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
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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
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
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Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:CASL Research Collection
Computer Science and Informatics Technical Reports

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