TY - JOUR
T1 - Hidden Markov models for prediction of protein features.
AU - Bystroff, Christopher
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.
U2 - 10.1007/978-1-59745-574-9_7
DO - 10.1007/978-1-59745-574-9_7
M3 - Journal article
C2 - 18075166
SN - 1064-3745
VL - 413
SP - 173
EP - 198
JO - Methods in Molecular Biology
JF - Methods in Molecular Biology
ER -