Automatic pattern embedding in protein structure models

This article describes an implementation of a set of rules for automatically embedding a minimal functional sequence pattern in a structural hidden Markov model. The final product is a library of wide ranging fold models with encoded information about the functional families.

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Bibliographic Details
Published inIEEE intelligent systems Vol. 16; no. 6; pp. 21 - 25
Main Authors Bienkowska, J., Hongxian He, Smith, T.F.
Format Journal Article
LanguageEnglish
Published Los Alamitos IEEE 01.11.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Amino acids
Artificial intelligence
Bayesian methods
Helium
Hidden Markov models
Length measurement
Libraries
Markov analysis
Prediction methods
Predictive models
Proteins
State-space methods
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Summary:This article describes an implementation of a set of rules for automatically embedding a minimal functional sequence pattern in a structural hidden Markov model. The final product is a library of wide ranging fold models with encoded information about the functional families.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1541-1672
1941-1294
DOI:10.1109/5254.972074