Genome-Wide Enzyme Annotation with Precision Control: Catalytic Families (CatFam) Databases
Abstract
In this article, we present a new method termed Cat- Fam (Catalytic Families) to automatically infer the functions of catalytic proteins, which account for 20-40% of all proteins in living organisms and play a critical role in a variety of biological processes. CatFam is a sequence-based method that generates sequence profiles to represent and infer protein catalytic functions. CatFam generates profiles through a stepwise procedure that carefully controls profile quality and employs nonenzymes as negative samples to establish profile-specific thresholds associated with a predefined nominal false-positive rate (FPR) of predictions. The adjustable FPR allows for fine precision control of each profile and enables the generation of profile databases that meet different needs: function annotation with high precision and hypothesis generation with moderate precision but better recall. Multiple tests of CatFam databases (generated with distinct nominal FPRs) against enzyme and nonenzyme datasets show that the method's predictions have consistently high precision and recall. For example, a 1% FPR database predicts protein catalytic functions for a dataset of enzymes and nonenzymes with 98.6% precision and 95.0% recall. Comparisons of CatFam databases against other established profile-based methods for the functional annotation of 13 bacterial genomes indicate that CatFam consistently achieves higher precision and (in most cases) higher recall, and that (on average) CatFam provides 21.9% additional catalytic functions not inferred by the other similarly reliable methods. These results strongly suggest that the proposed method provides a valuable contribution to the automated prediction of protein catalytic functions. The CatFam databases and the database search program are freely available at http://www.bhsai.org/ downloads/catfam.tar.gz.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2008
- Accession Number
- ADA593510
Entities
People
- Chenggang Yu
- Jaques Reifman
- Nela Zavaljevski
- Valmik Desai
Organizations
- United States Army Medical Research and Development Command