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Sampling Realistic Protein Conformations Using Local Structural Bias

Overview of attention for article published in PLoS Computational Biology, September 2006
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Title
Sampling Realistic Protein Conformations Using Local Structural Bias
Published in
PLoS Computational Biology, September 2006
DOI 10.1371/journal.pcbi.0020131
Pubmed ID
Authors

Thomas Hamelryck, John T Kent, Anders Krogh

Abstract

The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to attack the problem: a conformational sampling method generates plausible candidate structures, which are subsequently accepted or rejected using an energy function. Conceptually, this often corresponds to separating local structural bias from the long-range interactions that stabilize the compact, native state. However, sampling protein conformations that are compatible with the local structural bias encoded in a given protein sequence is a long-standing open problem, especially in continuous space. We describe an elegant and mathematically rigorous method to do this, and show that it readily generates native-like protein conformations simply by enforcing compactness. Our results have far-reaching implications for protein structure prediction, determination, simulation, and design.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Canada 3 3%
United Kingdom 3 3%
Germany 2 2%
Switzerland 1 1%
India 1 1%
Italy 1 1%
Colombia 1 1%
Belgium 1 1%
Other 3 3%
Unknown 77 78%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 32%
Researcher 19 19%
Student > Master 13 13%
Other 6 6%
Professor > Associate Professor 5 5%
Other 15 15%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 39%
Computer Science 14 14%
Chemistry 8 8%
Biochemistry, Genetics and Molecular Biology 8 8%
Engineering 7 7%
Other 12 12%
Unknown 11 11%