Prediction of protein secondary structure by the hidden Markov model

Kiyoshi Asai, Satoru Hayamizu, Ken'ichi Handa

研究成果: Article査読

111 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)141-146
ページ数6
ジャーナルBioinformatics
9
2
DOI
出版ステータスPublished - 1993 4
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 計算理論と計算数学
  • 計算数学

フィンガープリント

「Prediction of protein secondary structure by the hidden Markov model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル