Predicting Metal-Binding Sites from Protein Sequence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Metal binding in proteins: machine learning complements x-ray absorption spectroscopy
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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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@dsi.unifi.it Supplementary Information: Details and data can be found at http://metaldetector.dsi.unifi.it/help.php