title: MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence.
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.
  
  
  BACKGROUND
  Freely available at http://metaldetector.dsi.unifi.it.
  
  
  BACKGROUND
  Details and data can be found at http://metaldetector.dsi.unifi.it/help.php.
authors.affiliation: Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Lippi
authors.forename: Andrea
authors.initials: A
authors.lastname: Passerini
authors.forename: Marco
authors.initials: M
authors.lastname: Punta
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
authors.forename: Paolo
authors.initials: P
authors.lastname: Frasconi
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003545:Cysteine / Q000737:chemistry*; D030562:Databases, Protein; D004220:Disulfides / Q000737:chemistry* / Q000378:metabolism; D006639:Histidine / Q000737:chemistry*; D020407:Internet; D008667:Metalloproteins / Q000737:chemistry* / Q000378:metabolism; D008969:Molecular Sequence Data; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
chemical_list: D004220:Disulfides; D008667:Metalloproteins; D006639:Histidine; D003545:Cysteine