Skip Navigation



Bioinformatics Advance Access published online on January 12, 2006
Bioinformatics, doi:10.1093/bioinformatics/btk038
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrowOA All Versions of this Article:
22/6/723    most recent
btk038v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Polacco, B. J.
Right arrow Articles by Babbitt, P. C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Polacco, B. J.
Right arrow Articles by Babbitt, P. C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received October 5, 2005
Revised December 17, 2005
Accepted January 3, 2006

Article

Automated discovery of 3D motifs for protein function annotation

Benjamin J. Polacco 1 and Patricia C. Babbitt 1 *

1 Department of Biopharmaceutical Sciences, University of California, San Francisco, CA 94143-2250


  Abstract

Motivation: Function inference from structure is facilitated by the use of patterns of residues (3D motifs), normally identified by expert knowledge, that correlate with function. As an alternative to often limited expert knowledge, we use machine-learning techniques to identify patterns of 3 to 10 residues that maximize function prediction. This approach allows us to test the assumption that residues that provide function are the most informative for predicting function.

Results: We apply our method, GASPS, to the haloacid dehalogenase, enolase, amidohydrolase and crotonase superfamilies and to the serine proteases. The motifs found by GASPS are as good at function prediction as 3D motifs based on expert knowledge. The GASPS motifs with the greatest ability to predict protein function consist mainly of known functional residues. However, several residues with no known functional role are equally predictive. For four groups, we show that the predictive power of our 3D motifs is comparable to or better than that of approaches that use the entire fold (CE) or sequence profiles (PSI-BLAST).

Availability: Source code is freely available for academic use by contacting the authors.

Associate Editor: Anna Tramontano
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Brief BioinformHome page
J. Skolnick and M. Brylinski
FINDSITE: a combined evolution/structure-based approach to protein function prediction
Brief Bioinform, July 1, 2009; 10(4): 378 - 391.
[Abstract] [Full Text] [PDF]

Home page
 Brief Funct Genomic ProteomicHome page
P. F. Gherardini and M. Helmer-Citterich
Structure-based function prediction: approaches and applications
Brief Funct Genomic Proteomic, July 3, 2008; (2008) eln030v1.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
B. H. Dessailly, M. F. Lensink, C. A. Orengo, and S. J. Wodak
LigASite a database of biologically relevant binding sites in proteins with known apo-structures
Nucleic Acids Res., January 11, 2008; 36(suppl_1): D667 - D673.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.