Improvements in a secondary structure prediction method based on a search for local sequence homologies and its use as a model building tool.

This report describes an optimised version of a secondary structure prediction method based on local homologies, using a new data base. A 63% prediction accuracy, for three states, was obtained after elimination of the protein to be predicted and all proteins with a percentage identity greater than 22% from the data base. This corresponds to a 5% increase in accuracy on the original method (Levin et al. FEBS Lett. 205 (1986) 303-308). The flexibility of the method to the incorporation of information extraneous to the prediction was demonstrated by the prediction of the homologous proteins in the data base. Using the percentage identity with the protein to be predicted, to weight the relative importance of each protein, for all proteins with a percentage identity greater than 30%, the mean correct prediction per chain was 87%. As a result this algorithm can be used during the molecular modelling process, both to give an idea of the structural similarity between two proteins and as an aid in the determination of the best alignment. Incorporation of the result of a protein folding type assignment based on the global amino-acid composition increased the overall prediction to 66%.