Prediction of protein secondary structure by the hidden Markov model

Kiyoshi Asai*, Satoru Hayamizu, Ken'ichi Handa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)

Abstract

The purpose of this paper is to introduce a new method for analyzing the amino acid sequences of proteins using the hidden Markov model (HMM), which is a type of stochastic model. Secondary structures such as helix, sheet and turn are learned by HMMs, and these HMMs are applied to new sequences whose structures are unknown. The output probabilities from the HMMs are used to predict the secondary structures of the sequences. The authors tested this prediction system on ∼100 sequences from a public database (Brookhaven PDB). Although the implementation is 'without grammar' (no rule for the appearance patterns of secondary structure) the result was reasonable.

Original languageEnglish
Pages (from-to)141-146
Number of pages6
JournalBioinformatics
Volume9
Issue number2
DOIs
Publication statusPublished - 1993 Apr
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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