Distill : a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

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dc.contributor.author Baù, Davide
dc.contributor.author Martin, Alberto J. M.
dc.contributor.author Mooney, Catherine
dc.contributor.author Vullo, Alessandro
dc.contributor.author Walsh, Ian
dc.contributor.author Pollastri, Gianluca
dc.date.accessioned 2012-01-20T14:59:36Z
dc.date.available 2012-01-20T14:59:36Z
dc.date.copyright 2006 Baú et al; licensee BioMed Central Ltd en
dc.date.issued 2006-09-05
dc.identifier.citation BMC Bioinformatics en
dc.identifier.uri http://hdl.handle.net/10197/3444
dc.description.abstract We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non- redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of Cα traces for short proteins (up to 200 amino acids). The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA), 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein Cα traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348). The majority of the servers, including the Cα trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: http://distill.ucd.ie/distill/. 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.description.sponsorship Other funder en
dc.format.extent 417784 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 http://creativecommons.org/licenses/by/2.0/ en
dc.rights.uri CC BY 2.0 en
dc.subject Protein structure prediction en
dc.subject Secondary structure en
dc.subject Structural motifs en
dc.subject Neural networks en
dc.subject.lcsh Proteins--Analysis en
dc.subject.lcsh Neural networks (Computer science) en
dc.title Distill : a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins 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/1471-2105/7/402 en
dc.status Peer reviewed en
dc.identifier.volume 7 en
dc.identifier.issue September 2006 en
dc.identifier.startpage 402 en
dc.identifier.doi 10.1186/1471-2105-7-402
dc.neeo.contributor Baù|Davide|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.neeo.contributor Walsh|Ian|aut| en
dc.neeo.contributor Pollastri|Gianluca|aut| en
dc.description.othersponsorship UCD President's Award 2004 en
dc.description.admin au, da, sp, ke, ab - kpw13/1/12 en


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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. Except where otherwise noted, this item's license is described as 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.

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