HMM in predicting protein secondary structure

We introduced 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 m...

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Bibliographic Details
Published in:Wuhan University journal of natural sciences 2003, Vol.8 (1), p.307-310
Main Authors: Jing, Huang, Feng, Shi, Xiu-fen, Zou, Yuan-xiang, Li, Huai-bei, Zhou
Format: Article
Language:English
Subjects:
Amino acids
Genetic algorithms
Markov chains
Parameters
Probabilistic models
Protein structure
Proteins
Secondary structure
Statistical analysis
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Summary:We introduced 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), and we use genetic algorithm to determine the model parameters. After improving on the model and fixed on the parameters of the model, we write a program HMMPS. Our example shows that HMM is a nice method for protein secondary structure prediction.
ISSN:1007-1202
1993-4998
DOI:10.1007/BF02899499