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Prediction of protein structure classes and secondary structures by means of hidden Markov models

✍ Scribed by Hiroshi Yoshikawa; Mitsunori Ikeguchi; Shugo Nakamura; Kentaro Shimizu; Junta Doi

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 267 KB
Volume: 30
Category: Article
ISSN: 0882-1666
DOI: 10.1002/(sici)1520-684x(19991130)30:13<13::aid-scj2>3.0.co;2-7

No coin nor oath required. For personal study only.

✦ Synopsis

This study deals with structure class/secondary structure prediction of proteins using hidden Markov models (HMMs). With the proposed method, prediction is performed using HMMs designed so as to represent hierarchicality and periodicity of protein structural features. Secondary structures (partial tertiary structures) are formed from amino acid sequences while tertiary structures are formed through packing of secondary structures; thus, hierarchicality of protein structure is represented by means of hierarchical combination of multiple HMMs. In so doing, the tertiary HMM is built from a sequence of secondary structure segments while the secondary HMM is built from amino acid sequences. In addition, periodicity is introduced into the HMM network topology so that periodical structural features can be represented. Transition probabilities and output probabilities are determined through learning of data related to known structures. HMMs designed as mentioned were applied to the prediction of unknown structures. Accuracy above 50% was achieved for structure class prediction. Besides, high accuracy prediction of secondary structure was obtained for D class and D/E class. Thus, the proposed method proved to offer faithful representation of protein structural features.

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