Protein structural motif prediction in multidimensional φ-ψ space leads to improved secondary structure prediction

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dc.contributor.author Mooney, Catherine
dc.contributor.author Vullo, Alessandro
dc.contributor.author Pollastri, Gianluca
dc.date.accessioned 2011-12-12T10:18:17Z
dc.date.available 2011-12-12T10:18:17Z
dc.date.copyright 2011 Mary Ann Liebert, Inc en
dc.date.issued 2006-10-24
dc.identifier.citation Journal of Computational Biology en
dc.identifier.uri http://hdl.handle.net/10197/3393
dc.description.abstract A significant step towards establishing the structure and function of a protein is the prediction of the local conformation of the polypeptide chain. In this article, we present systems for the prediction of three new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple φ-ψ angle values collected from high-resolution protein structures. The predictive systems, based on ensembles of bidirectional recurrent neural network architectures, and trained on a large non-redundant set of protein structures, achieve 72%, 66%, and 60% correct motif prediction on an independent test set for di-peptides (six classes), tri-peptides (eight classes) and tetra-peptides (14 classes), respectively, 28–30% above baseline statistical predictors. We then build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into a traditional 3-class (helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using the “hard” CASP 3-class assignment, and 81.4% with a more lenient assignment, outper- forming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. The structural motif predictor is publicly available at: http://distill.ucd.ie/porter+/. 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 283397 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Mary Ann Liebert en
dc.rights This is a copy of an article published in the JOURNAL OF COMPUTATIONAL BIOLOGY © 2011 Mary Ann Liebert, Inc.; JOURNAL OF COMPUTATIONAL BIOLOGY is available online at: http://www.liebertonline.com 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--Structure en
dc.subject.lcsh Neural networks (Computer science) en
dc.subject.lcsh Multidimensional scaling en
dc.title Protein structural motif prediction in multidimensional φ-ψ space leads to improved secondary structure prediction en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://dx.doi.org/10.1089/cmb.2006.13.1489 en
dc.status Peer reviewed en
dc.identifier.volume 13 en
dc.identifier.issue 8 en
dc.identifier.startpage 1489 en
dc.identifier.endpage 1502 en
dc.identifier.doi 10.1089/cmb.2006.13.1489
dc.neeo.contributor Mooney|Catherine|aut| en
dc.neeo.contributor Vullo|Alessandro|aut| en
dc.neeo.contributor Pollastri|Gianluca|aut| en
dc.description.admin au, ti, ke, ab - kpw28/11/11 en


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