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

Abstract

BackgroundIn 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.DescriptionHere 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.ConclusionsMINE 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0087-1) contains supplementary material, which is available to authorized users.

Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis

“…One solution to increase mass spectral library coverage is to use quantum chemical simulations predict electron ionization mass spectra9 or to utilize novel machine learning methods to improve compound identification10. This can also include novel metabolic compounds that can be expected to exist from known metabolic transformations11.…”

<strong>Interstitial Cystitis-Associated Urinary Metabolites </strong><strong>Identified by Mass-Spectrometry Based Metabolomics Analysis</strong>

“…One solution to increase mass spectral library coverage is to use quantum chemical simulations predict electron ionization mass spectra 9 or to utilize novel machine learning methods to improve compound identification 10 . This can also include novel metabolic compounds that can be expected to exist from known metabolic transformations 11 . Histidine, one of essential amino acids in humans, is a known precursor of the neurotransmitter histamine.…”

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

“…In one particularly impressive example, MS/MS spectra were predicted via CFM-ID for all of the compounds in the Dictionary of Natural Products [115]. By constructing a spectral similarity network and using sophisticated spectral matching comparisons it was possible to use this network of predicted MS/MS spectra to identify a number of previously unidentified compounds [155]. …”

“…A number of commercial tools now exist for calculating phase I metabolism and phase I metabolite structures using known structures as the starting point [156]. Software is also starting to appear to predict promiscuous enzyme products [155] and microbial biotransformations [157] from known structures. Given the existence of these tools, it seems that the next logical step would be to bring them together and to start creating a large database of biologically reasonable or biologically feasible metabolites.…”