Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions
Institution:
1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA;2. Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, 60637, USA;3. Department of Data Science and Learning, Argonne National Laboratory, Argonne, IL, 60439, USA;4. Department of Carcinogenesis and Oncogerontology, N.N. Petrov National Medical Research Center of Oncology, Saint Petersburg, 197758, Russia;5. Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA;6. Plant and Microbial Biology Department, University of Minnesota, Saint Paul, MN, 55108, USA;7. West Coast Metabolomics Center, University of California, Davis, CA, USA;8. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA;1. US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA;2. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA;3. USDA Agricultural Research Service, Wheat Health, Genetics and Quality, Washington State University, Pullman, WA, USA;4. Department of Plant Pathology, Washington State University, Pullman, WA, USA;5. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA;6. Center for Advanced Bioenergy and Bioproducts Innovation, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA;7. Global Center for Food, Land, and Water Resources, Hokkaido University, Hokkaido, 060-8589, Japan;1. Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA;2. Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA;3. Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, 90064, USA;4. Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA;5. Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA;6. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA;1. Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China;2. Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China;1. State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, China;2. International Joint Laboratory on Food Safety, Jiangnan University, Wuxi, 214122, China;3. Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China;4. Suzhou Microvac Biotech Co., Ltd., Suzhou, 215021, China;1. Department of Chemistry, University of California, Davis, CA, 95616, USA;2. Genome Center, University of California, Davis, CA, 95616, USA;3. Department of Biomedical Engineering, University of California, Davis, CA 95616, USA;4. Department of Computer Science, University of California, Davis, CA, 95616, USA
Abstract:
Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways’ side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl.gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.
