HMM with protein structure grammar

Kiyoshi Asai, Satoru Hayamizu, Kentaro Onizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

The authors propose a structure-prediction framework for proteins that uses hidden Markov models (HMM) with a protein structure grammar. By adopting a protein structure grammar, the HMM makes it possible to treat global interactions, the interaction between two secondary structures which are apart in the sequence. In this framework, prediction of local and global structures are totally treated through global and local interactions which are expressed by the protein sequence grammar. The relations between some of the previous methods for secondary structure prediction and HMMs are discussed. Some experimental results on secondary structure prediction are included. The learning algorithms for the HMMs are presented.

Original languageEnglish
Title of host publicationProceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993
PublisherIEEE Computer Society
Pages783-791
Number of pages9
ISBN (Electronic)0818632305
DOIs
Publication statusPublished - 1993
Externally publishedYes
Event26th Hawaii International Conference on System Sciences, HICSS 1993 - Wailea, United States
Duration: 1993 Jan 8 → …

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume1
ISSN (Print)1530-1605

Conference

Conference26th Hawaii International Conference on System Sciences, HICSS 1993
CountryUnited States
CityWailea
Period93/1/8 → …

ASJC Scopus subject areas

  • Engineering(all)

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