: 10.1016/j.yexmp.2008.12.004.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Hamelberg, D., Mongan, J., & McCammon, J. A. (2004). Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. The Journal of Chemical Physics, 120(24), 11919–11929. doi: 10.1063/1.1755656.CrossRefPubMedGoogle Scholar
  35. 35.
    Pierce, L. C. T., Salomon-Ferrer, R., Augusto, C., de Oliveira, F., McCammon, Ja, & Walker, R. C. (2012). Routine access to millsecond timescale evenrs with accelerated molecular dynamics. Journal of Chemical Theory and Computation, 8, 2997–3002. doi: 10.1021/ct300284c.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Desdouits, N., Nilges, M., & Blondel, A. (2015). Principal component analysis reveals correlation of cavities evolution and functional motions in proteins. Journal of Molecular Graphics and Modelling, 55, 13–24. doi: 10.1016/j.jmgm.2014.10.011.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Shaimaa Ahmed
    • 1
  • Suresh B. Vepuri
    • 1
  • Mahantesh Jadhav
    • 1
  • Rahul S. Kalhapure
    • 1
  • Thirumala Govender
    • 1
  1. 1.Discipline of Pharmaceutical Sciences, School of Health SciencesUniversity of KwaZulu-NatalDurbanSouth Africa

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