Literature DB >> 19100748

Sequence and structural features of enzymes and their active sites by EC class.

Tracey Bray1, Andrew J Doig, Jim Warwicker.   

Abstract

We have analysed a non-redundant set of 294 enzymes for differences in sequence and structural features between the six main Enzyme Commission (EC) classification groups. This systematic study of enzymes, and their active sites in particular, aims to increase understanding of how the structure of an enzyme relates to its functional role. Many features showed significant differences between the EC classes, including active-site polarity, enzyme size and active-site amino acid propensities. Many attributes correlate with each other to form clusters of related features from which we chose representative features for further analysis. Oxidoreductases have more non-polar active sites, which can be attributed to cofactor binding and a preference for Glu over Asp in active sites in comparison to the other classes. Lyases form a significantly higher proportion of oligomers than any other class, whilst the hydrolases form the largest proportion of monomers. These features were then used in a prediction model that classified each enzyme into its top EC class with an accuracy of 33.1%, which is an increase of 16.4% over random classification. Understanding the link between structure and function is critical to improving enzyme design and the prediction of protein function from structure without transfer of annotation from alignments.

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Year:  2008        PMID: 19100748     DOI: 10.1016/j.jmb.2008.11.057

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  8 in total

1.  Computational Approaches for Automated Classification of Enzyme Sequences.

Authors:  Akram Mohammed; Chittibabu Guda
Journal:  J Proteomics Bioinform       Date:  2011-08-23

2.  Is EC class predictable from reaction mechanism?

Authors:  Neetika Nath; John B O Mitchell
Journal:  BMC Bioinformatics       Date:  2012-04-24       Impact factor: 3.169

3.  Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.

Authors:  Chioko Nagao; Nozomi Nagano; Kenji Mizuguchi
Journal:  PLoS One       Date:  2014-01-08       Impact factor: 3.240

Review 4.  Exploring the biological and chemical complexity of the ligases.

Authors:  Gemma L Holliday; Syed Asad Rahman; Nicholas Furnham; Janet M Thornton
Journal:  J Mol Biol       Date:  2014-03-21       Impact factor: 5.469

5.  Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

Authors:  Gemma L Holliday; Rebecca Davidson; Eyal Akiva; Patricia C Babbitt
Journal:  Methods Mol Biol       Date:  2017

6.  SitesIdentify: a protein functional site prediction tool.

Authors:  Tracey Bray; Pedro Chan; Salim Bougouffa; Richard Greaves; Andrew J Doig; Jim Warwicker
Journal:  BMC Bioinformatics       Date:  2009-11-18       Impact factor: 3.169

7.  Evidence for the adaptation of protein pH-dependence to subcellular pH.

Authors:  Pedro Chan; Jim Warwicker
Journal:  BMC Biol       Date:  2009-10-22       Impact factor: 7.431

8.  Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering.

Authors:  Elisa Boari de Lima; Wagner Meira; Raquel Cardoso de Melo-Minardi
Journal:  PLoS Comput Biol       Date:  2016-06-27       Impact factor: 4.475

  8 in total

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