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Title: Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5’-Phosphate Production in E. coli

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. Here, 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 amore » 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.« less
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [2] ;  [4] ;  [2] ;  [6] ;  [2] ;  [1]
  1. Tel Aviv Univ., Tel Aviv (Israel); Univ. of Maryland, College Park, MD (United States)
  2. Tel Aviv Univ., Tel Aviv (Israel)
  3. Univ. of Maryland, College Park, MD (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States)
  5. Inst. for Research in Biomedicine (IRB), Barcelona (Spain)
  6. Univ. of Maryland, College Park, MD (United States). Maryland Pathogen Research Institute
Publication Date:
OSTI Identifier:
1258643
Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 12; Journal Issue: 1; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Research Org:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES