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Cited by 397

The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.

Protein-Folding Dynamics: Overview of Molecular Simulation Techniques

Scheraga, Harold A., Khalili, Mey, Liwo, Adam·2007·Annual Review of Physical Chemistry

Abstract

Molecular dynamics (MD) is an invaluable tool with which to study protein folding in silico. Although just a few years ago the dynamic behavior of a protein molecule could be simulated only in the neighborhood of the experimental conformation (or protein unfolding could be simulated at high temperature), the advent of distributed computing, new techniques such as replica-exchange MD, new approaches (based on, e.g., the stochastic difference equation), and physics-based reduced models of proteins now make it possible to study protein-folding pathways from completely unfolded structures. In this review, we present algorithms for MD and their extensions and applications to protein-folding studies, using all-atom models with explicit and implicit solvent as well as reduced models of polypeptide chains.

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Crossref · 10.1146/annurev.physchem.58.032806.104614

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Metadata

Authors
Scheraga, H. A., Khalili, M., & Liwo, A.
Venue
Annual Review of Physical Chemistry
Published
2007-05-01
Volume
58
Issue
1
Pages
57-83
ISSN
  • 0066-426X (ISSN-L)
  • 1545-1593
OpenAlex
W2108102848
Cited by
397
References
122
FWCI
0.92
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Protein-Folding Dynamics: Overview of Molecular Simulation Techniques. https://app.cochat.ai/papers/10.1146/annurev.physchem.58.032806.104614
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Scheraga, H. A., Khalili, M., & Liwo, A. (2007). Protein-Folding Dynamics: Overview of Molecular Simulation Techniques. Annual Review of Physical Chemistry. https://doi.org/10.1146/annurev.physchem.58.032806.104614

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

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.