BIOINFORMATICS A New Representation for Protein Secondary Structure Prediction Based on Frequent Patterns

Motivation: A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using Support Vector Machines (SVMs). Results: Three different sets of features based on frequent patterns are evaluated in a blind testing setup using 150 targets from the EVA contest and compared to predictions of PSI-PRED, PHD and PROF-sec. Even though being trained on only 940 proteins, a simple SVM classifier based on this new representation yields results compara-ble to PSI-PRED and PROFsec. Finally, we show that the method contributes significant information to consensus predictions. Availability: The method is available from the authors upon request. Contact