Nucleic Acids Research Advance Access published online on December 4, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn944
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Computational Biology |
FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator
1Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, 2Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 and 3Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA
*To whom correspondence should be addressed. Tel: +1 301 619 1990; Fax: +1 301 619 1983; Email: raj{at}bioanalysis.org
Received August 1, 2008. Revised November 5, 2008. Accepted November 7, 2008.
Protein domain prediction is often the preliminary step in both experimental and computational protein research. Here we present a new method to predict the domain boundaries of a multidomain protein from its amino acid sequence using a fuzzy mean operator. Using the nr-sequence database together with a reference protein set (RPS) containing known domain boundaries, the operator is used to assign a likelihood value for each residue of the query sequence as belonging to a domain boundary. This procedure robustly identifies contiguous boundary regions. For a dataset with a maximum sequence identity of 30%, the average domain prediction accuracy of our method is 97% for one domain proteins and 58% for multidomain proteins. The presented model is capable of using new sequence/structure information without re-parameterization after each RPS update. When tested on a current database using a four year old RPS and on a database that contains different domain definitions than those used to train the models, our method consistently yielded the same accuracy while two other published methods did not. A comparison with other domain prediction methods used in the CASP7 competition indicates that our method performs better than existing sequence-based methods.