Secondary structure prediction using segment similarity

We present a secondary structure prediction method based on finding similarities between sequence segments from the target sequence and segments contained in the database of proteins with known structures, The similarity definition is optimized using a genetic algorithm and is based on a 21 x 40 similarity matrix, comparing a target sequence with the sequence and burial status of the proteins from the database. The three-state secondary structure prediction accuracy reaches 72.4% on a non homologous (maximum sequence identity <25%) data set derived from PDB and is reproduced on two independent testing sets, including the set of CASP2 prediction targets and a group of newly solved PDB structures. The prediction method was developed with simplicity and open architecture in mind, allowing for an easy extension to other types of predictions and to the analysis of the contributions to the local structure formation. For instance, the design of the prediction procedure allows us to trace back segments of the database that contributed to the prediction. It can be shown that those segments came from various structural classes and that even complete exclusion of related folds from the database does not result in a significant decrease in prediction accuracy.