This report describes an optimised version of a secondary structure prediction method based on mocal homologies, using a new dam 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 Le~. 205 (1986) 303-308). The flex|bility of the method to the incorperation of information exWaneous to the prediction was demonstrated by the prediction of the homologous proteins in the dau~ base. Using the percentage identity with the protein to be predicmd, to weight the relative imlmrtance 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 rroteins 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 comlmsition increased the overa|! prediction to 66%.