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Bioinformatics. 2008 Sep 15;24(18):2094-5. doi: 10.1093/bioinformatics/btn371. Epub 2008 Jul 16.

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

Author information

1
Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.

Abstract

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.

SUPPLEMENTARY INFORMATION:

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

PMID:
18635571
PMCID:
PMC2732205
DOI:
10.1093/bioinformatics/btn371
[Indexed for MEDLINE]
Free PMC Article

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