Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

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dc.contributor.author Pollastri, Gianluca
dc.contributor.author Martin, Alberto J. M.
dc.contributor.author Mooney, Catherine
dc.contributor.author Vullo, Alessandro
dc.date.accessioned 2011-12-12T11:19:41Z
dc.date.available 2011-12-12T11:19:41Z
dc.date.copyright 2007 Pollastri et al; licensee BioMed Central Ltd. en
dc.date.issued 2007-06-14
dc.identifier.citation BMC Bioinformatics en
dc.identifier.uri http://hdl.handle.net/10197/3394
dc.description.abstract Background : Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. Results: Here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of PDB templates. We compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. We show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. For sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. Gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available. Conclusion: The predictive system are publicly available at the address http://distill.ucd.ie en
dc.description.sponsorship Science Foundation Ireland en
dc.description.sponsorship Irish Research Council for Science, Engineering and Technology en
dc.description.sponsorship Health Research Board en
dc.format.extent 357570 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher BioMed Central en
dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en
dc.rights.uri CC BY 2.0 en
dc.rights.uri http://creativecommons.org/licenses/by/2.0 en
dc.subject Secondary structure en
dc.subject Solvent accessibility en
dc.subject Neural networks en
dc.subject Protein structure prediction en
dc.subject.lcsh Proteins--Structure en
dc.subject.lcsh Homology (Biology) en
dc.subject.lcsh Neural networks (Computer science) en
dc.title Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://www.biomedcentral.com/1471-2105/8/201 en
dc.status Peer reviewed en
dc.identifier.volume 8 en
dc.identifier.issue 201 en
dc.identifier.doi 10.1186/1471-2105-8-201
dc.neeo.contributor Pollastri|Gianluca|aut| en
dc.neeo.contributor Martin|Alberto J. M.|aut| en
dc.neeo.contributor Mooney|Catherine|aut| en
dc.neeo.contributor Vullo|Alessandro|aut| en
dc.description.othersponsorship UCD President's Award 2004 en
dc.description.admin au, da, ke, ab, sp - kpw30/11/11 en


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