Convex constraint analysis: a natural deconvolution of circular dichroism curves of proteins

Abstract

A new algorithm, called convex constraint analysis, has been developed to deduce the chiral contribution of the common secondary structures directly from experimental CD curves of a large number of proteins. The analysis is based on CD data reported by Yang, J.T., Wu, C.-S.C. and Martinez, H.M. [Methods Enzymol., 130, 208-269 (1986)]. Application of the decomposition algorithm for simulated protein data sets resulted in component spectra [B (lambda, i)] identical to the originals and weights [C (i, k)] with excellent Pearson correlation coefficients (R) [Chang, C.T., Wu, C.-S.C. and Yang, J.T. (1978) Anal. Biochem., 91, 12-31]. Test runs were performed on sets of simulated protein spectra created by the Monte Carlo technique using poly-L-lysine-based pure component spectra. The significant correlational coefficients (R greater than 0.9) demonstrated the high power of the algorithm. The algorithm, applied to globular protein data, independent of X-ray data, revealed that the CD spectrum of a given protein is composed of at least four independent sources of chirality. Three of the computed component curves show remarkable resemblance to the CD spectra of known protein secondary structures. This approach yields a significant improvement in secondary structural evaluations when compared with previous methods, as compared with X-ray data, and yields a realistic set of pure component spectra. The new method is a useful tool not only in analyzing CD spectra of globular proteins but also has the potential for the analysis of integral membrane proteins.