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The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline calledvolcalcthat uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate howvolcalcmight be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. Withvolcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. Thevolcalcpackage is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.
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Calculated volatilities for all metabolites and their related genomes in the KEGG database
Volatility describes the tendency for a compound to partition into the gas phase and volatile metabolites facilitate unique biological interactions which have an influence on Earth's atmospheric physics and chemistry. Estimating which metabolites may be volatile is difficult, especially for those which do not have measured vapor pressures. Volcalc is a newly developed vapor pressure estimation tool which utilizes the SIMPOL.1 method, allowing users to rapidly identify plausible volatile metabolites within the Kyoto Encyclopedia for Genes and Genomes (KEGG) database. Here, we estimate the volatiles of all KEGG metabolites and associate them with KEGG reactions, enzymes, orthologs (KOs) and whole genomes within the KEGG database. This information may be used to identify which genes or species may be linked to particular forms of volatile metabolism, for the purpose hypothesis generation and integration into additional bioinformatics pipelines. This data is listed as a compliment to the publication "Automating methods for estimating metabolite volatility". The column "Paper" indicates whether the listed species is one from the subset analyzed within the data for Figure 3.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu
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- Award ID(s):
- 2045332
- PAR ID:
- 10542217
- Publisher / Repository:
- University of Arizona Research Data Repository
- Date Published:
- Subject(s) / Keyword(s):
- Bioinformatic methods development Proteomics and metabolomics
- Format(s):
- Medium: X Size: 337736220 Bytes
- Size(s):
- 337736220 Bytes
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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