Bioinformatics Advance Access published online on March 17, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl102
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1 Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine
* To whom correspondence should be addressed.
Motivation: Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition-finding a proper template for a given query protein. Results: Here we present a two-stage machine learning, information retrieval, approach to fold recognition. First, we use alignment methods to derive pairwise similarity features for query-template protein pairs. We also use global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map, and beta-strand pairing to extract pairwise structural compatibility features. Second, we apply support vector machines to these features to predict the structural relevance (i.e. in the same fold or not) of the query-template pairs. For each query, the continuous relevance scores are used to rank the templates. The FOLDpro approach is modular, scalable, and effective. Compared to 11 other fold recognition methods, FOLDpro yields the best results in almost all standard categories on a comprehensive benchmark dataset. Using predictions of the top-ranked template, the sensitivity is about 85%, 56%, and 27% at the family, superfamily, and fold levels respectively. Using the 5 top-ranked templates, the sensitivity increases to 90%, 70%, and 48%. Availability: The FOLDpro server is available with the SCRATCH suite through http://www.igb.uci.edu/servers/psss.html.
Received January 17, 2006
Revised March 4, 2006
Accepted March 15, 2006
Article
A machine learning information retrieval approach to protein fold recognition
Jianlin Cheng 1
and
Pierre Baldi 1 *
Pierre Baldi, E-mail: pfbaldi{at}ics.uci.edu
Abstract
Associate Editor: Anna Tramontano
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