Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics
Open accessAbstract
The use of free-energy landscapes rationalizes a wide range of aspects of protein behavior by providing a clear illustration of the different states accessible to these molecules, as well as of their populations and pathways of interconversion. The determination of the free-energy landscapes of proteins by computational methods is, however, very challenging as it requires an extensive sampling of their conformational spaces. We describe here a technique to achieve this goal with relatively limited computational resources by incorporating nuclear magnetic resonance (NMR) chemical shifts as collective variables in metadynamics simulations. As in this approach the chemical shifts are not used as structural restraints, the resulting free-energy landscapes correspond to the force fields used in the simulations. We illustrate this approach in the case of the third Ig-binding domain of protein G from streptococcal bacteria (GB3). Our calculations reveal the existence of a folding intermediate of GB3 with nonnative structural elements. Furthermore, the availability of the free-energy landscape enables the folding mechanism of GB3 to be elucidated by analyzing the conformational ensembles corresponding to the native, intermediate, and unfolded states, as well as the transition states between them. Taken together, these results show that, by incorporating experimental data as collective variables in metadynamics simulations, it is possible to enhance the sampling efficiency by two or more orders of magnitude with respect to standard molecular dynamics simulations, and thus to estimate free-energy differences among the different states of a protein with a k B T accuracy by generating trajectories of just a few microseconds.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Metadynamics methods have been successfully applied to study free energy landscapes of flexible proteins, protein oligomers, peptide folding, ligand and selective ion permeation through membrane channels.
“These methods alone, or in combination with high-resolution experimental techniques such as x-ray crystallography and NMR, have been successfully applied to the study of the free energy landscape of flexible proteins and protein oligomers,36,388–389 peptide folding,390–392 ligand and selective ion permeation through cell membrane protein channels.393”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [392]
Cites
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · see citing works
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 38,335 · see citing works
Canonical dynamics: Equilibrium phase-space distributions
Hoover, W. G. (1985) · Physical Review A · doi:10.1103/physreva.31.1695 · Cited by 23,453 · see citing works
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M., Darden, T., Lee, H.-C., & Pedersen, L. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 21,329 · see citing works
LINCS: A linear constraint solver for molecular simulations
Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199709)18:12 · Cited by 18,369 · see citing works
GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. R. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700301q · Cited by 15,830 · see citing works
A molecular dynamics method for simulations in the canonical ensemble
Nosé, S. (1984) · Molecular Physics · doi:10.1080/00268978400101201 · Cited by 10,049 · see citing works
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626 · see citing works
Self-citationReplica-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 4,499 · see citing works
P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation
Hess, B. (2007) · Journal of Chemical Theory and Computation · doi:10.1021/ct700200b · Cited by 4,020 · see citing works
Molecular dynamics simulations of biomolecules
Karplus, M., & McCammon, J. (2002) · Nature Structural Biology · doi:10.1038/nsb0902-646 · Cited by 3,401 · see citing works
The Energy Landscapes and Motions of Proteins
Frauenfelder, H., Sligar, S. G., & Wolynes, P. G. (1991) · Science · doi:10.1126/science.1749933 · Cited by 3,250 · see citing works
TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts
Shen, Y., Delaglio, F., Cornilescu, G., & Bax, A. (2009) · Journal of Biomolecular NMR · doi:10.1007/s10858-009-9333-z · Cited by 2,519 · see citing works
A new algorithm for Monte Carlo simulation of Ising spin systems
Bortz, A. B., Kalos, M. H., & Lebowitz, J. L. (1975) · Journal of Computational Physics · doi:10.1016/0021-9991(75)90060-1 · Cited by 2,430 · see citing works
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 · see citing works
How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980 · see citing works
Bolhuis, P. G., Chandler, D., Dellago, C., & Geissler, P. L. (2002) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.53.082301.113146 · Cited by 1,972 · see citing works
Atomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., et al. (2010) · Science · doi:10.1126/science.1187409 · Cited by 1,923 · see citing works
PLUMED: A portable plugin for free-energy calculations with molecular dynamics
Bonomi, M., Branduardi, D., Bussi, G., Camilloni, C., Provasi, D., Raiteri, P., Donadio, D., Marinelli, F., Pietrucci, F., Broglia, R. A., et al. (2009) · Computer Physics Communications · doi:10.1016/j.cpc.2009.05.011 · Cited by 1,890 · see citing works
Self-citationStructure and Mechanism in Protein Science. A guide to Enzyme Catalysis and Protein Folding
Howland, J. L. (2001) · Biochemistry and Molecular Biology Education · Cited by 1,823 · see citing works
Laio, A., & Gervasio, F. L. (2008) · Reports on Progress in Physics · doi:10.1088/0034-4885/71/12/126601 · Cited by 1,619 · see citing works
Self-citationWolynes, P., Onuchic, J., & Thirumalai, D. (1995) · Science · doi:10.1126/science.7886447 · Cited by 1,080 · see citing works
Protein Folding: A Perspective from Theory and Experiment
Dobson, C. M., Šali, A., & Karplus, M. (1998) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(19980420)37:7 · Cited by 859 · see citing works
How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization?
Piana, S., Lindorff‐Larsen, K., & Shaw, D. E. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.03.051 · Cited by 798 · see citing works
Simultaneous determination of protein structure and dynamics
Lindorff‐Larsen, K., Best, R. B., DePristo, M. A., Dobson, C. M., & Vendruscolo, M. (2005) · Nature Cell Biology · doi:10.1038/nature03199 · Cited by 715 · see citing works
Self-citationSHIFTX2: significantly improved protein chemical shift prediction
Han, B., Liu, Y., Ginzinger, S., & Wishart, D. S. (2011) · Journal of Biomolecular NMR · doi:10.1007/s10858-011-9478-4 · Cited by 673 · see citing works
A simple model for calculating the kinetics of protein folding from three-dimensional structures
Muñoz, V., & Eaton, W. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.20.11311 · Cited by 636 · see citing works
A Bias-Exchange Approach to Protein Folding
Piana, S., & Laio, A. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp067873l · Cited by 600 · see citing works
Self-citationAnton, a special-purpose machine for molecular dynamics simulation
Shaw, D., Deneroff, M. M., Dror, R., Kuskin, J., Larson, R. H., Salmon, J., Young, C., Batson, B., Bowers, K., Chao, J. C., et al. (2008) · CACM · doi:10.1145/1364782.1364802 · Cited by 588 · see citing works
Shen, Y., & Bax, A. (2010) · Journal of Biomolecular NMR · doi:10.1007/s10858-010-9433-9 · Cited by 557 · see citing works
Screen Savers of the World Unite!
Shirts, M., & Pande, V. (2000) · Science · doi:10.1126/science.290.5498.1903 · Cited by 550 · see citing works
Protein Folding and Unfolding at Atomic Resolution
Fersht, A. R., & Daggett, V. (2002) · Cell · doi:10.1016/s0092-8674(02)00620-7 · Cited by 495 · see citing works
Equilibrium Free Energies from Nonequilibrium Metadynamics
Bussi, G., Laio, A., & Parrinello, M. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.090601 · Cited by 436 · see citing works
Self-citationThree key residues form a critical contact network in a protein folding transition state
Vendruscolo, M., Paci, E., Dobson, C. M., & Karplus, M. (2001) · Nature · doi:10.1038/35054591 · Cited by 429 · see citing works
Self-citationStructure of an Intermediate State in Protein Folding and Aggregation
Neudecker, P., Robustelli, P., Cavalli, A., Walsh, P., Lundström, P., Zarrine‐Afsar, A., Sharpe, S. J., Vendruscolo, M., & Kay, L. E. (2012) · Science · doi:10.1126/science.1214203 · Cited by 399 · see citing works
Self-citationPande, V. S., Baker, I., Chapman, J., Elmer, S. P., Khaliq, S., Larson, S. M., Rhee, Y. M., Shirts, M. R., Snow, C. D., Sorin, E. J., et al. (2002) · Biopolymers · doi:10.1002/bip.10219 · Cited by 370 · see citing works
Critical role of beta-hairpin formation in protein G folding.
McCallister, E. L., Alm, E. J., & Baker, D. A. (2000) · Nature Structural Biology · doi:10.1038/77971 · Cited by 368 · see citing works
Head swivel on the ribosome facilitates translocation by means of intra-subunit tRNA hybrid sites
Ratje, A. H., Loerke, J., Mikolajka, A., Brunner, M. J., Hildebrand, P. W., Starosta, A. L., Dönhöfer, A., Connell, S. R., Fucini, P., Mielke, T., et al. (2010) · Nature · doi:10.1038/nature09547 · Cited by 358 · see citing works
Ulmer, T. S., Ramirez, B. E., Delaglio, F., & Bax, A. (2003) · Journal of the American Chemical Society · doi:10.1021/ja0350684 · Cited by 301 · see citing works
A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations
Marinelli, F., Pietrucci, F., Laio, A., & Piana, S. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000452 · Cited by 274 · see citing works
Self-citationFast and Accurate Predictions of Protein NMR Chemical Shifts from Interatomic Distances
Kohlhoff, K., Robustelli, P., Cavalli, A., Salvatella, X., & Vendruscolo, M. (2009) · Journal of the American Chemical Society · doi:10.1021/ja903772t · Cited by 264 · see citing works
Self-citationGenomics-aided structure prediction
Sulkowska, J. I., Morcos, F., Weigt, M., Terence Hwa, B., & Onuchic, J. N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207864109 · Cited by 226 · see citing works
Pietrucci, F., & Laio, A. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct900202f · Cited by 196 · see citing works
Self-citationUsing NMR Chemical Shifts as Structural Restraints in Molecular Dynamics Simulations of Proteins
Robustelli, P., Kohlhoff, K., Cavalli, A., & Vendruscolo, M. (2010) · Structure · doi:10.1016/j.str.2010.04.016 · Cited by 164 · see citing works
Self-citationAlexander, P., Orban, J. P., & Bryan, P. N. (1992) · Biochemistry · doi:10.1021/bi00147a006 · Cited by 161 · see citing works
Christen, M., & van Gunsteren, W. F. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20725 · Cited by 160 · see citing works
Blanco, F. J., & Serrano, L. (1995) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.1995.0634h.x · Cited by 148 · see citing works
Atomistic molecular simulations of protein folding
Best, R. B. (2012) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2011.12.001 · Cited by 139 · see citing works
Molecular dynamics simulations of protein folding from the transition state
Gsponer, J. A., & Caflisch, A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.092686399 · Cited by 132 · see citing works
Blanco, F. J., & Serrano, L. G. (1995) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.1995.tb20605.x · Cited by 126 · see citing works
Determination of a Transition State at Atomic Resolution from Protein Engineering Data
Paci, E., Vendruscolo, M., Dobson, C. M., & Karplus, M. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00944-0 · Cited by 114 · see citing works
Self-citationMETAGUI. A VMD interface for analyzing metadynamics and molecular dynamics simulations
Biarnés, X., Pietrucci, F., Marinelli, F., & Laio, A. (2012) · Computer Physics Communications · doi:10.1016/j.cpc.2011.08.020 · Cited by 92 · see citing works
Self-citationPark, S.-H., O’Neil, K. T., & Röder, H. (1997) · Biochemistry · doi:10.1021/bi971914+ · Cited by 88 · see citing works
Camilloni, C., Robustelli, P., Simone, A. D., Cavalli, A., & Vendruscolo, M. (2012) · Journal of the American Chemical Society · doi:10.1021/ja210951z · Cited by 84 · see citing works
Self-citationExploring the protein G helix free‐energy surface by solute tempering metadynamics
Camilloni, C., Provasi, D., Tiana, G., & Broglia, R. A. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21852 · Cited by 76 · see citing works
Self-citationProtein Folding and Ligand-Enzyme Binding from Bias-Exchange Metadynamics Simulations
Baftizadeh, F., Cossio, P., Pietrucci, F., & Laio, A. (2012) · Current Physical Chemistry · doi:10.2174/1877946811202010079 · Cited by 58 · see citing works
Self-citationCamilloni, C., Broglia, R. A., & Tiana, G. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3523345 · Cited by 57 · see citing works
Self-citationVelázquez-Muriel, J. A., Lasker, K., Russel, D., Phillips, J. L., Webb, B., Schneidman‐Duhovny, D., & Šali, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216549109 · Cited by 42 · see citing works
Determination of Conformational Equilibria in Proteins Using Residual Dipolar Couplings
Simone, A. D., Montalvão, R. W., & Vendruscolo, M. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct200361b · Cited by 39 · see citing works
Self-citationGB1 Is Not a Two-State Folder: Identification and Characterization of an On-Pathway Intermediate
Morrone, A., Giri, R., Toofanny, R. D., Travaglini‐Allocatelli, C., Brunori, M., Daggett, V., & Gianni, S. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.09.013 · Cited by 36 · see citing works
ADAPT: A molecular mechanics approach for studying the structural properties of long DNA sequences
Lafontaine, I., & Lavery, R. (2000) · Biopolymers · doi:10.1002/1097-0282(2000)56:4 · Cited by 21 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wei, G., Xi, W., Nussinov, R., & Ma, B. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00562 · Cited by 436 · see citing works
Spectral gap optimization of order parameters for sampling complex molecular systems
Tiwary, P., & Berne, B. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600917113 · Cited by 259 · see citing works
Huggins, D. J., Biggin, P. C., Dämgen, M. A., Essex, J. W., Harris, S. A., Henchman, R. H., Khalid, S., Kuzmanic, A., Laughton, C. A., Michel, J., et al. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1393 · Cited by 169 · see citing works
Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76 · see citing works
- Authors
- Granata, D., Camilloni, C., Vendruscolo, M., & Laio, A.
- Affiliations
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- University of Cambridge (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-04-09
- Volume
- 110
- Issue
- 17
- Pages
- 6817-6822
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3637744
- OpenAlex
- W1972594892
- Cited by
- 143
- References
- 61
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 7 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 15 · 2019: 13 · 2018: 20 · 2017: 18 · 2016: 15 · 2015: 16 · 2014: 14 · 2013: 7
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- SDL (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- free energy landscape, metadynamics, NMR chemical shifts, collective variables, protein folding, protein G, globotriaosylceramide, folding intermediates, conformational sampling, transition state, conformational ensembles
- MeSH terms
- Bacterial Proteins, Models, Molecular, Magnetic Resonance Spectroscopy, Protein Conformation, Thermodynamics, Protein Folding, Molecular Dynamics Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
