Details
| Original language | English |
|---|---|
| Pages (from-to) | 851-861 |
| Number of pages | 11 |
| Journal | CHEMBIOCHEM |
| Volume | 14 |
| Issue number | 7 |
| Publication status | Published - 10 May 2013 |
Abstract
Profile hidden Markov models (HMMs) were used to predict the configuration of secondary alcohols and α-methyl branches of modular polyketides. Based on the configurations of two chiral centers in these polyketides, 78 ketoreductases were classified into four different types of polyketide producers. The identification of positions that discriminate between these protein families was followed by fitting six profile HMMs to the data set and the corresponding subsets, to model the conserved regions of the protein types. Ultimately, the profile HMMs described herein predict protein subtypes based on the complete information-rich region; consequently, slight changes in a multiple sequence alignment do not significantly alter the outcome of this classification method. Additionally, Viterbi scores can be used to assess the reliability of the classification.
Keywords
- Configurational assignment, Ketoreductases, Natural products, Polyketides, Profile hidden Markov model
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Molecular Medicine
- Biochemistry, Genetics and Molecular Biology(all)
- Molecular Biology
- Chemistry(all)
- Organic Chemistry
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In: CHEMBIOCHEM, Vol. 14, No. 7, 10.05.2013, p. 851-861.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Configurational Assignment of Secondary Hydroxyl Groups and Methyl Branches in Polyketide Natural Products through Bioinformatic Analysis of the Ketoreductase Domain
AU - Kitsche, Andreas
AU - Kalesse, Markus
PY - 2013/5/10
Y1 - 2013/5/10
N2 - Profile hidden Markov models (HMMs) were used to predict the configuration of secondary alcohols and α-methyl branches of modular polyketides. Based on the configurations of two chiral centers in these polyketides, 78 ketoreductases were classified into four different types of polyketide producers. The identification of positions that discriminate between these protein families was followed by fitting six profile HMMs to the data set and the corresponding subsets, to model the conserved regions of the protein types. Ultimately, the profile HMMs described herein predict protein subtypes based on the complete information-rich region; consequently, slight changes in a multiple sequence alignment do not significantly alter the outcome of this classification method. Additionally, Viterbi scores can be used to assess the reliability of the classification.
AB - Profile hidden Markov models (HMMs) were used to predict the configuration of secondary alcohols and α-methyl branches of modular polyketides. Based on the configurations of two chiral centers in these polyketides, 78 ketoreductases were classified into four different types of polyketide producers. The identification of positions that discriminate between these protein families was followed by fitting six profile HMMs to the data set and the corresponding subsets, to model the conserved regions of the protein types. Ultimately, the profile HMMs described herein predict protein subtypes based on the complete information-rich region; consequently, slight changes in a multiple sequence alignment do not significantly alter the outcome of this classification method. Additionally, Viterbi scores can be used to assess the reliability of the classification.
KW - Configurational assignment
KW - Ketoreductases
KW - Natural products
KW - Polyketides
KW - Profile hidden Markov model
UR - http://www.scopus.com/inward/record.url?scp=84877048798&partnerID=8YFLogxK
U2 - 10.1002/cbic.201300063
DO - 10.1002/cbic.201300063
M3 - Article
C2 - 23576424
AN - SCOPUS:84877048798
VL - 14
SP - 851
EP - 861
JO - CHEMBIOCHEM
JF - CHEMBIOCHEM
SN - 1439-4227
IS - 7
ER -