Lippi Marco, Passerini Andrea, Punta Marco, Rost Burkhard, Frasconi Paolo
Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.
Bioinformatics. 2008 Sep 15;24(18):2094-5. doi: 10.1093/bioinformatics/btn371. Epub 2008 Jul 16.
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.
Freely available at http://metaldetector.dsi.unifi.it.
Details and data can be found at http://metaldetector.dsi.unifi.it/help.php.
网络服务器MetalDetector将蛋白质中的组氨酸残基分为两种状态之一（游离或金属结合），将半胱氨酸分为三种状态之一（游离、金属结合或二硫键桥连）。决策树整合了两种先前开发的方法（DISULFIND和金属配体预测器）的预测结果。交叉验证性能评估表明，我们的服务器预测二硫键结合状态的精度为88.6%，召回率为85.1%，而它识别过渡金属结合位点中的半胱氨酸和组氨酸的精度分别为79.9%和76.8%，以及60.8%和40.7%。
可在http://metaldetector.dsi.unifi.it免费获取。
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