Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity

Transmembrane (TM) proteins represent about 20-30% of the protein sequences in higher eukaryotes, playing important roles across a range of cellular functions. Moreover, knowledge about topology of these proteins often provides crucial hints toward their function. Due to the difficulties in experime...

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Bibliographic Details
Published in:Amino acids Vol. 34; no. 1; pp. 111 - 117
Main Authors: Diao, Y, Ma, D, Wen, Z, Yin, J, Xiang, J, Li, M
Format: Journal Article
Language:English
Published: Vienna Vienna : Springer-Verlag 2008
Springer-Verlag
Springer Nature B.V
Subjects:
Amino acid composition
Amino acid sequence
amino acid sequences
Amino acids
Amino Acids - chemistry
Amino Acids - metabolism
Analytical Chemistry
Augmented covariant-discriminant algorithm
Automata theory
Biochemical Engineering
Biochemistry
Biomedical and Life Sciences
Cellular automata
Cellular structure
Chou's invariance theorem
Complexity
Composition
Computational Biology
data collection
drugs
Eukaryotes
eukaryotic cells
Genetics
Lempel-Ziv complexity
Life Sciences
Mathematical models
Membrane proteins
Membrane Proteins - chemistry
Membrane Proteins - metabolism
Membranes
Models, Molecular
Neurobiology
prediction
Protein structure
Protein Structure, Secondary
Protein Structure, Tertiary
Proteins
Proteomics
Pseudo amino acid composition
Sequence Analysis, Protein
Source code
Structure-function relationships
Topology
Transmembrane regions
Keywords: Cellular automata – Pseudo amino acid composition – Lempel-Ziv complexity – Augmented covariant-discriminant algorithm – Chou’s invariance theorem – Transmembrane regions
ISSN:0939-4451, 1438-2199, 1438-2199
Online Access:Get full text
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Summary:Transmembrane (TM) proteins represent about 20-30% of the protein sequences in higher eukaryotes, playing important roles across a range of cellular functions. Moreover, knowledge about topology of these proteins often provides crucial hints toward their function. Due to the difficulties in experimental structure determinations of TM protein, theoretical prediction methods are highly preferred in identifying the topology of newly found ones according to their primary sequences, useful in both basic research and drug discovery. In this paper, based on the concept of pseudo amino acid composition (PseAA) that can incorporate sequence-order information of a protein sequence so as to remarkably enhance the power of discrete models (Chou, K. C., Proteins: Structure, Function, and Genetics, 2001, 43: 246-255), cellular automata and Lempel-Ziv complexity are introduced to predict the TM regions of integral membrane proteins including both α-helical and β-barrel membrane proteins, validated by jackknife test. The result thus obtained is quite promising, which indicates that the current approach might be a quite potential high throughput tool in the post-genomic era. The source code and dataset are available for academic users at liml@scu.edu.cn.
Bibliography:http://dx.doi.org/10.1007/s00726-007-0550-z
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ISSN:0939-4451
1438-2199
1438-2199
DOI:10.1007/s00726-007-0550-z