A Segmental Semi Markov Model for protein secondary structure prediction

Hidden Markov Models (HMMs) are practical tools which provide probabilistic base for protein secondary structure prediction. In these models, usually, only the information of the left hand side of an amino acid is considered. Accordingly, these models seem to be inefficient with respect to long range correlations. In this work we discuss a Segmental Semi Markov Model (SSMM) in which the information of both sides of amino acids are considered. It is assumed and seemed reasonable that the information on both sides of an amino acid can provide a suitable tool for measuring dependencies. We consider these dependencies by dividing them into shorter dependencies. Each of these dependency models can be applied for estimating the probability of segments in structural classes. Several conditional probabilities concerning dependency of an amino acid to the residues appeared on its both sides are considered. Based on these conditional probabilities a weighted model is obtained to calculate the probability of each segment in a structure. This results in 2.27% increase in prediction accuracy in comparison with the ordinary Segmental Semi Markov Models, SSMMs. We also compare the performance of our model with that of the Segmental Semi Markov Model introduced by Schmidler et al. [C.S. Schmidler, J.S. Liu, D.L. Brutlag, Bayesian segmentation of protein secondary structure, J. Comp. Biol. 7(1/2) (2000) 233-248]. The calculations show that the overall prediction accuracy of our model is higher than the SSMM introduced by Schmidler.