Literature DB >> 26077845

iCataly-PseAAC: Identification of Enzymes Catalytic Sites Using Sequence Evolution Information with Grey Model GM (2,1).

Xuan Xiao1,2,3, Meng-Juan Hui4, Zi Liu5, Wang-Ren Qiu6.   

Abstract

Enzymes play pivotal roles in most of the biological reaction. The catalytic residues of an enzyme are defined as the amino acids which are directly involved in chemical catalysis; the knowledge of these residues is important for understanding enzyme function. Given an enzyme, which residues are the catalytic sites, and which residues are not? This is the first important problem for in-depth understanding the catalytic mechanism and drug development. With the explosive of protein sequences generated during the post-genomic era, it is highly desirable for both basic research and drug design to develop fast and reliable method for identifying the catalytic sites of enzymes according to their sequences. To address this problem, we proposed a new predictor, called iCataly-PseAAC. In the prediction system, the peptide sample was formulated with sequence evolution information via grey system model GM(2,1). It was observed by the rigorous jackknife test and independent dataset test that iCataly-PseAAC was superior to exist predictions though its only use sequence information. As a user-friendly web server, iCataly-PseAAC is freely accessible at http://www.jci-bioinfo.cn/iCataly-PseAAC. A step-by-step guide has been provided on how to use the web server to get the desired results for the convenience of most experimental scientists.

Keywords:  Catalytic active sites; Grey system model; Pseudo amino acid composition; Web server; iCataly-PseAAC

Mesh:

Substances:

Year:  2015        PMID: 26077845     DOI: 10.1007/s00232-015-9815-8

Source DB:  PubMed          Journal:  J Membr Biol        ISSN: 0022-2631            Impact factor:   1.843


  37 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Prediction of catalytic residues in enzymes based on known tertiary structure, stability profile, and sequence conservation.

Authors:  Motonori Ota; Kengo Kinoshita; Ken Nishikawa
Journal:  J Mol Biol       Date:  2003-04-11       Impact factor: 5.469

3.  Analysis of catalytic residues in enzyme active sites.

Authors:  Gail J Bartlett; Craig T Porter; Neera Borkakoti; Janet M Thornton
Journal:  J Mol Biol       Date:  2002-11-15       Impact factor: 5.469

4.  Prediction of protein functional residues from sequence by probability density estimation.

Authors:  J D Fischer; C E Mayer; J Söding
Journal:  Bioinformatics       Date:  2008-01-02       Impact factor: 6.937

5.  Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.

Authors:  Yu-Fei Gao; Bi-Qing Li; Yu-Dong Cai; Kai-Yan Feng; Zhan-Dong Li; Yang Jiang
Journal:  Mol Biosyst       Date:  2012-11-02

6.  Predicting protein folding types by distance functions that make allowances for amino acid interactions.

Authors:  K C Chou; C T Zhang
Journal:  J Biol Chem       Date:  1994-09-02       Impact factor: 5.157

7.  NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features.

Authors:  Pu Wang; Xuan Xiao; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-08-15       Impact factor: 3.240

8.  Some remarks on protein attribute prediction and pseudo amino acid composition.

Authors:  Kuo-Chen Chou
Journal:  J Theor Biol       Date:  2010-12-17       Impact factor: 2.691

9.  CD-HIT: accelerated for clustering the next-generation sequencing data.

Authors:  Limin Fu; Beifang Niu; Zhengwei Zhu; Sitao Wu; Weizhong Li
Journal:  Bioinformatics       Date:  2012-10-11       Impact factor: 6.937

10.  How accurate and statistically robust are catalytic site predictions based on closeness centrality?

Authors:  Eric Chea; Dennis R Livesay
Journal:  BMC Bioinformatics       Date:  2007-05-11       Impact factor: 3.169

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  3 in total

1.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

2.  Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.

Authors:  Khurshid Ahmad; Muhammad Waris; Maqsood Hayat
Journal:  J Membr Biol       Date:  2016-01-08       Impact factor: 1.843

3.  The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China.

Authors:  Xiaobing Yang; Jiaojiao Zou; Deguang Kong; Gaofeng Jiang
Journal:  Medicine (Baltimore)       Date:  2018-08       Impact factor: 1.817

  3 in total

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