Prediction of protein secondary structure from amino acid sequence

The conformational parameters Pk for each amino acid species (j = 1-20) of sequential peptides in proteins are presented as the product of P(i,k), where i is the number of the sequential residues in the kth conformational state (k = alpha-helix, beta-sheet, beta-turn, or unordered structure). Since the average parameter for an n-residue segment is related to the average probability of finding the segment in the kth state, it becomes a geometric mean of (Pk)av = II (P(i,k))1/n with amino acid residue i increasing from 1 to n. We then used ln(Pk)av to convert a multiplicative process to a summation, i.e., ln(Pk)av = (1/n)sigma P(i,k) (i = 1 to n) for ease of operation. However, this is unlike the popular Chou-Fasman algorithm, which has the flaw of using the arithmetic mean for relative probabilities. The Chou-Fasman algorithm happens to be close to our calculations in many cases mainly because the difference between their Pk and our ln Pk is nearly constant for about one-half of the 20 amino acids. When stronger conformation formers and breakers exist, the difference become larger and the prediction at the N- and C-terminal alpha-helix or beta-sheet could differ. If the average conformational parameters of the overlapping segments of any two states are too close for a unique solution, our calculations could lead to a different prediction.