The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Free energy landscape of protein folding in water: Explicit vs. implicit solvent
Abstract
The Generalized Born (GB) continuum solvent model is arguably the most widely used implicit solvent model in protein folding and protein structure prediction simulations; however, it still remains an open question on how well the model behaves in these large‐scale simulations. The current study uses the β‐hairpin from C‐terminus of protein G as an example to explore the folding free energy landscape with various GB models, and the results are compared to the explicit solvent simulations and experiments. All free energy landscapes are obtained from extensive conformation space sampling with a highly parallel replica exchange method. Because solvation model parameters are strongly coupled with force fields, five different force field/solvation model combinations are examined and compared in this study, namely the explicit solvent model: OPLSAA/SPC model, and the implicit solvent models: OPLSAA/SGB (Surface GB), AMBER94/GBSA (GB with Solvent Accessible Surface Area), AMBER96/GBSA, and AMBER99/GBSA. Surprisingly, we find that the free energy landscapes from implicit solvent models are quite different from that of the explicit solvent model. Except for AMBER96/GBSA, all other implicit solvent models find the lowest free energy state not the native state. All implicit solvent models show erroneous salt‐bridge effects between charged residues, particularly in OPLSAA/SGB model, where the overly strong salt‐bridge effect results in an overweighting of a non‐native structure with one hydrophobic residue F52 expelled from the hydrophobic core in order to make better salt bridges. On the other hand, both AMBER94/GBSA and AMBER99/GBSA models turn the β‐hairpin in to an α‐helix, and the α‐helical content is much higher than the previously reported α‐helices in an explicit solvent simulation with AMBER94 (AMBER94/TIP3P). Only AMBER96/GBSA shows a reasonable free energy landscape with the lowest free energy structure the native one despite an erroneous salt‐bridge between D47 and K50. Detailed results on free energy contour maps, lowest free energy structures, distribution of native contacts, α‐helical content during the folding process, NOE comparison with NMR, and temperature dependences are reported and discussed for all five models. Proteins 2003. © 2003 Wiley‐Liss, Inc.
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A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,103
Replica-exchange molecular dynamics method for protein folding
Sugita, Y., & Okamoto, Y. (1999) · Chemical Physics Letters · doi:10.1016/s0009-2614(99)01123-9 · Cited by 4,514
Wang, J., Cieplak, P., & Kollman, P. A. (2000) · Journal of Computational Chemistry · doi:10.1002/1096-987x(200009)21:12<1049::aid-jcc3>3.0.co;2-f · Cited by 4,015
Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987) · Physics Letters B · doi:10.1016/0370-2693(87)91197-x · Cited by 3,872
Semianalytical treatment of solvation for molecular mechanics and dynamics
Still, W. C., Tempczyk, A., Hawley, R. C., & Hendrickson, T. (1990) · Journal of the American Chemical Society · doi:10.1021/ja00172a038 · Cited by 3,643
Classical Electrostatics in Biology and Chemistry
Honig, B., & Nicholls, A. (1995) · Science · doi:10.1126/science.7761829 · Cited by 2,879
Knowledge‐based protein secondary structure assignment
Frishman, D., & Argos, P. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340230412 · Cited by 2,523
Analytical molecular surface calculation
Connolly, M. L. (1983) · Journal of Applied Crystallography · doi:10.1107/s0021889883010985 · Cited by 2,508
From Levinthal to pathways to funnels
Dill, K. A., & Chan, H. S. (1997) · Nature Structural & Molecular Biology · doi:10.1038/nsb0197-10 · Cited by 2,293
Structure and Mechanism in Protein Science
Fersht, A. (1998) · Cited by 1,882
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Parallel tempering: Theory, applications, and new perspectives
Earl, D. J., & Deem, M. W. (2005) · Physical Chemistry Chemical Physics · doi:10.1039/b509983h · Cited by 1,313
Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.092707.153558 · Cited by 1,163
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Li, J., Abel, R., Zhu, K., Cao, Y., Zhao, S., & Friesner, R. A. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23106 · Cited by 1,159
Feig, M., & Brooks, C. L. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.03.009 · Cited by 566
Hidden complexity of free energy surfaces for peptide (protein) folding
Krivov, S. V., & Karplus, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406234101 · Cited by 358
Metadata
- Authors
- Zhou, R.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2003-09-03
- Volume
- 53
- Issue
- 2
- Pages
- 148-161
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2135469866
- Cited by
- 300
- References
- 49
- FWCI
- 7.40
- Type
- journal article
- Language
- en
- Open access
- closed
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