HMM(Hidden Markov Models)-A New Method for Predicting Protein Secondary Structure

Abstract

We introduce a new method-duration Hidden Markov Model (dHMM) to predicate the secondary structure of Protein. In our study, we divide the basic second structure of protein into three parts: H (α-Helix), E (β-sheet) and O (others, include coil and turn). HMM is a kind of probabilistic model which more thinking of the interaction between adjacent amino acids (these interaction were represented by transmit probability). After improving on the model and fix on the parameters of the model, we write a program HMMPS. Our example shows (for example, we give the predicated secondary structure of C-structure protein (include 100 amino acids) of Hog Cholera Virus Lapinized Chinese Strain (HCLV)) that HMM is a nice method for protein secondary structure prediction.

Cite this paper

@article{zhongxi2004hidden,
  title = {{HMM(Hidden Markov Models)-A New Method for Predicting Protein Secondary Structure}},
  author = {Zhong-xi, MO and Zhang, Chuyu},
  journal = {Journal of Biomathematics},
  year = {2004}
}

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