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

Marco Lippi, Andrea Passerini, Marco Punta, Burkhard Rost, Paolo Frasconi

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

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.

Original languageEnglish
Pages (from-to)2094-2095
Number of pages2
JournalBioinformatics
Volume24
Issue number18
DOIs
StatePublished - Sep 2008
Externally publishedYes

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