Literature DB >> 10336379

Prediction of membrane protein types and subcellular locations.

K C Chou1, D W Elrod.   

Abstract

Membrane proteins are classified according to two different schemes. In scheme 1, they are discriminated among the following five types: (1) type I single-pass transmembrane, (2) type II single-pass transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, and (5) GPI-anchored membrane proteins. In scheme 2, they are discriminated among the following nine locations: (1) chloroplast, (2) endoplasmic reticulum, (3) Golgi apparatus, (4) lysosome, (5) mitochondria, (6) nucleus, (7) peroxisome, (8) plasma, and (9) vacuole. An algorithm is formulated for predicting the type or location of a given membrane protein based on its amino acid composition. The overall rates of correct prediction thus obtained by both self-consistency and jackknife tests, as well as by an independent dataset test, were around 76-81% for the classification of five types, and 66-70% for the classification of nine cellular locations. Furthermore, classification and prediction were also conducted between inner and outer membrane proteins; the corresponding rates thus obtained were 88-91%. These results imply that the types of membrane proteins, as well as their cellular locations and other attributes, are closely correlated with their amino acid composition. It is anticipated that the classification schemes and prediction algorithm can expedite the functionality determination of new proteins. The concept and method can be also useful in the prioritization of genes and proteins identified by genomics efforts as potential molecular targets for drug design.

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Year:  1999        PMID: 10336379

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  41 in total

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5.  Using fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types.

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Authors:  Haruhiko Miyata; Julio M Castaneda; Yoshitaka Fujihara; Zhifeng Yu; Denise R Archambeault; Ayako Isotani; Daiji Kiyozumi; Maya L Kriseman; Daisuke Mashiko; Takafumi Matsumura; Ryan M Matzuk; Masashi Mori; Taichi Noda; Asami Oji; Masaru Okabe; Renata Prunskaite-Hyyrylainen; Ramiro Ramirez-Solis; Yuhkoh Satouh; Qian Zhang; Masahito Ikawa; Martin M Matzuk
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-29       Impact factor: 11.205

9.  Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins.

Authors:  Bing Niu; Yu-Huan Jin; Kai-Yan Feng; Wen-Cong Lu; Yu-Dong Cai; Guo-Zheng Li
Journal:  Mol Divers       Date:  2008-05-28       Impact factor: 2.943

10.  Development of a membrane-anchored chemerin receptor agonist as a novel modulator of allergic airway inflammation and neuropathic pain.

Authors:  Jamie R Doyle; Subrahmanian T Krishnaji; Guangli Zhu; Zhen-Zhong Xu; Daniel Heller; Ru-Rong Ji; Bruce D Levy; Krishna Kumar; Alan S Kopin
Journal:  J Biol Chem       Date:  2014-03-21       Impact factor: 5.157

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