Hamelryck, T., Kent, J.T. and Krogh, A. (2006) Sampling Realistic Protein Conformations Using Local Structural Bias. PLoS Computational Biology, 2 (9). e131. ISSN 1553-734X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2006 Hamelryck et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Sherpa Assistant |
Date Deposited: | 06 Nov 2009 12:07 |
Last Modified: | 04 Nov 2016 04:01 |
Published Version: | http://dx.doi.org/10.1371/journal.pcbi.0020131 |
Status: | Published |
Publisher: | Public Library of Science |
Identification Number: | doi: 10.1371/journal.pcbi.0020131 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:10104 |