Literature DB >> 15807506

Prediction of membrane protein types by incorporating amphipathic effects.

Kuo-Chen Chou1, Yu-Dong Cai.   

Abstract

According to their intramolecular arrangement and position in a cell, membrane proteins are generally classified into the following six types: (1) type I transmembrane, (2) type II transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, (5) GPI-anchored membrane, and (6) peripheral membrane. Situated in a heteropolar environment, these six types of membrane proteins must have quite different amphiphilic sequence-order patterns in order to stabilize their respective frameworks. To incorporate such a feature into the predictor, the amphiphilic pseudo amino acid composition has been formulated that contains a series of hydrophobic and hydrophilic correlation factors. The success rates thus obtained have been remarkably enhanced in identifying the types of membrane proteins, as demonstrated by the jackknife test and independent data set test, respectively.

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Year:  2005        PMID: 15807506     DOI: 10.1021/ci049686v

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  37 in total

1.  Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions.

Authors:  Hong-Liang Zou; Xuan Xiao
Journal:  J Membr Biol       Date:  2015-10-12       Impact factor: 1.843

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

Authors:  Hui Liu; Jie Yang; Meng Wang; Li Xue; Kuo-Chen Chou
Journal:  Protein J       Date:  2005-08       Impact factor: 2.371

3.  Prediction of interaction between small molecule and enzyme using AdaBoost.

Authors:  Bing Niu; Yuhuan Jin; Lin Lu; Kaiyan Fen; Lei Gu; Zhisong He; Wencong Lu; Yixue Li; Yudong Cai
Journal:  Mol Divers       Date:  2009-02-14       Impact factor: 2.943

4.  Using the nonlinear dimensionality reduction method for the prediction of subcellular localization of Gram-negative bacterial proteins.

Authors:  Tong Wang; Jie Yang
Journal:  Mol Divers       Date:  2009-03-28       Impact factor: 2.943

5.  iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.

Authors:  Xuan Xiao; Hong-Liang Zou; Wei-Zhong Lin
Journal:  J Membr Biol       Date:  2015-03-22       Impact factor: 1.843

Review 6.  A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  J Membr Biol       Date:  2016-11-19       Impact factor: 1.843

7.  iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.

Authors:  Xuan Xiao; Mengjuan Hui; Zi Liu
Journal:  J Membr Biol       Date:  2016-11-03       Impact factor: 1.843

8.  Evolutionary insights into the active-site structures of the metallo-β-lactamase superfamily from a classification study with support vector machine.

Authors:  Lili Wang; Ling Yang; Yu-Lan Feng; Hao Zhang
Journal:  J Biol Inorg Chem       Date:  2020-09-18       Impact factor: 3.358

9.  The Secreted Form of Transmembrane Protein 98 Promotes the Differentiation of T Helper 1 Cells.

Authors:  Weiwei Fu; Yingying Cheng; Yanfei Zhang; Xiaoning Mo; Ting Li; Yuanfeng Liu; Pingzhang Wang; Wen Pan; Yingyu Chen; Yintong Xue; Dalong Ma; Yu Zhang; Wenling Han
Journal:  J Interferon Cytokine Res       Date:  2015-05-06       Impact factor: 2.607

10.  A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.

Authors:  Chao Huang; Jing-Qi Yuan
Journal:  J Membr Biol       Date:  2013-04-02       Impact factor: 1.843

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