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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
High precision multi-genome scale reannotation of enzyme function by EFICAz
Adrian K Arakaki1, Weidong Tian, Jeffrey Skolnick
1Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA. adrian.arakaki@gatech.edu <adrian.arakaki@gatech.edu>
BMC Genomics
|December 15, 2006
Summary
This study reannotates 245 genomes using EFICAz, improving enzyme function prediction accuracy. The enhanced method identifies novel enzyme functions and corrects existing annotations, aiding comparative genomics and pathway reconstruction.
Area of Science:
- Genomics
- Bioinformatics
- Enzymology
Background:
- Gene annotation often relies on sequence similarity, leading to inaccuracies.
- A reannotation of 245 genomes was performed using an updated EFICAz tool for enzyme function prediction.
Purpose of the Study:
- To improve the accuracy and coverage of enzyme function predictions in newly sequenced genomes.
- To identify novel enzyme functions and correct erroneous existing annotations.
Main Methods:
- Utilized an updated version of EFICAz, a precise enzyme function prediction method.
- Performed reannotation across 245 genomes, including Archaea, Bacteria, and Eukarya.
- Validated predictions against recently characterized hypothetical proteins and existing database entries.
Main Results:
- Achieved lower-bound estimates for average enzyme content: Archaea (29%), Bacteria (30%), Eukarya (18%).
- EFICAz predictions showed higher coverage than KEGG, especially for eukaryotes.
- Correctly identified functions for 96% (three-field) and 84% (four-field) of tested hypothetical proteins.
- Identified annotation lags in databases for proteins like PA1167 and Rv1700.
- Provided hypotheses for hypothetical proteins, such as FLJ11151 (endopolyphosphatase) and MW0119 (sphingomyelin phosphodiesterase).
Conclusions:
- Generated enzyme function annotations with high precision and recall.
- EFICAz predictions can generate biologically significant hypotheses.
- Useful for comparative genome analysis and automated metabolic pathway reconstruction.
