Protein secondary structure prediction by combining hidden Markov models and sliding window scores

Abstract

Instead of conformation states of single residues, refined conformation states of quintuplets are proposed to reflect conformation correlation. Simple hidden Markov models combining with sliding window scores are used for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of the length distribution. The window scores for residues are a window version of the Chou-Fasman propensities estimated under an approximation of conditional independency. Different window widths are examined, and the optimal width is found to be 17. A high accuracy about 70% is achieved.