Improvement of protein secondary structure prediction by combination of statistical algorithms and circular dichroism.

Three different approaches (propensity curve shifting, hydropathy index evaluation, and iterative attribution/cancellation of secondary structure) to the use of secondary structure percentages derived from circular dichroism measurements to improve the success rate of a protein secondary structure prediction method, without using decision constants, are described and compared. Propensity-curve shifting appears to be the best-performing approach, bearing an increase of 5.3% in the success rate of single-residue structural prediction when exact information on the secondary structure, obtained by X-ray crystallography, is employed; with information of an accuracy comparable to that obtainable by circular dichroism, the improvement stays between 3.5 and 4.9%, for a three-state prediction. Although developed with circular dichroism in mind, the method can use percentages of secondary structure obtained by any other experimental methodology from which they can be inferred, for instance Raman spectroscopy and infrared spectroscopy.