Secondary structure prediction for modelling by homology.

Abstract

An improved method of secondary structure prediction has been developed to aid the modelling of proteins by homology. Selected data from four published algorithms are scaled and combined as a weighted mean to produce consensus algorithms. Each consensus algorithm is used to predict the secondary structure of a protein homologous to the target protein and of known structure. By comparison of the predictions to the known structure, accuracy values are calculated and a consensus algorithm chosen as the optimum combination of the composite data for prediction of the homologous protein. This customized algorithm is then used to predict the secondary structure of the unknown protein. In this manner the secondary structure prediction is initially tuned to the required protein family before prediction of the target protein. The method improves statistical secondary structure prediction and can be incorporated into more comprehensive systems such as those involving consensus prediction from multiple sequence alignments. Thirty one proteins from five families were used to compare the new method to that of Garnier, Osguthorpe and Robson (GOR) and sequence alignment. The improvement over GOR is naturally dependent on the similarity of the homologous protein, varying from a mean of 3% to 7% with increasing alignment significance score.