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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Veröffentlicht in:	Amino acids Jg. 34; H. 1; S. 111 - 117
Hauptverfasser:	Diao, Y, Ma, D, Wen, Z, Yin, J, Xiang, J, Li, M
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Vienna : Springer-Verlag 2008 Springer-Verlag Springer Nature B.V
Schlagworte:	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-Zugang:	Volltext
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