Burkoff, Nikolas S., Várnai, Csilla and Wild, David L. (2013) Predicting protein : sheet contacts using a maximum entropy-based correlated mutation measure. Bioinformatics, Volume 29 (Number 5). pp. 580-587. doi:10.1093/bioinformatics/btt005 ISSN 1367-4803.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Request Changes to record.

Abstract

Motivation: The problem of ab initio protein folding is one of the most difficult in modern computational biology. The prediction of residue contacts within a protein provides a more tractable immediate step. Recently introduced maximum entropy-based correlated mutation measures (CMMs), such as direct information, have been successful in predicting residue contacts. However, most correlated mutation studies focus on proteins that have large good-quality multiple sequence alignments (MSA) because the power of correlated mutation analysis falls as the size of the MSA decreases. However, even with small autogenerated MSAs, maximum entropy-based CMMs contain information. To make use of this information, in this article, we focus not on general residue contacts but contacts between residues in β-sheets. The strong constraints and prior knowledge associated with β-contacts are ideally suited for prediction using a method that incorporates an often noisy CMM.
Results: Using contrastive divergence, a statistical machine learning technique, we have calculated a maximum entropy-based CMM. We have integrated this measure with a new probabilistic model for β-contact prediction, which is used to predict both residue- and strand-level contacts. Using our model on a standard non-redundant dataset, we significantly outperform a 2D recurrent neural network architecture, achieving a 5% improvement in true positives at the 5% false-positive rate at the residue level. At the strand level, our approach is competitive with the state-of-the-art single methods achieving precision of 61.0% and recall of 55.4%, while not requiring residue solvent accessibility as an input.

Item Type:	Journal Article
Divisions:	Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Official Date:	2 March 2013
Dates:	Date Event 2 March 2013 UNSPECIFIED
Volume:	Volume 29
Number:	Number 5
Page Range:	pp. 580-587
DOI:	10.1093/bioinformatics/btt005
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

Request changes or add full text files to a record

Repository staff actions (login required)

View Item