  Simple hidden Markov models are proposed 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 length distribution, and focus on inclusion of short range correlations of
residues and of conformation states in the models. Conformation-independent and
-dependent amino acid coarse-graining schemes are designed for the models by
means of proper mutual information. We compare models of different level of
complexity, and establish a practical model with a high prediction accuracy.
