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Systematic Validation of Protein Force Fields against Experimental Data

Overview of attention for article published in PLOS ONE, February 2012
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Title
Systematic Validation of Protein Force Fields against Experimental Data
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0032131
Pubmed ID
Authors

Kresten Lindorff-Larsen, Paul Maragakis, Stefano Piana, Michael P. Eastwood, Ron O. Dror, David E. Shaw

Abstract

Molecular dynamics simulations provide a vehicle for capturing the structures, motions, and interactions of biological macromolecules in full atomic detail. The accuracy of such simulations, however, is critically dependent on the force field--the mathematical model used to approximate the atomic-level forces acting on the simulated molecular system. Here we present a systematic and extensive evaluation of eight different protein force fields based on comparisons of experimental data with molecular dynamics simulations that reach a previously inaccessible timescale. First, through extensive comparisons with experimental NMR data, we examined the force fields' abilities to describe the structure and fluctuations of folded proteins. Second, we quantified potential biases towards different secondary structure types by comparing experimental and simulation data for small peptides that preferentially populate either helical or sheet-like structures. Third, we tested the force fields' abilities to fold two small proteins--one α-helical, the other with β-sheet structure. The results suggest that force fields have improved over time, and that the most recent versions, while not perfect, provide an accurate description of many structural and dynamical properties of proteins.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 23 3%
Germany 10 1%
United Kingdom 10 1%
Canada 4 <1%
Australia 4 <1%
Spain 3 <1%
Italy 2 <1%
Switzerland 2 <1%
Portugal 2 <1%
Other 10 1%
Unknown 670 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 215 29%
Researcher 161 22%
Student > Master 66 9%
Student > Bachelor 59 8%
Student > Doctoral Student 33 4%
Other 110 15%
Unknown 96 13%
Readers by discipline Count As %
Chemistry 174 24%
Agricultural and Biological Sciences 159 21%
Biochemistry, Genetics and Molecular Biology 108 15%
Physics and Astronomy 70 9%
Engineering 28 4%
Other 85 11%
Unknown 116 16%