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

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Title: Protein structural motif prediction in multidimensional φ-ψ space leads to improved secondary structure prediction
Authors: Mooney, Catherine
Vullo, Alessandro
Pollastri, Gianluca
Permanent link: http://hdl.handle.net/10197/3393
Date: 24-Oct-2006
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+/.
Funding Details: Science Foundation Ireland
Irish Research Council for Science, Engineering and Technology
Health Research Board
Type of material: Journal Article
Publisher: Mary Ann Liebert
Copyright (published version): 2011 Mary Ann Liebert, Inc
Keywords: Protein structure prediction;Secondary structure;Structural motifs;Neural networks
Subject LCSH: Proteins--Structure
Neural networks (Computer science)
Multidimensional scaling
DOI: 10.1089/cmb.2006.13.1489
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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