Literature DB >> 7670379

A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase.

K C Chou1.   

Abstract

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1', R2', R3', and R4', where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is being anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a new method has been proposed based on the vector-projection approach as well as the sequence-coupled principle. By incorporating the sequence-coupled effect among the subsites, the interaction mechanism among subsites during glycosylation can be reflected and, by using the vector projection approach, arbitrary assignment for insufficient experimental data can be avoided. The very high ratio of correct predictions versus total predictions for the data in both the training and the testing sets indicates that the method is self-consistent and efficient. It provides a rapid means for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase, which might be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.

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Year:  1995        PMID: 7670379      PMCID: PMC2143175          DOI: 10.1002/pro.5560040712

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  22 in total

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Review 5.  Assembly of asparagine-linked oligosaccharides.

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Journal:  Biochemistry       Date:  1985-09-24       Impact factor: 3.162

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Journal:  Mol Cell Biochem       Date:  1986 Nov-Dec       Impact factor: 3.396

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  20 in total

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Authors:  J E Hansen; O Lund; N Tolstrup; A A Gooley; K L Williams; S Brunak
Journal:  Glycoconj J       Date:  1998-02       Impact factor: 2.916

3.  Database analysis of O-glycosylation sites in proteins.

Authors:  T H Thanka Christlet; K Veluraja
Journal:  Biophys J       Date:  2001-02       Impact factor: 4.033

4.  A novel model to predict O-glycosylation sites using a highly unbalanced dataset.

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Journal:  Glycoconj J       Date:  2012-08-03       Impact factor: 2.916

5.  Predicting drug-target interaction networks based on functional groups and biological features.

Authors:  Zhisong He; Jian Zhang; Xiao-He Shi; Le-Le Hu; Xiangyin Kong; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2010-03-11       Impact factor: 3.240

6.  Structural features of the human salivary mucin, MUC7.

Authors:  T L Gururaja; N Ramasubbu; P Venugopalan; M S Reddy; K Ramalingam; M J Levine
Journal:  Glycoconj J       Date:  1998-05       Impact factor: 2.916

7.  Prediction of protein domain with mRMR feature selection and analysis.

Authors:  Bi-Qing Li; Le-Le Hu; Lei Chen; Kai-Yan Feng; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2012-06-15       Impact factor: 3.240

8.  Classification of protein quaternary structure by functional domain composition.

Authors:  Xiaojing Yu; Chuan Wang; Yixue Li
Journal:  BMC Bioinformatics       Date:  2006-04-04       Impact factor: 3.169

9.  Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs.

Authors:  Yong-Zi Chen; Yu-Rong Tang; Zhi-Ya Sheng; Ziding Zhang
Journal:  BMC Bioinformatics       Date:  2008-02-18       Impact factor: 3.169

10.  iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins.

Authors:  Yan Xu; Xiao-Jian Shao; Ling-Yun Wu; Nai-Yang Deng; Kuo-Chen Chou
Journal:  PeerJ       Date:  2013-10-03       Impact factor: 2.984

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