EnzDP: Improved enzyme annotation for metabolic network reconstruction based on domain composition profiles

Nguyen, Nam-Ninh, Srihari, Sriganesh, Leong, Hon Wai and Chong, Ket-Fah (2015). EnzDP: Improved enzyme annotation for metabolic network reconstruction based on domain composition profiles. In: Introduction to selected papers from GIW/InCoB 2015. GIW/InCoB 2015, Odaiba, Tokyo Bay, Japan, (1543003.1-1543003.30). 9-11 September 2015. doi:10.1142/S0219720015430039

Author Nguyen, Nam-Ninh
Srihari, Sriganesh
Leong, Hon Wai
Chong, Ket-Fah
Title of paper EnzDP: Improved enzyme annotation for metabolic network reconstruction based on domain composition profiles
Conference name GIW/InCoB 2015
Conference location Odaiba, Tokyo Bay, Japan
Conference dates 9-11 September 2015
Proceedings title Introduction to selected papers from GIW/InCoB 2015   Check publisher's open access policy
Journal name Journal of Bioinformatics and Computational Biology   Check publisher's open access policy
Place of Publication London, United Kingdom
Publisher Imperial College Press
Publication Year 2015
Year available 2015
Sub-type Fully published paper
DOI 10.1142/S0219720015430039
Open Access Status Not Open Access
ISSN 0219-7200
Volume 13
Issue 5
Start page 1543003.1
End page 1543003.30
Total pages 30
Collection year 2016
Language eng
Formatted Abstract/Summary
Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP.

Keyword Automated function prediction
Domain enzyme association
Enzyme classification
Functional domain architecture
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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