Toward predicting protein topology: an approach to identifying beta hairpins.

Although secondary structure prediction methods have recently improved, progress from secondary to tertiary structure prediction has been limited. A promising but largely unexplored route to this goal is to predict structure motifs from secondary structure knowledge. Here we present a novel method for the recognition of beta hairpins that combines secondary structure predictions and threading methods by using a database search and a neural network approach. The method successfully predicts 48 and 77%, respectively, of all of hairpin and nonhairpin beta-coil-beta motifs in a protein database. We find that the main contributors to motif recognition are predicted accessibility and turn propensities.