Advertisement

Molecular Dynamics Simulations of Protein–Drug Complexes: A Computational Protocol for Investigating the Interactions of Small-Molecule Therapeutics with Biological Targets and Biosensors

  • Jodi A. Hadden
  • Juan R. Perilla
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1762)

Abstract

MD simulations provide a powerful tool for the investigation of protein–drug complexes. The following chapter uses the aryl acylamidase–acetaminophen system as an example to describe a general protocol for preparing and running simulations of protein–drug complexes, complete with a step-by-step tutorial. The described approach is broadly applicable toward the study of drug interactions in the context of both biological targets and biosensing enzymes.

Key words

Drug development Drug target Enzyme biosensors Force field parameterization Molecular dynamics simulation Protein–drug complex 

Notes

Acknowledgements

The authors acknowledge funding from the University of Delaware and the National Institutes of Health COBRE grant 5P30GM110758-04.

References

  1. 1.
    Lee EH, Hsin J, Sotomayor M, Comellas G, Schulten K (2009) Discovery through the computational microscope. Structure 17:1295–1306CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Perilla JR, Goh BC, Keith Cassidy C, Bo L, Bernardi RC, Rudack T, Hang Y, Zhe W, Schulten K (2015) Molecular dynamics simulations of large macromolecular complexes. Curr Opin Struct Biol 31:64–74CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Ebele AJ, Abdallah MA-E, Harrad S (2017) Pharmaceuticals and personal care products (PPCPs) in the freshwater aquatic environment. Emerging Contaminants 3(1):1–16CrossRefGoogle Scholar
  4. 4.
    Banica F-G (2012) Chemical sensors and biosensors: fundamentals and applications. John Wiley & Sons, ChichesterCrossRefGoogle Scholar
  5. 5.
    Perilla JR, Hadden JA, Goh BC, Mayne CG, Schulten K (2016) All-atom molecular dynamics of virus capsids as drug targets. J Phys Chem Lett 7:1836–1844CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Dart RC, Green JL (2016) The prescription paradox of acetaminophen safety. Pharmacoepidemiol Drug Saf 25(5):599–601CrossRefPubMedGoogle Scholar
  7. 7.
    Hinson JA, Roberts DW, James LP (2010) Mechanisms of acetaminophen-induced liver necrosis. Handb Exp Pharmacol 196:369–405CrossRefGoogle Scholar
  8. 8.
    Yoon E, Babar A, Choudhary M, Kutner M, Pyrsopoulos N (2016) Acetaminophen-induced hepatotoxicity: a comprehensive update. J Clin Transl Hepatol 4(2):131PubMedPubMedCentralGoogle Scholar
  9. 9.
    Michael Bulger, Jan Holinsky (2014) Acetaminophen assay. US Patent 8,715,952, 6 May 2014Google Scholar
  10. 10.
    Hammond PM, Scawen MD, Tony Atkinson RSC, Price CP (1984) Development of an enzyme-based assay for acetaminophen. Anal Biochem 143(1):152–157CrossRefPubMedGoogle Scholar
  11. 11.
    Morris HC, Overton PD, Richard Ramsay J, Stewart Campbell R, Hammond PM, Atkinson T, Price CP (1990) Development and validation of an automated enzyme assay for paracetamol (acetaminophen). Clin Chim Acta 187(2):95–104CrossRefPubMedGoogle Scholar
  12. 12.
    Vaughan PA, Scott LDL, McAller JF (1991) Amperometric biosensor for the rapid determination of acetaminophen in whole blood. Anal Chim Acta 248(2):361–365CrossRefGoogle Scholar
  13. 13.
    Lee S, Park E-H, Ko H-J, Bang WG, Kim H-Y, Kim KH, Choi IG (2015) Crystal structure analysis of a bacterial aryl acylamidase belonging to the amidase signature enzyme family. Biochem Biophys Res Commun 467(2):268–274CrossRefPubMedGoogle Scholar
  14. 14.
    Humphrey W, Dalke A, Schulten K (1996) VMD–visual molecular dynamics. J Mol Graph 14(1):33–38CrossRefPubMedGoogle Scholar
  15. 15.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen M-y, Pieper U, Sali A (2006) Comparative protein structure modeling using MODELLER. Cur Protoc Bioinformatics. https://doi.org/10.1002/0471250953.bi0506s15
  17. 17.
    Das R, Baker D (2008) Macromolecular modeling with ROSETTA. Annu Rev Biochem 77:363–382CrossRefPubMedGoogle Scholar
  18. 18.
    Wriggers W (2010) Using situs for the integration of multi-resolution structures. Biophys Rev 2:21–27CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Trabuco LG, Villa E, Mitra K, Frank J, Schulten K (2008) Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16:673–683CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Goh BC, Hadden JA, Bernardi RC, Singharoy A, McGreevy R, Rudack T, Keith Cassidy C, Schulten K (2016) Computational methodologies for real-space structural refinement of large macromolecular complexes. Annu Rev Biophys 45:253–278CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Trott O, Olson AJ (2010) Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461PubMedPubMedCentralGoogle Scholar
  22. 22.
    Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9(2):91–102CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Dolinsky TJ, Czodrowski P, Li H, Nielsen JE, Jensen JH, Klebe G, Baker NA (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35(Web Server):W522–W525CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Søndergaard CR, Olsson MHM, Rostkowski M l, Jensen JH (2011) Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J Chem Theory Comput 7(7):2284–2295CrossRefPubMedGoogle Scholar
  25. 25.
    Ko M, Huang Y, Jankowska AM, Pape UJ, Tahiliani M, Bandukwala HS, An J, Lamperti ED, Koh KP, Ganetzky R, Shirley Liu X, Aravind L, Agarwal S, Maciejewski JP, Rao A (2010) Impaired hydroxylation of 5-methylcytosine in myeloid cancers with mutant TET2. Nature 468:839–843CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935CrossRefGoogle Scholar
  27. 27.
    Onufriev A (2008) Implicit solvent models in molecular dynamics simulations: a brief overview. Annu Rep Comput Chem 4:125–137CrossRefGoogle Scholar
  28. 28.
    MacKerell AD Jr, Bashford D, Bellott M, Dunbrack RL Jr, Evanseck JD et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616CrossRefPubMedGoogle Scholar
  29. 29.
    MacKerell AD Jr, Feig M, Brooks CL (2004) Improved treatment of the protein backbone in empirical force fields. J Am Chem Soc 126:698–699CrossRefPubMedGoogle Scholar
  30. 30.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Developing and testing of a general amber force field. J Comput Chem 25(9):1157–1174CrossRefPubMedGoogle Scholar
  31. 31.
    Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, MacKerell AD Jr (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690PubMedPubMedCentralGoogle Scholar
  32. 32.
    Vanommeslaeghe K, MacKerell AD Jr (2012) Automation of the CHARMM general force field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52(12):3144–3154CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Vanommeslaeghe K, Prabhu Raman E, MacKerell AD Jr (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Maestro Release 2017-2: MacroModel, Schrödinger, LLC, New York, NY, 2017Google Scholar
  35. 35.
    Mayne CG, Saam J, Schulten K, Tajkhorshid E, Gumbart JC (2013) Rapid parameterization of small molecules using the force field toolkit. J Comput Chem 34:2757–2770CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryUniversity of DelawareNewarkUSA

Personalised recommendations