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Systematic Validation of Protein Force Fields against Experimental Data

Open access
Lindorff-Larsen, Kresten, Maragakis, Paul, Piana, Stefano, Eastwood, Michael P., Dror, Ron O., Shaw, David E.·2012·PLoS ONE

Abstract

Molecular dynamics simulations provide a vehicle for capturing the structures, motions, and interactions of biological macromolecules in full atomic detail. The accuracy of such simulations, however, is critically dependent on the force field--the mathematical model used to approximate the atomic-level forces acting on the simulated molecular system. Here we present a systematic and extensive evaluation of eight different protein force fields based on comparisons of experimental data with molecular dynamics simulations that reach a previously inaccessible timescale. First, through extensive comparisons with experimental NMR data, we examined the force fields' abilities to describe the structure and fluctuations of folded proteins. Second, we quantified potential biases towards different secondary structure types by comparing experimental and simulation data for small peptides that preferentially populate either helical or sheet-like structures. Third, we tested the force fields' abilities to fold two small proteins--one α-helical, the other with β-sheet structure. The results suggest that force fields have improved over time, and that the most recent versions, while not perfect, provide an accurate description of many structural and dynamical properties of proteins.

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Research works printed but not linked: 39

  1. JL Klepeis, 2009, Long-timescale molecular dynamics simulations of protein structure and function., Curr Opin Struct Biol, 19, 120, 10.1016/j.sbi.2009.03.004 · DOI · Paper page
  2. JE Stone, 2010, GPU-accelerated molecular modeling coming of age., J Mol Graph Model, 29, 116, 10.1016/j.jmgm.2010.06.010 · DOI · Paper page
  3. M Vendruscolo, 2011, Protein dynamics: Moore's law in molecular biology., Curr Biol, 21, R68, 10.1016/j.cub.2010.11.062 · DOI
  4. AF Voter, 1998, Parallel replica method for dynamics of infrequent events., Phys Rev B, 57, R13985, 10.1103/PhysRevB.57.R13985 · DOI · Paper page
  5. KJ Bowers, 2007, Zonal methods for the parallel execution of range-limited N-body simulations., J Comput Phys, 221, 303, 10.1016/j.jcp.2006.06.014 · DOI
  6. H Lei, 2007, Improved sampling methods for molecular simulation., Curr Opin Struct Biol, 17, 187, 10.1016/j.sbi.2007.03.003 · DOI
  7. DE Shaw, 2010, Atomic-level characterization of the structural dynamics of proteins., Science, 330, 341, 10.1126/science.1187409 · DOI · Paper page
  8. RB Best, 2008, Are current molecular dynamics force fields too helical?, Biophys J, 95, L07, 10.1529/biophysj.108.132696 · DOI · Paper page
  9. OF Lange, 2010, Scrutinizing molecular mechanics force fields on the submicrosecond timescale with NMR data., Biophys J, 99, 647, 10.1016/j.bpj.2010.04.062 · DOI · Paper page
  10. V Hornak, 2006, Comparison of multiple Amber force fields and development of improved protein backbone parameters., Proteins, 65, 712, 10.1002/prot.21123 · DOI · Paper page
  11. RB Best, 2009, Optimized molecular dynamics force fields applied to the helix-coil transition of polypeptides., J Phys Chem B, 113, 9004, 10.1021/jp901540t · DOI · Paper page
  12. K Lindorff-Larsen, 2010, Improved side-chain torsion potentials for the Amber ff99SB protein force field., Proteins, 78, 1950, 10.1002/prot.22711 · DOI · Paper page
  13. S Piana, 2011, How robust are protein folding simulations with respect to force field parameterization?, Biophys J, 100, L47, 10.1016/j.bpj.2011.03.051 · DOI · Paper page
  14. Y Duan, 2003, A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations., J Comput Chem, 24, 1999, 10.1002/jcc.10349 · DOI · Paper page
  15. GA Kaminski, 2001, Evaluation and parametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides., J Phys Chem B, 105, 6474, 10.1021/jp003919d · DOI · Paper page
  16. AD MacKerell Jr, 1998, All-atom empirical potential for molecular modeling and dynamics studies of proteins., J Phys Chem B, 102, 3586, 10.1021/jp973084f · DOI · Paper page
  17. AD MacKerell Jr, 2004, Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations., J Comput Chem, 25, 1400, 10.1002/jcc.20065 · DOI · Paper page
  18. PR Markwick, 2007, Exploring multiple timescale motions in protein GB3 using accelerated molecular dynamics and NMR spectroscopy., J Am Chem Soc, 129, 4734, 10.1021/ja0687668 · DOI · Paper page
  19. RB Fenwick, 2011, Weak long-range correlated motions in a surface patch of ubiquitin involved in molecular recognition., J Am Chem Soc, 133, 10336, 10.1021/ja200461n · DOI · Paper page
  20. OF Lange, 2008, Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution., Science, 320, 1471, 10.1126/science.1157092 · DOI · Paper page
  21. PL Freddolino, 2009, Force field bias in protein folding simulations., Biophys J, 96, 3772, 10.1016/j.bpj.2009.02.033 · DOI · Paper page
  22. W Shalongo, 1994, Distribution of helicity within the model peptide acetyl(AAQAA)3amide., J Am Chem Soc, 116, 8288, 10.1021/ja00097a039 · DOI
  23. S Honda, 2008, Crystal structure of a ten-amino acid protein., J Am Chem Soc, 130, 15327, 10.1021/ja8030533 · DOI · Paper page
  24. PL Freddolin, 2010, Challenges in protein folding simulations: timescale, representation, and analysis., Nat Phys, 6, 751, 10.1038/nphys1713 · DOI · Paper page
  25. JC Faver, 2011, The energy computation paradox and ab initio protein folding., PLoS One, 6, e18868, 10.1371/journal.pone.0018868 · DOI
  26. RB Best, 2010, Balance between alpha and beta structures in ab initio protein folding., J Phys Chem B, 114, 8790, 10.1021/jp102575b · DOI
  27. J Kubelka, 2006, Sub-microsecond protein folding., J Mol Biol, 359, 546, 10.1016/j.jmb.2006.03.034 · DOI · Paper page
  28. S Piana, 2011, Computational design and experimental testing of the fastest-folding β-sheet protein., J Mol Biol, 405, 43, 10.1016/j.jmb.2010.10.023 · DOI
  29. WL Jorgensen, 1983, Comparison of simple potential functions for simulating liquid water., J Chem Phys, 79, 926, 10.1063/1.445869 · DOI · Paper page
  30. Y Shan, 2005, Gaussian split Ewald: a fast Ewald mesh method for molecular simulation., J Chem Phys, 122, 54101, 10.1063/1.1839571 · DOI · Paper page
  31. G Conilescu, 1998, Validation of protein structure from anisotropic carbonyl chemical shifts in a dilute liquid crystalline phase., J Am Chem Soc, 120, 6836, 10.1021/ja9812610 · DOI · Paper page
  32. TS Ulmer, 2003, Evaluation of backbone proton positions and dynamics in a small protein by liquid crystal NMR spectroscopy., J Am Chem Soc, 125, 9179, 10.1021/ja0350684 · DOI · Paper page
  33. B Vögeli, 2007, Limits on variations in protein backbone dynamics from precise measurements of scalar couplings., J Am Chem Soc, 129, 9377, 10.1021/ja070324o · DOI
  34. K Lindorff-Larsen, 2005, Interpreting dynamically-averaged scalar couplings in proteins., J Biomol NMR, 32, 273, 10.1007/s10858-005-8873-0 · DOI
  35. AC Wang, 1996, Determination of the backbone dihedral angles φ in human ubiquitin from reparametrized empirical Karplus equations., J Am Chem Soc, 118, 2483, 10.1021/ja9535524 · doi:10.1021/ja9535524
  36. K Lindorff-Larsen, 2005, Simultaneous determination of protein structure and dynamics., Nature, 433, 128, 10.1038/nature03199 · DOI · Paper page
  37. E Marinari, 1992, Simulated tempering: a new Monte Carlo scheme., Europhys Lett, 19, 451, 10.1209/0295-5075/19/6/002 · DOI · Paper page
  38. SH Northrup, 1980, The stable states picture of chemical reactions. I. Formulation for rate constants and initial conditional effects., J Chem Phys, 73, 2700, 10.1063/1.440484 · DOI · Paper page
  39. CJ Fennell, 2006, Is the Ewald summation still necessary? Pairwise alternatives to the accepted standard for long-range electrostatics., J Chem Phys, 124, 234104, 10.1063/1.2206581 · DOI · Paper page

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Metadata

Authors
Lindorff-Larsen, K., Maragakis, P., Piana, S., Eastwood, M. P., Dror, R. O., & Shaw, D. E.
Venue
PLoS ONE
Published
2012-02-22
Volume
7
Issue
2
Pages
e32131
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2037159954
Cited by
645
References
3
FWCI
22.89
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Systematic Validation of Protein Force Fields against Experimental Data. https://app.cochat.ai/papers/10.1371/journal.pone.0032131
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Lindorff-Larsen, K., Maragakis, P., Piana, S., Eastwood, M. P., Dror, R. O., & Shaw, D. E. (2012). Systematic Validation of Protein Force Fields against Experimental Data. PLoS ONE. https://doi.org/10.1371/journal.pone.0032131

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