Predicting Protein Function from Structure: Unique Structural Features of Proteases

We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smaller than average surface areas, smaller radii of gyration, and higher Calphadensities. These findings imply that proteases are, as a group, more tightly p...

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Published in:	Proceedings of the National Academy of Sciences - PNAS Vol. 97; no. 8; pp. 3954 - 3958
Main Authors:	Stawiski, Eric W., Baucom, Albion E., Lohr, Scott C., Gregoret, Lydia M.
Format:	Journal Article
Language:	English
Published:	United States National Academy of Sciences of the United States of America 11-04-2000 National Acad Sciences National Academy of Sciences
Subjects:	Autolysis Biochemistry Biological Sciences Datasets Endopeptidases - chemistry Endopeptidases - metabolism Genomes Genomics Gyration Molecular structure Physiological regulation Protein Structure, Secondary Proteins Structure-Activity Relationship Surface areas Training
Online Access:	Get full text
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Summary:	We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smaller than average surface areas, smaller radii of gyration, and higher Calphadensities. These findings imply that proteases are, as a group, more tightly packed than other proteins. There are also notable differences in secondary structure content between these two groups of proteins: proteases have fewer helices and more loops. We speculate that both high packing density and low α -helical content coevolved in proteases to avoid autolysis. By using the structural parameters that seem to show some separation between proteases and nonproteases, a neural network has been trained to predict protease function with over 86% accuracy. Moreover, it is possible to identify proteases whose folds were not represented during training. Similar structural analyses may be useful for identifying other classes of proteins and may be of great utility for categorizing the flood of structures soon to flow from structural genomics initiatives.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by Robert T. Sauer, Massachusetts Institute of Technology, Cambridge, MA, and approved February 10, 2000 To whom reprint requests should be addressed. E-mail: gregoret@chemistry.ucsc.edu.
ISSN:	0027-8424 1091-6490
DOI:	10.1073/pnas.070548997