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Protein Science (2004), 13:221-229. Published by Cold Spring Harbor Laboratory Press. Copyright © 2004 The Protein Society
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PFIT and PFRIT: Bioinformatic algorithms for detecting glycosidase function from structure and sequence

Gary Kleiger1, Ekaterina M. Panina1, Parag Mallick1,2 and David Eisenberg1

1 Howard Hughes Medical Institute, University of California, Los Angeles-Department Of Energy (UCLA-DOE) Institute of Genomics and Proteomics, UCLA, Los Angeles, California 90095, USA

Reprint requests to: David Eisenberg, Howard Hughes Medical Institute, UCLA-DOE Institute of Genomics and Proteomics, UCLA, Box 951570, Los Angeles, CA 90095, USA; e-mail: david{at}mbi.ucla.edu; fax: (310) 206-3914.

The identification of the enzymes involved in the metabolism of simple and complex carbohydrates presents one bioinformatic challenge in the post-genomic era. Here, we present the PFIT and PFRIT algorithms for identifying those proteins adopting the {alpha}/ß barrel fold that function as glycosidases. These algorithms are based on the observation that proteins adopting the {alpha}/ß barrel fold share positions in their tertiary structures having equivalent sets of atomic interactions. These are conserved tertiary interaction positions, which have been implicated in both structure and function. Glycosidases adopting the {alpha}/ß barrel fold share more conserved tertiary interactions than {alpha}/ß barrel proteins having other functions. The enrichment pattern of conserved tertiary interactions in the glycosidases is the information that PFIT and PFRIT use to predict whether any given {alpha}/ß barrel will function as a glycosidase or not. Using as a test set a database of 19 glycosidase and 45 nonglycosidase {alpha}/ß barrel proteins with low sequence similarity, PFIT and PFRIT can correctly predict glycosidase function for 84% of the proteins known to function as glycosidases. PFIT and PFRIT incorrectly predict glycosidase function for 25% of the nonglycosidases. The program PSI-BLAST can also correctly identify 84% of the 19 glycosidases, however, it incorrectly predicts glycosidase function for 50% of the nonglycosidases (twofold greater than PFIT and PFRIT). Overall, we demonstrate that the structure-based PFIT and PFRIT algorithms are both more selective and sensitive for predicting glycosidase function than the sequence-based PSI-BLAST algorithm.

Keywords: glycosidase; tertiary interaction; bioinformatics; structure; {alpha}/ß barrel; fold

Abbreviations: PFIT, prediction of function through tertiary interactions • PFRIT, prediction of function through residues at tertiary interactions • 3D, three-dimensional • EC, enzyme commission • ROC, receiver-operator characteristic • BPI, bactericidal/permeability-increasing protein


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