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dc.contributor.authorLi, Qiwei
Dahl, David B.
Vannucci, Marina
Joo, Hyun
Tsai, Jerry W.
dc.date.accessioned 2015-01-06T19:20:34Z
dc.date.available 2015-01-06T19:20:34Z
dc.date.issued 2014
dc.identifier.citation Li, Qiwei, Dahl, David B., Vannucci, Marina, et al.. "Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction." PLoS ONE, 9, no. 10 (2014) Public Library of Science: e109832. http://dx.doi.org/10.1371/journal.pone.0109832.
dc.identifier.urihttps://hdl.handle.net/1911/78895
dc.description.abstract Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a proteinメs secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.
dc.language.iso eng
dc.publisher Public Library of Science
dc.rights 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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction
dc.type Journal article
dc.contributor.funder National Institutes of Health National Institute of General Medical Sciences
dc.citation.journalTitle PLoS ONE
dc.citation.volumeNumber 9
dc.citation.issueNumber 10
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pone.0109832
dc.identifier.pmcid PMC4196994
dc.identifier.pmid 25314659
dc.identifier.grantID R01 GM104972 (National Institutes of Health National Institute of General Medical Sciences)
dc.type.publication publisher version
dc.citation.firstpage e109832


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