Hidden Markov models for prediction of protein features.

Christopher Bystroff; Anders Krogh

doi:10.1007/978-1-59745-574-9_7

Hidden Markov models for prediction of protein features.

Christopher Bystroff, Anders Krogh

Computational and RNA Biology

19 Citations (Scopus)

Abstract

Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.

Original language	English
Journal	Methods in Molecular Biology
Volume	413
Pages (from-to)	173-98
Number of pages	25
ISSN	1064-3745
DOIs	https://doi.org/10.1007/978-1-59745-574-9_7
Publication status	Published - 2008

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title = "Hidden Markov models for prediction of protein features.",

abstract = "Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.",

author = "Christopher Bystroff and Anders Krogh",

note = "Key Words: Transmembrane - local - motif - Viterbi - Baum–Welch - profile - topology - folding",

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AU - Krogh, Anders

N1 - Key Words: Transmembrane - local - motif - Viterbi - Baum–Welch - profile - topology - folding

PY - 2008

Y1 - 2008

N2 - Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.

AB - Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.

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VL - 413

SP - 173

EP - 198

JO - Methods in Molecular Biology

JF - Methods in Molecular Biology

ER -

Hidden Markov models for prediction of protein features.

Abstract

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