Back to Search
Start Over
Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.
- Source :
- PLoS Computational Biology, Vol 12, Iss 1, p e1004705 (2016)
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
Abstract
- Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.
- Subjects :
- Biology (General)
QH301-705.5
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X and 15537358
- Volume :
- 12
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4ec339628dd24f6596250a5429b8f4bb
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1004705