Protein Backbone Angle Prediction in Multidimensional φ-ψ Space

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Title: Protein Backbone Angle Prediction in Multidimensional φ-ψ Space
Authors: Mooney, CatherineVullo, AlessandroPollastri, Gianluca
Permanent link: http://hdl.handle.net/10197/12353
Date: 20-Jan-2006
Online since: 2021-07-28T15:20:01Z
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 3 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 structural motif prediction on an independent test set for di-peptides (6 classes), tripeptides (8 classes) and tetra-peptides (14 classes), respectively, 28-30% above base-line statistical predictors. To demonstrate that structural motif predictions contain relevant structural information, we build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into 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, outperforming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. All the predictive systems will be provided free of charge to academic users and made publicly available at the address http://distill.ucd.ie/.
Funding Details: Health Research Board
Irish Research Council for Science, Engineering and Technology
Science Foundation Ireland
Funding Details: UCD President’s Award 2004
Type of material: Technical Report
Publisher: University College Dublin. School of Computer Science and Informatics
Series/Report no.: UCD CSI Technical Reports; ucd-csi-2006-1
Copyright (published version): 2006 the Authors
Keywords: Amino acid sequencesProtein structuresPeptide predictionPeptide classificationComputational biology
Other versions: https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html
Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:CASL Research Collection
Computer Science and Informatics Technical Reports

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