Prediction of Golgi Type II membrane proteins based on their transmembrane domains

Yuan, Zheng and Teasdale, Rohan D. (2002) Prediction of Golgi Type II membrane proteins based on their transmembrane domains. Bioinformatics, 18 8: 1109-1115. doi:10.1093/bioinformatics/18.8.1109

Author Yuan, Zheng
Teasdale, Rohan D.
Title Prediction of Golgi Type II membrane proteins based on their transmembrane domains
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
Publication date 2002-01-01
Sub-type Article (original research)
DOI 10.1093/bioinformatics/18.8.1109
Volume 18
Issue 8
Start page 1109
End page 1115
Total pages 7
Place of publication Oxford
Publisher Oxford University Press
Language eng
Subject C1
239901 Biological Mathematics
780101 Mathematical sciences
Abstract Motivation: A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches. Results: A new method has been developed to predict Golgi membrane proteins based on their transmembrane domains. To establish the prediction procedure, we took the hydrophobicity values and frequencies of different residues within the transmembrane domains into consideration. A simple linear discriminant function was developed with a small number of parameters derived from a dataset of Type II transmembrane proteins of known localization. This can discriminate between proteins destined for Golgi apparatus or other locations (post-Golgi) with a success rate of 89.3% or 85.2%, respectively on our redundancy-reduced data sets.
Keyword Mathematics, Interdisciplinary Applications
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Statistics & Probability
Subcellular Locations
Signal Peptides
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 40 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 15 Aug 2007, 04:58:32 EST