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Beyond the twilight zone : automated prediction of structural properties of proteins by recursive neural networks and remote homology information
Author(s)
Date Issued
2009-10
Date Available
2012-01-20T14:33:08Z
Abstract
The prediction of 1D structural properties of proteins is an important step toward the prediction of protein structure and function, not only in the ab initio case but also when homology information to known structures is available. Despite this the vast majority of 1D predictors do not incorporate homology information into the prediction process. We
develop a novel structural alignment method,
SAMD, which we use to build alignments of putative remote homologues that we compress into templates of structural frequency profiles. We use these templates as additional input to ensembles of recursive
neural networks, which we specialise for the prediction of query sequences that show only remote homology to any Protein Data Bank structure. We predict four 1D structural properties – secondary structure, relative solvent accessibility, backbone structural motifs, and contact density. Secondary structure prediction accuracy, tested by five-fold cross-validation on a large set of proteins allowing less than 25% sequence identity between training and test set and query sequences and templates, exceeds 82%, outperforming its ab initio counterpart, other state-of-the-art secondary structure predictors (Jpred 3 and PSIPRED) and two other systems based on PSI-BLAST and COMPASS templates. We show that structural information from homologues improves prediction accuracy well beyond the
Twilight Zone of sequence similarity, even below 5% sequence identity, for all four structural properties. Significant improvement over the extraction of
structural information directly from PDB templates suggests that the combination of sequence and template information is more informative than templates alone.
Sponsorship
Science Foundation Ireland
Health Research Board
Type of Material
Journal Article
Publisher
Wiley InterScience
Journal
Proteins: Structure, Function, and Bioinformatics
Volume
77
Issue
1
Start Page
181
End Page
190
Copyright (Published Version)
2009 Wiley-Liss, Inc.
Subject – LCSH
Sequence alignment (Bioinformatics)
Homology (Biology)
Machine learning
Web versions
Language
English
Status of Item
Peer reviewed
ISSN
1097-0134
This item is made available under a Creative Commons License
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