Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions.

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.

A pathway for every product? Tools to discover and design plant metabolism.

The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.

Metabolite damage and repair in metabolic engineering design.

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways - particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile 'plug and play' set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.

Predicting novel substrates for enzymes with minimal experimental effort with active learning.

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.

'Nothing of chemistry disappears in biology': the Top 30 damage-prone endogenous metabolites.

Many common metabolites are intrinsically unstable and reactive, and hence prone to chemical (i.e. non-enzymatic) damage in vivo Although this fact is widely recognized, the purely chemical side-reactions of metabolic intermediates can be surprisingly hard to track down in the literature and are often treated in an unprioritized case-by-case way. Moreover, spontaneous chemical side-reactions tend to be overshadowed today by side-reactions mediated by promiscuous ('sloppy') enzymes even though chemical damage to metabolites may be even more prevalent than damage from enzyme sloppiness, has similar outcomes, and is held in check by similar biochemical repair or pre-emption mechanisms. To address these limitations and imbalances, here we draw together and systematically integrate information from the (bio)chemical literature, from cheminformatics, and from genome-scale metabolic models to objectively define a 'Top 30' list of damage-prone metabolites. A foundational part of this process was to derive general reaction rules for the damage chemistries involved. The criteria for a 'Top 30' metabolite included predicted chemical reactivity, essentiality, and occurrence in diverse organisms. We also explain how the damage chemistry reaction rules ('operators') are implemented in the Chemical-Damage-MINE (CD-MINE) database (minedatabase.mcs.anl.gov/#/top30) to provide a predictive tool for many additional potential metabolite damage products. Lastly, we illustrate how defining a 'Top 30' list can drive genomics-enabled discovery of the enzymes of previously unrecognized damage-control systems, and how applying chemical damage reaction rules can help identify previously unknown peaks in metabolomics profiles.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics.

BACKGROUND: In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. DESCRIPTION: Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. CONCLUSIONS: MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

A directed-overflow and damage-control N-glycosidase in riboflavin biosynthesis.

Plants and bacteria synthesize the essential human micronutrient riboflavin (vitamin B2) via the same multi-step pathway. The early intermediates of this pathway are notoriously reactive and may be overproduced in vivo because riboflavin biosynthesis enzymes lack feedback controls. In the present paper, we demonstrate disposal of riboflavin intermediates by COG3236 (DUF1768), a protein of previously unknown function that is fused to two different riboflavin pathway enzymes in plants and bacteria (RIBR and RibA respectively). We present cheminformatic, biochemical, genetic and genomic evidence to show that: (i) plant and bacterial COG3236 proteins cleave the N-glycosidic bond of the first two intermediates of riboflavin biosynthesis, yielding relatively innocuous products; (ii) certain COG3236 proteins are in a multi-enzyme riboflavin biosynthesis complex that gives them privileged access to riboflavin intermediates; and (iii) COG3236 action in Arabidopsis thaliana and Escherichia coli helps maintain flavin levels. COG3236 proteins thus illustrate two emerging principles in chemical biology: directed overflow metabolism, in which excess flux is diverted out of a pathway, and the pre-emption of damage from reactive metabolites.