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Bioinformatics Advance Access originally published online on February 12, 2004
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Bioinformatics 20(9) © Oxford University Press 2004; all rights reserved.

Automatic prediction of protein domains from sequence information using a hybrid learning system

Niranjan Nagarajan and Golan Yona *

Department of Computer Science, Cornell University, Upson Hall, Ithaca, NY 14853, USA

Received on August 6, 2003; revised on December 7, 2003; accepted on December 12, 2003
Advance Access Publication February 12, 2004

Motivation: We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains.

Results: The method was assessed using the domain definitions in SCOP and CATH for proteins of known structure and was compared with several other existing methods. Our method performs well both in terms of accuracy and sensitivity. It improves significantly over the best methods available, even some of the semi-manual ones, while being fully automatic. Our method can also be used to suggest and verify domain partitions based on structural data. A few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed.

Availability: An online domain-prediction server is available at http://biozon.org/tools/domains/

Contact: niranjan{at}cs.cornell.edu; golan{at}cs.cornell.edu

* To whom correspondence should be addressed.


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