Protein seer: a Web server for protein homology detection

We present and evaluate a publicly available Web server which classifies protein sequences into SCOP 1.63 PDB95 structural superfamilies. The Website returns ranked lists of likely superfamilies and hence implicit structural predictions according to three computational techniques: BLAST, HMMER and a discriminative classifier SVM-BLOCKS. It is the first Website to provide predictions using SVM-BLOCKS. In addition to the ranked lists, the Website displays alignment information and a Web services interface is also available for computationally intensive use. We conduct a large-scale evaluation which mimics the predictions returned by the Website. The study indicates that the site provides valid predictions and that SVM-BLOCKS approach can outperform BLAST and HMMER when sufficient examples are available to learn the SVM classifiers.