Conformational Preferences of an Intrinsically Disordered Protein Domain: A Case Study for Modern Force Fields
Open accessAbstract
, ff99SB*-ILDNP with TIP3P and TIP4P-D, and FB15 with TIP3P-FB water. Comparing the results from REST2-enhanced sampling simulations with experimental CD spectra and secondary chemical shifts reveals that the ff99SB-disp force field can realistically capture the broad and mildly helical structural ensemble of free c-Myb. The structural ensembles yielded by CHARMM36m, ff99SB*-ILDNP together with TIP4P-D water, and FB15 are also mildly helical; however, each of these force fields can be assigned a specific subset of c-Myb residues for which the simulations could not reproduce the experimental secondary chemical shifts. In addition, microsecond-timescale MD simulations of the KIX/c-Myb complex show that most force fields used preserve a stable helix fold of c-Myb in the complex. Still, all force fields predict a KIX/c-Myb complex interface that differs slightly from the structures provided by NMR because several NOE-derived distances between KIX and c-Myb were exceeded in the simulations. Taken together, the ff99SB-disp force field in the first place but also CHARMM36m, ff99SB*-ILDNP together with TIP4P-D water, and FB15 can be suitable choices for future simulation studies of the coupled folding and binding mechanism of the KIX/c-Myb complex and potentially also other IDPs.
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?
Disordered proteins and regions are characterized by broad and heterogeneous ensembles with small differences in relative conformational stabilities and low barriers.
“Disordered proteins (regions) are characterized by broad and heterogeneous ensembles where the differences in the relative conformational stabilities are small, or even minor and the barriers are low.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [128]
Cites
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
Molecular dynamics with coupling to an external bath
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,626 · see citing works
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015) · SoftwareX · doi:10.1016/j.softx.2015.06.001 · Cited by 27,190 · 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
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,034 · see citing works
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp973084f · Cited by 14,270 · see citing works
Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models
Miyamoto, S., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130805 · Cited by 7,268 · see citing works
PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data.
Roe, D., & Cheatham, T. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400341p · Cited by 6,969 · see citing works
CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins
Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 6,708 · see citing works
Improved side-chain torsion potentials for the Amber ff99SB protein force field
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J., Dror, R., & Shaw, D. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,037 · 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
How to study proteins by circular dichroism
Kelly, S. M., Jess, T. J., & Price, N. C. (2005) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2005.06.005 · Cited by 3,356 · see citing works
Sreerama, N., & Woody, R. W. (2000) · Analytical Biochemistry · doi:10.1006/abio.2000.4880 · Cited by 2,997 · see citing works
Intrinsically disordered proteins in cellular signalling and regulation
Wright, P. E., & Dyson, H. J. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3920 · Cited by 2,371 · see citing works
Classification of Intrinsically Disordered Regions and Proteins
van der Lee, R., Buljan, M., Lang, B., Weatheritt, R. J., Daughdrill, G. W., Dunker, A. K., Fuxreiter, M., Gough, J., Gsponer, J., Jones, D. T., et al. (2014) · Chemical Reviews · doi:10.1021/cr400525m · Cited by 2,155 · see citing works
CBP/p300 in cell growth, transformation, and development
Goodman, R. H., & Smolik, S. (2000) · Genes & Development · doi:10.1101/gad.14.13.1553 · Cited by 1,778 · see citing works
Intrinsically Disordered Proteins and Intrinsically Disordered Protein Regions
Oldfield, C. J., & Dunker, A. K. (2014) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-072711-164947 · Cited by 1,057 · see citing works
Developing a molecular dynamics force field for both folded and disordered protein states
Robustelli, P., Piana, S., & Shaw, D. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800690115 · Cited by 1,047 · see citing works
Piana, S., Donchev, A., Robustelli, P., & Shaw, D. E. (2015) · The Journal of Physical Chemistry B · doi:10.1021/jp508971m · Cited by 855 · see citing works
Optimized Molecular Dynamics Force Fields Applied to the Helix−Coil Transition of Polypeptides
Best, R. B., & Hummer, G. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp901540t · Cited by 836 · see citing works
Louis‐Jeune, C., Andrade-Navarro, M. A., & Perez‐Iratxeta, C. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23188 · Cited by 746 · see citing works
A flexible algorithm for calculating pair interactions on SIMD architectures
Páll, S., & Hess, B. (2013) · Computer Physics Communications · doi:10.1016/j.cpc.2013.06.003 · Cited by 715 · see citing works
Wang, L.-L., Friesner, R., & Berne, B. (2011) · Journal of Physical Chemistry B · doi:10.1021/jp204407d · Cited by 713 · see citing works
Fukunishi, H., Watanabe, O., & Takada, S. (2002) · The Journal of Chemical Physics · doi:10.1063/1.1472510 · Cited by 713 · see citing works
Replica exchange with solute tempering: a method for sampling biological systems in explicit water.
Liu, P., Kim, B., Friesner, R., & Berne, B. (2005) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0506346102 · Cited by 698 · see citing works
Systematic Validation of Protein Force Fields against Experimental Data
Lindorff‐Larsen, K., Maragakis, P., Piana, S., Eastwood, M. P., Dror, R. O., & Shaw, D. E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032131 · Cited by 648 · see citing works
Analyzing protein circular dichroism spectra for accurate secondary structures
Johnson, W. C. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990515)35:3 · Cited by 639 · 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
Rauscher, S., Gapsys, V., Gajda, M. J., Zweckstetter, M., de Groot, B. L., & Grubmüller, H. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00736 · Cited by 408 · see citing works
Relating sequence encoded information to form and function of intrinsically disordered proteins
Das, R. K., Ruff, K. M., & Pappu, R. V. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.03.008 · Cited by 400 · see citing works
Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements
Beauchamp, K., Lin, Y.-S., Das, R., & Pande, V. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct2007814 · Cited by 394 · see citing works
Best, R., Zheng, W.-W., & Mittal, J. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500569b · Cited by 312 · see citing works
Hamiltonian replica exchange in GROMACS: a flexible implementation
Bussi, G. (2013) · Molecular Physics · doi:10.1080/00268976.2013.824126 · Cited by 277 · see citing works
Henriques, J., Cragnell, C., & Skepö, M. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct501178z · Cited by 265 · see citing works
Arai, M., Sugase, K., Dyson, H. J., & Wright, P. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1512799112 · Cited by 250 · see citing works
Structure and Dynamics of an Unfolded Protein Examined by Molecular Dynamics Simulation
Lindorff‐Larsen, K., Trbovic, N., Maragakis, P., Piana, S., & Shaw, D. E. (2012) · Journal of the American Chemical Society · doi:10.1021/ja209931w · Cited by 249 · see citing works
Aliev, A. E., Kulke, M., Khaneja, H. S., Chudasama, V., Sheppard, T. D., & Lanigan, R. M. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24350 · Cited by 244 · see citing works
New developments in force fields for biomolecular simulations
Nerenberg, P. S., & Head-Gordon, T. L. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.02.002 · Cited by 236 · see citing works
Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15
Wang, L., McKiernan, K. A., Gomes, J., Beauchamp, K. A., Head-Gordon, T. L., Rice, J. E., Swope, W. C., Martı́nez, T. J., & Pande, V. S. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b02320 · Cited by 235 · see citing works
Bayesian ensemble refinement by replica simulations and reweighting
Hummer, G., & Köfinger, J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4937786 · Cited by 229 · see citing works
Computational and theoretical advances in studies of intrinsically disordered proteins
Best, R. B. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.01.006 · Cited by 211 · see citing works
Water models for biomolecular simulations
Onufriev, A. V., & Izadi, S. (2017) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1347 · Cited by 209 · see citing works
Solution Structure of the KIX Domain of CBP Bound to the Transactivation Domain of c-Myb
Zor, T., Guzman, R. N. D., Dyson, H. J., & Wright, P. E. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.01.038 · Cited by 206 · see citing works
Scrutinizing Molecular Mechanics Force Fields on the Submicrosecond Timescale with NMR Data
Lange, O. F., van der Spoel, D., & de Groot, B. L. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.04.062 · Cited by 199 · see citing works
Song, D., Luo, R., & Chen, H. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00135 · Cited by 198 · see citing works
Debiec, K. T., Cerutti, D. S., Baker, L. R., Gronenborn, A. M., Case, D. A., & Chong, L. T. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00567 · Cited by 192 · see citing works
Chodera, J., & Shirts, M. (2011) · Journal of Chemical Physics · doi:10.1063/1.3660669 · Cited by 183 · see citing works
Functional advantages of dynamic protein disorder
Berlow, R. B., Dyson, H. J., & Wright, P. E. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.06.003 · Cited by 181 · see citing works
Force field development and simulations of intrinsically disordered proteins
Huang, J., & MacKerell, A. D. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.008 · Cited by 173 · see citing works
Zor, T., Mayr, B. M., Dyson, H. J., Montminy, M., & Wright, P. E. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m207361200 · Cited by 169 · see citing works
Modeling Intrinsically Disordered Proteins with Bayesian Statistics
Fisher, C. K., Huang, A., & Stultz, C. M. (2010) · Journal of the American Chemical Society · doi:10.1021/ja105832g · Cited by 166 · see citing works
On easy implementation of a variant of the replica exchange with solute tempering in GROMACS
Terakawa, T., Kameda, T., & Takada, S. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21703 · Cited by 137 · see citing works
Comparison of force fields for Alzheimer's A: A case study for intrinsically disordered proteins
Carballo‐Pacheco, M., & Strodel, B. (2016) · Protein Science · doi:10.1002/pro.3064 · Cited by 134 · see citing works
Finding Our Way in the Dark Proteome
Bhowmick, A., Brookes, D. H., Yost, S. R., Dyson, H. J., Forman-Kay, J. D., Gunter, D., Head-Gordon, M., Hura, G. L., Pande, V. S., Wemmer, D. E., et al. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b06543 · Cited by 128 · see citing works
Accuracy of Current All-Atom Force-Fields in Modeling Protein Disordered States
Palazzesi, F., Prakash, M. K., Bonomi, M., & Barducci, A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500718s · Cited by 120 · see citing works
Helix nucleation kinetics from molecular simulations in explicit solvent
Hummer, G., Garcı́a, A. E., & Garde, S. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20010101)42:1 · Cited by 107 · see citing works
ff14ipq: A Self-Consistent Force Field for Condensed-Phase Simulations of Proteins
Cerutti, D. S., Swope, W. C., Rice, J. E., et al. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500643c · Cited by 102 · see citing works
Giri, R., Morrone, A., Toto, A., Brunori, M., & Gianni, S. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307337110 · Cited by 99 · see citing works
SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures
Nagy, G., Igaev, M., Jones, N. C., Hoffmann, S. V., & Grubmüller, H. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00203 · Cited by 94 · see citing works
Shammas, S. L., Travis, A. J., & Clarke, J. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp404267e · Cited by 91 · see citing works
Water Model Tuning for Improved Reproduction of Rotational Diffusion and NMR Spectral Density
Takemura, K., & Kitao, A. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp301100g · Cited by 91 · see citing works
Computer Simulations of Intrinsically Disordered Proteins
Chong, S., Chatterjee, P., & Ham, S. (2017) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-052516-050843 · Cited by 84 · see citing works
Meng, W., Lyle, N. J., Luan, B., Raleigh, D. P., & Pappu, R. V. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216979110 · Cited by 80 · see citing works
Experimental Inferential Structure Determination of Ensembles for Intrinsically Disordered Proteins
Brookes, D. H., & Head-Gordon, T. L. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b00351 · Cited by 79 · see citing works
Mercadante, D., Milles, S., Fuertes, G., Svergun, D. I., Lemke, E. A., & Gräter, F. (2015) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5b03440 · Cited by 77 · see citing works
Benchmarking all-atom simulations using hydrogen exchange
Skinner, J. J., Yu, W., Gichana, E., Baxa, M. C., Hinshaw, J. R., Freed, K. F., & Sosnick, T. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1404213111 · Cited by 73 · see citing works
Lincoff, J., Haghighatlari, M., Krzeminski, M., et al. (2020) · Communications Chemistry · doi:10.1038/s42004-020-0323-0 · Cited by 67 · see citing works
Hermann, M. R., & Hub, J. S. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00338 · Cited by 63 · see citing works
Secondary Structure of Rat and Human Amylin across Force Fields
Hoffmann, K. Q., McGovern, M., Chiu, C., & Pablo, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134091 · Cited by 59 · see citing works
Liu, H., Song, D., Zhang, Y., Yang, S., Luo, R., & Chen, H. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp03434j · Cited by 54 · see citing works
Yang, S., Liu, H., Zhang, Y., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00647 · Cited by 50 · see citing works
Iesmantavicius, V., Jensen, M. R., Ozenne, V., Blackledge, M., Poulsen, F., & Kjaergaard, M. (2013) · Journal of the American Chemical Society · doi:10.1021/ja4045532 · Cited by 47 · see citing works
Yu, L., Li, D., & Brüschweiler, R. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01062 · Cited by 47 · see citing works
The combined force field-sampling problem in simulations of disordered amyloid-β peptides
Lincoff, J., Sasmal, S., & Head-Gordon, T. L. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5078615 · Cited by 43 · see citing works
Ithuralde, R. E., Roitberg, A. E., & Turjanski, A. G. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b02016 · Cited by 35 · see citing works
Peng, E., Todorova, N., & Yarovsky, I. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186219 · Cited by 34 · see citing works
Rotational and Translational Diffusion of Proteins as a Function of Concentration
Bashardanesh, Z., Elf, J., Zhang, H., & van der Spoel, D. (2019) · ACS Omega · doi:10.1021/acsomega.9b02835 · Cited by 24 · see citing works
Papaleo, E., Camilloni, C., Teilum, K., Vendruscolo, M., & Lindorff‐Larsen, K. (2018) · PeerJ · doi:10.7717/peerj.5125 · Cited by 23 · see citing works
How accurate is circular dichroism-based model validation?
Nagy, G., & Grubmüller, H. (2020) · European Biophysics Journal · doi:10.1007/s00249-020-01457-6 · Cited by 22 · see citing works
Conformation dynamics of the intrinsically disordered protein c-Myb with the ff99IDPs force field
Guo, X. J., Han, J., Luo, R., et al. (2017) · RSC Advances · doi:10.1039/c7ra04133k · Cited by 17 · see citing works
Zou, J., Simmerling, C. L., & Raleigh, D. P. (2019) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b08323 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
- Authors
- Gopal, S. M., Wingbermühle, S., Schnatwinkel, J., Juber, S., Herrmann, C., & Schäfer, L. V.
- Affiliations
- Ruhr University Bochum (DE)
- Corresponding author
- Schäfer, Lars V.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-12-31
- Volume
- 125
- Issue
- 1
- Pages
- 24-35
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W3116254766
- Cited by
- 37
- References
- 81
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 6 · 2023: 6 · 2022: 8 · 2021: 9
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Physical Chemistry B (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (331085229, EXC 2033 -- 390677874 -- RESOLV, GRK2376)
- 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
- intrinsically disordered proteins, MYB, KIX domain, force field comparison, CHARMM36m, water model, conformational ensembles, coupled folding and binding, protein-protein complex
- MeSH terms
- Computer Simulation, Molecular Conformation, Magnetic Resonance Spectroscopy, Protein Conformation, Water, Molecular Dynamics Simulation, Intrinsically Disordered Proteins
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