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Water Dispersion Interactions Strongly Influence Simulated Structural Properties of Disordered Protein States

Open access
Piana, Stefano, Donchev, Alexander G., Robustelli, Paul, Shaw, David E.·2015·The Journal of Physical Chemistry B

Abstract

Many proteins can be partially or completely disordered under physiological conditions. Structural characterization of these disordered states using experimental methods can be challenging, since they are composed of a structurally heterogeneous ensemble of conformations rather than a single dominant conformation. Molecular dynamics (MD) simulations should in principle provide an ideal tool for elucidating the composition and behavior of disordered states at an atomic level of detail. Unfortunately, MD simulations using current physics-based models tend to produce disordered-state ensembles that are structurally too compact relative to experiments. We find that the water models typically used in MD simulations significantly underestimate London dispersion interactions, and speculate that this may be a possible reason for these erroneous results. To test this hypothesis, we create a new water model, TIP4P-D, that approximately corrects for these deficiencies in modeling water dispersion interactions while maintaining compatibility with existing physics-based models. We show that simulations of solvated proteins using this new water model typically result in disordered states that are substantially more expanded and in better agreement with experiment. These results represent a significant step toward extending the range of applicability of MD simulations to include the study of (partially or fully) disordered protein states.

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Metadata

Authors
Piana, S., Donchev, A. G., Robustelli, P., & Shaw, D. E.
Venue
The Journal of Physical Chemistry B
Published
2015-04-13
Volume
119
Issue
16
Pages
5113-5123
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
OpenAlex
W1968790827
Cited by
852
References
2
FWCI
24.70
Type
journal article
Language
en
Open access
hybrid

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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