Building a complete metabolic model

Investigators at Burnham Institute for Medical Research, University of California, San Diego, The Scripps Research Institute, Genomics Institute of the Novartis Research Foundation and other institutions have constructed a complete model, including three-dimensional protein structures, of the central metabolic network of the bacterium Thermotoga maritima (T. maritima). This is the first time scientists have developed such a comprehensive model of a metabolic network overlaid with an atomic resolution of network proteins. The analysis of the model, among others, highlights the important role of a small number of essential protein shapes, lending new insights into the evolution of protein networks and the functions within these networks.

Combining biochemical studies, structural genomics and computer modelling, the researchers deciphered the shapes, functions and interactions of 478 proteins that make up T. maritima’s central metabolism. The team also found connections between these proteins and 503 unique metabolites in 562 intracellular and 83 extracellular metabolic reactions.

With this data, scientists can simulate metabolism simultaneously on a biochemical and molecular level. This information has the promise to expand computer modelling to allow investigators to simulate the interactions between proteins and various compounds in an entire system. Furthermore, the procedure developed in this study could be applied to study many other organisms, including humans. It could potentially help identify both positive and adverse drug reactions before pre-clinical and clinical trials. The research may also have applications in energy research, as bacteria like T. maritima can be engineered to more efficiently produce hydrogen, a key source of clean energy.

Researchers were surprised by the degree of structural conservation within the network. Of the 478 proteins, with 714 domains, there were only 182 distinct folds. This supports the hypothesis that nature uses existing shapes, slightly modified, to perform new tasks.

The team used genomic, metabolic and structural reconstruction to determine the network down to the atomic level. They then classified metabolic reactions based on whether they were similar, connected or unrelated and found that enzymes that catalyse similar reactions have a higher probability of having similar folds. In addition, using a reductive evolution simulation approach, they uncovered the absolutely essential proteins to support a minimal viable network.

This work was funded by grants from the National Institute of General Medical Sciences and the Office of Biological and Environmental Research within the US Department of Energy's Office of Science.