Secondary structure prediction for modelling by homology

An improved method of secondary structure prediction has beendeveloped to aid the modelling of proteins by homology. Selecteddata from four published algorithms are scaled and combinedas a weighted mean to produce consensus algorithms. Each consensusalgorithm is used to predict the secondary structure of a proteinhomologous to the target protein and of known structure. Bycomparison of the predictions to the known structure, accuracyvalues are calculated and a consensus algorithm chosen as theoptimum combination of the composite data for prediction ofthe homologous protein. This customized algorithm is then usedto predict the secondary structure of the unknown protein. Inthis manner the secondary structure prediction is initiallytuned to the required protein family before prediction of thetarget protein. The method improves statistical secondary structureprediction and can be incorporated into more comprehensive systemssuch as those involving consensus prediction from multiple sequencealignments. Thirty one proteins from five families were usedto compare the new method to that of Garnier, Osguthorpe andRobson (GOR) and sequence alignment. The improvement over GORis naturally dependent on the similarity of the homologous protein,varying from a mean of 3% to 7% with increasing alignment significancescore.