Skip to main content

Molecular Dynamics Simulation of Proteins

  • Protocol
  • First Online:
Protein Nanotechnology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2073))

Abstract

Molecular dynamics simulations allow the conformational motion of a molecule such as a protein to be followed over time at atomic-level detail. Several choices need to be made prior to running a simulation, including the software, which molecules to include in the simulation, and the force field used to describe their behavior. Guidance on making these choices and other important aspects of running MD simulations is outlined here.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Habchi J, Tompa P, Longhi S, Uversky VN (2014) Introducing protein intrinsic disorder. Chem Rev 114(13):6561–6588. https://doi.org/10.1021/cr400514h

    Article  CAS  Google Scholar 

  2. Best RB, Zhu X, Shim J, Lopes PEM, Mittal J, Feig M, MacKerell AD (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone phi, psi and side-chain chi(1) and chi(2) dihedral angles. J Chem Theory Comput 8(9):3257–3273. https://doi.org/10.1021/ct300400x

    Article  CAS  Google Scholar 

  3. Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot BL, Grubmuller H, MacKerell AD (2017) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 14(1):71–73. https://doi.org/10.1038/Nmeth.4067

    Article  CAS  Google Scholar 

  4. MacKerell JAD (2001) Atomistic models and force fields. In: Becker OM, MacKerell JAD, Roux B, Watanabe M (eds) Computational biochemistry and biophysics. Marcel Dekker Inc, New York, NY, pp 7–38

    Google Scholar 

  5. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1996) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules (vol 117, pg 5179, 1995). J Am Chem Soc 118(9):2309–2309. https://doi.org/10.1021/ja955032e

    Article  CAS  Google Scholar 

  6. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713. https://doi.org/10.1021/acs.jctc.5b00255

    Article  CAS  Google Scholar 

  7. Reif MM, Hünenberger PH, Oostenbrink C (2012) New Interaction Parameters for Charged Amino Acid Side Chains in the GROMOS Force Field, J Chem Theory Comput 8(10):3705–3723. https://doi.org/10.1021/ct300156h

  8. Reif MM, Winger M, Oostenbrink C (2013) Testing of the GROMOS force-field parameter set 54A8: structural properties of electrolyte solutions, lipid bilayers, and proteins. J Chem Theory Comput 9(2):1247–1264. https://doi.org/10.1021/ct300874c

    Article  CAS  Google Scholar 

  9. Schmid N, Eichenberger AP, Choutko A, Riniker S, Winger M, Mark AE, van Gunsteren WF (2011) Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur Biophys J Biophy 40(7):843–856. https://doi.org/10.1007/s00249-011-0700-9

    Article  CAS  Google Scholar 

  10. Harder E, Damm W, Maple J, Wu CJ, Reboul M, Xiang JY, Wang LL, Lupyan D, Dahlgren MK, Knight JL, Kaus JW, Cerutti DS, Krilov G, Jorgensen WL, Abel R, Friesner RA (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296. https://doi.org/10.1021/acs.jctc.5b00864

    Article  CAS  Google Scholar 

  11. Jorgensen WL, Maxwell DS, TiradoRives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11225–11236. https://doi.org/10.1021/ja9621760

    Article  CAS  Google Scholar 

  12. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics for protein folding. Chem Phys Lett 314:141–151

    Article  CAS  Google Scholar 

  13. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24):11919–11929. https://doi.org/10.1063/1.1755656

    Article  CAS  Google Scholar 

  14. Huber T, Torda AE, van Gunsteren WF (1994) Local elevation: a method for improving the searching properties of molecular dynamics simulation. J Comput Aided Mol Des 8:695–708

    Article  CAS  Google Scholar 

  15. Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci U S A 99(20):12562–12566

    Article  CAS  Google Scholar 

  16. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  17. 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(16):1781–1802. https://doi.org/10.1002/jcc.20289

    Article  CAS  Google Scholar 

  18. Brooks BR, Brooks CL, Mackerell AD, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614. https://doi.org/10.1002/jcc.21287

    Article  CAS  Google Scholar 

  19. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular simulation package. WIREs Comput Mol Sci 3(2):198–210. https://doi.org/10.1002/wcms.1121

    Article  CAS  Google Scholar 

  20. Gowers RJ, Linke M, Barnoud J, Reddy TJE, Melo MN, Seyler SL, Dotson DL, Domanski J, Buchoux S, Kenney IM, Beckstein O (2016) MDAnalysis: a python package for the rapid analysis of molecular dynamics simulations. In: Benthall S, Rostrup S (eds) Proceedings of the 15th python in science conference. SciPy, Austin, TX, pp 102–109

    Google Scholar 

  21. Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011) Software news and updates MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32(10):2319–2327. https://doi.org/10.1002/jcc.21787

    Article  CAS  Google Scholar 

  22. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14(1):33–38. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  CAS  Google Scholar 

  23. Schrödinger L (2015) The PyMOL molecular graphics system, Version 1.8

    Google Scholar 

  24. Williams T, Kelley C (2010) Gnuplot 4.4: an interactive plotting program

    Google Scholar 

  25. Stambulchik E (2000) Grace

    Google Scholar 

  26. Winter A (2017) QtGrace

    Google Scholar 

  27. Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5(3):299–314

    Google Scholar 

  28. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  29. Sali A, Blundell TL (1993) Comparative protein modeling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815. https://doi.org/10.1006/jmbi.1993.1626

    Article  CAS  Google Scholar 

  30. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18(15):2714–2723. https://doi.org/10.1002/elps.1150181505

    Article  CAS  Google Scholar 

  31. Groom CR, Bruno IJ, Lightfoot MP, Ward SC (2016) The Cambridge structural database. Acta Cryst B72:171–179

    Google Scholar 

  32. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminformatics 4:Artn 17. https://doi.org/10.1186/1758-2946-4-17

    Article  CAS  Google Scholar 

  33. Malde AK, Zuo L, Breeze M, Stroet M, Poger D, Nair PC, Oostenbrink C, Mark AE (2011) An automated force field topology builder (ATB) and repository: version 1.0. J Chem Theory Comput 7(12):4026–4037. https://doi.org/10.1021/ct200196m

    Article  CAS  Google Scholar 

  34. Welsh ID, Allison JR (2019) CherryPicker: An Algorithm for the Automated Parametrization of Large Biomolecules for Molecular Simulation. Frontiers in Chemistry 7:400. https://doi.org/10.3389/fchem.2019.00400

    Article  CAS  Google Scholar 

  35. Yu WB, He XB, Vanommeslaeghe K, MacKerell AD (2012) Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations. J Comput Chem 33(31):2451–2468. https://doi.org/10.1002/jcc.23067

    Article  CAS  Google Scholar 

  36. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, MacKerell AD (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):671–690. https://doi.org/10.1002/jcc.21367

    Article  CAS  Google Scholar 

  37. Wang JM, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260. https://doi.org/10.1016/j.jmgm.2005.12.005

    Article  CAS  Google Scholar 

  38. Dodda LS, de Vaca IC, Tirado-Rives J, Jorgensen WL (2017) LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res 45(W1):W331–W336. https://doi.org/10.1093/nar/gkx312

    Article  CAS  Google Scholar 

  39. Dodda LS, Vilseck JZ, Tirado-Rives J, Jorgensen WL (2017) 1.14∗CM1A-LBCC: localized bond-charge corrected CM1A charges for condensed-phase simulations. J Phys Chem B 121(15):3864–3870. https://doi.org/10.1021/acs.jpcb.7b00272

    Article  CAS  Google Scholar 

  40. Jorgensen WL, Tirado-Rives J (2005) Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proc Natl Acad Sci U S A 102(19):6665–6670. https://doi.org/10.1073/pnas.0408037102

    Article  CAS  Google Scholar 

  41. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  CAS  Google Scholar 

  42. Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32(suppl_2):W500–W502. https://doi.org/10.1093/nar/gkh429

    Article  CAS  Google Scholar 

  43. Schuttelkopf AW, van Aalten DM (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 60(Pt 8):1355–1363. https://doi.org/10.1107/S0907444904011679

    Article  CAS  Google Scholar 

  44. Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32(11):2359–2368. https://doi.org/10.1002/jcc.21816

    Article  CAS  Google Scholar 

  45. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593. https://doi.org/10.1063/1.470117

    Article  CAS  Google Scholar 

  46. Tironi IG, Sperb R, Smith PE, Vangunsteren WF (1995) A generalized reaction field method for molecular-dynamics simulations. J Chem Phys 102(13):5451–5459. https://doi.org/10.1063/1.469273

    Article  CAS  Google Scholar 

  47. Reisser S, Poger D, Stroet M, Mark AE (2017) Real cost of speed: the effect of a time-saving multiple-time-stepping algorithm on the accuracy of molecular dynamics simulations. J Chem Theory Comput 13(6):2367–2372. https://doi.org/10.1021/acs.jctc.7b00178

    Article  CAS  Google Scholar 

  48. Rauscher S, Gapsys V, Gajda MJ, Zweckstetter M, de Groot BL, Grubmuller H (2015) Structural ensembles of intrinsically disordered proteins depend strongly on force field: a comparison to experiment. J Chem Theory Comput 11(11):5513–5524. https://doi.org/10.1021/acs.jctc.5b00736

    Article  CAS  Google Scholar 

  49. Guvench O, MacKerell AD, Jr. (2008) Comparison of protein force fields for molecular dynamics simulations. Methods Mol Biol 443:63-88. doi:https://doi.org/10.1007/978-1-59745-177-2_4

  50. Martin-Garcia F, Papaleo E, Gomez-Puertas P, Boomsma W, Lindorff-Larsen K (2015) Comparing molecular dynamics force fields in the essential subspace. PLoS One 10(3):e0121114. https://doi.org/10.1371/journal.pone.0121114

    Article  CAS  Google Scholar 

  51. Lindorff-Larsen K, Maragakis P, Piana S, Eastwood MP, Dror RO, Shaw DE (2012) Systematic validation of protein force fields against experimental data. PLoS One 7(2):e32131. https://doi.org/10.1371/journal.pone.0032131

    Article  CAS  Google Scholar 

  52. Piana S, Lindorff-Larsen K, Shaw DE (2011) How robust are protein folding simulations with respect to force field parameterization? Biophys J 100(9):L47–L49. https://doi.org/10.1016/j.bpj.2011.03.051

    Article  CAS  Google Scholar 

  53. Man VH, Nguyen PH, Derreumaux P (2017) High-resolution structures of the amyloid-beta 1-42 dimers from the comparison of four atomistic force fields. J Phys Chem B 121(24):5977–5987. https://doi.org/10.1021/acs.jpcb.7b04689

    Article  CAS  Google Scholar 

  54. Piggot TJ, Piñeiro Á, Khalid S (2012) Molecular dynamics simulations of phosphatidylcholine membranes: a comparative force field study. J Chem Theory Comput 8(11):4593–4609. https://doi.org/10.1021/ct3003157

    Article  CAS  Google Scholar 

  55. Berendsen HJC, Postma JPM, Vangunsteren WF, Dinola A, Haak JR (1984) Molecular-dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690. https://doi.org/10.1063/1.448118

    Article  CAS  Google Scholar 

  56. Hoover WG (1985) Canonical dynamics – equilibrium phase-space distributions. Phys Rev A 31(3):1695–1697. https://doi.org/10.1103/PhysRevA.31.1695

    Article  CAS  Google Scholar 

  57. Nose S (1984) A unified formulation of the constant temperature molecular-dynamics methods. J Chem Phys 81(1):511–519. https://doi.org/10.1063/1.447334

    Article  CAS  Google Scholar 

  58. Nose S (1984) A molecular-dynamics method for simulations in the canonical ensemble. Mol Phys 52(2):255–268. https://doi.org/10.1080/00268978400101201

    Article  CAS  Google Scholar 

  59. van Gunsteren WF, Berendsen HJC (1977) Algorithms for macromolecular dynamics and constraint dynamics. Mol Phys 34(5):1311–1327

    Article  Google Scholar 

  60. Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical-integration of cartesian equations of motion of a system with constraints – molecular-dynamics of N-alkanes. J Comput Phys 23(3):327–341. https://doi.org/10.1016/0021-9991(77)90098-5

    Article  CAS  Google Scholar 

  61. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472. https://doi.org/10.1002/(Sici)1096-987x(199709)18:12<1463::Aid-Jcc4>3.3.Co;2-L

    Article  CAS  Google Scholar 

  62. Andersen HC (1983) Rattle – a velocity version of the shake algorithm for molecular-dynamics calculations. J Comput Phys 52(1):24–34. https://doi.org/10.1016/0021-9991(83)90014-1

    Article  CAS  Google Scholar 

  63. Ewald P (1921) Die Berechnung optischer und elektrostatischer Gitterpotentiale. Ann Phys 369(3):253–287

    Article  Google Scholar 

  64. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190

    Article  CAS  Google Scholar 

  65. Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40(W1):W537–W541. https://doi.org/10.1093/nar/gks375

    Article  CAS  Google Scholar 

  66. Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pK(a)s and adding missing hydrogens to macromolecules. Nucleic Acids Res 33:W368–W371. https://doi.org/10.1093/nar/gki464

    Article  CAS  Google Scholar 

  67. Myers J, Grothaus G, Narayanan S, Onufriev A (2006) A simple clustering algorithm can be accurate enough for use in calculations of pKs in macromolecules. Proteins Struct Funct Bioinform 63(4):928–938. https://doi.org/10.1002/prot.20922

    Article  CAS  Google Scholar 

  68. Olsson MHM, Sondergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pK(a) predictions. J Chem Theory Comput 7(2):525–537. https://doi.org/10.1021/ct100578z

    Article  CAS  Google Scholar 

  69. Sondergaard CR, Olsson MHM, Rostkowski M, Jensen JH (2011) Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pK(a) values. J Chem Theory Comput 7(7):2284–2295. https://doi.org/10.1021/ct200133y

    Article  CAS  Google Scholar 

  70. Wassenaar TA, Mark AE (2006) The effect of box shape on the dynamic properties of proteins simulated under periodic boundary conditions. J Comput Chem 27(3):316–325. https://doi.org/10.1002/jcc.20341

    Article  CAS  Google Scholar 

  71. 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):926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  72. Durell SR, Brooks BR, Bennaim A (1994) Solvent-induced forces between 2 hydrophilic groups. J Phys Chem 98(8):2198–2202. https://doi.org/10.1021/j100059a038

    Article  CAS  Google Scholar 

  73. Neria E, Fischer S, Karplus M (1996) Simulation of activation free energies in molecular systems. J Chem Phys 105(5):1902–1921. https://doi.org/10.1063/1.472061

    Article  CAS  Google Scholar 

  74. Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. https://doi.org/10.1002/jcc.20035

    Article  CAS  Google Scholar 

  75. Berendsen HJC, Postma JPM, van Gunsteren WF, Hermans J (1981) Interaction models for water in relation to protein hydration. In: Pullman B (ed) Intermolecular forces. Reidel, Dordrecht, pp 331–342

    Chapter  Google Scholar 

  76. Berendsen HJC, Grigera JR, Straatsma TP (1987) The missing term in effective pair potentials. J Phys Chem 91(24):6269–6271. https://doi.org/10.1021/j100308a038

    Article  CAS  Google Scholar 

  77. Chatterjee S, Debenedetti PG, Stillinger FH, Lynden-Bell RM (2008) A computational investigation of thermodynamics, structure, dynamics and solvation behavior in modified water models. J Chem Phys 128(12):Artn 124511. https://doi.org/10.1063/1.2841127

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported financially by DSTL (T.J.P.) and a Rutherford Discovery Fellowship (15-MAU-001) and Marsden grant (15-UOA-105) (J.R.A.).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jane R. Allison .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Collier, T.A., Piggot, T.J., Allison, J.R. (2020). Molecular Dynamics Simulation of Proteins. In: Gerrard, J., Domigan, L. (eds) Protein Nanotechnology. Methods in Molecular Biology, vol 2073. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9869-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9869-2_17

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9868-5

  • Online ISBN: 978-1-4939-9869-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics