We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. High accuracy is achieved by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique is rooted in machine learning that shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure, using the joint hypothesis we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily- , and fold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to those of the individual component classifiers. In particular, we achieve error rates that are 3 to 12 times less than the individual classifiers' error rates at the family level, 1.5 to 4.5 times less at the superfamily level, and 1.1 to 2.4 times less at the fold level.

Abstract not found

The CATH database provides hierarchical classification of protein domains based on their folding patterns. Domains are obtained from protein structures deposited in the Protein Data Bank and both domain identification and subsequent classification use manual as well as automated procedures. The accompanying website (www.cathdb.info) provides an easy-to-use entry to the classification, allowing for both browsing and downloading of data. Here, we give a brief review of the database, its corresponding website and some related tools.

Summary: The F2CS server provides access to the software, F2CS2.00, that implements an automated prediction method of SCOP and CATH classifications of proteins, based on their FSSP Z-scores (Getz et al., 2002),