U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Understanding Enzyme Specificity Through Systems-Level Metabolic Modeling
Published: August 31, 2012
Posted: October 18, 2012

In biology, some enzymes are highly specialized and catalyze specific reactions with a few or only one substrate, while other enzymes are promiscuous and can catalyze reactions using a variety of substrates. This phenomenon also has been observed experimentally for microbes involved in bioenergy-related processes. What is not understood, however, is why, within an organism, some enzymes are highly specialized while others remain generalists. Recently, researchers addressed this question using whole genome metabolic reconstructions and analysis, including dynamical simulations of environmental changes to understand microbial responses. Their findings indicate that enzymes with very specialized function maintain a higher flux, or processing rate, and require more regulation of their activities. This higher flux and higher regulation allows these enzymes to be more responsive and adaptive to environmental surroundings and changes then their less specialized counterparts. This work also illustrates that understanding environmental cellular physiology is greatly enhanced when using a systems biology approach rather than approaches that are focused on single enzyme simulations. These new results offer a means of translating genomic information into functional capabilities, with particular relevance for microbes involved in biofuel production.

Reference: Nam, H., N. E. Lewis, J. A. Lerman, D.-H. Lee, R. L. Chang, D. Kim, and B. O. Palsson. 2012. "Network Context and Selection in the Evolution to Enzyme Specificity," Science 337(6098), 1101-04. DOI: 10.1126/science.1216861. (Reference link)

Contact: Susan Gregurick, SC-23.2, (301) 903-7672
Topic Areas:

  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Microbes and Communities
  • Research Area: Sustainable Biofuels and Bioproducts
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-23.2 Biological Systems Science Division, BER


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