Skip Navigation


Bioinformatics Advance Access originally published online on July 16, 2008
Bioinformatics 2008 24(18):2094-2095; doi:10.1093/bioinformatics/btn371
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
24/18/2094    most recent
btn371v1
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
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Lippi, M.
Right arrow Articles by Frasconi, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lippi, M.
Right arrow Articles by Frasconi, P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence

Marco Lippi 1, Andrea Passerini 1, Marco Punta 2,3,4, Burkhard Rost 2,3,4 and Paolo Frasconi 1,*

1Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy, 2Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, 3Columbia University Center for Computational Biology and Bioinformatics (C2B2), 1130 St Nicholas Ave. and 4Northeast Structural Genomics Consortium (NESG), Columbia University, 1130 St Nicholas Ave. Rm. 802, New York, NY 10032, USA

*To whom correspondence should be addressed.


  Abstract

Summary: The web server MetalDetector classifies histidine residues in proteins into one of two states (free or metal bound) and cysteines into one of three states (free, metal bound or disulfide bridged). A decision tree integrates predictions from two previously developed methods (DISULFIND and Metal Ligand Predictor). Cross-validated performance assessment indicates that our server predicts disulfide bonding state at 88.6% precision and 85.1% recall, while it identifies cysteines and histidines in transition metal-binding sites at 79.9% precision and 76.8% recall, and at 60.8% precision and 40.7% recall, respectively.

Availability: Freely available at http://metaldetector.dsi.unifi.it

Contact: metaldetector{at}dsi.unifi.it

Supplementary Information: Details and data can be found at http://metaldetector.dsi.unifi.it/help.php

Associate Editor: John Quackenbush

Received on April 17, 2008; revised on June 27, 2008; accepted on July 14, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




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.