Journal
SCIENCE
Volume 340, Issue 6137, Pages 1220-1223Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.1234012
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Funding
- NSF [GK-12 742551]
- U.S. Department of Energy [DE-SC0004917]
- NIH [R01GM101457, R01 GM057089, U54GM094586]
- Novo Nordisk Foundation
- U.S. Department of Energy (DOE) [DE-SC0004917] Funding Source: U.S. Department of Energy (DOE)
- Novo Nordisk Fonden [NNF10CC1016517] Funding Source: researchfish
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Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.
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