SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations
- University of California, San Francisco, United States
- Gladstone Institutes, United States
Abstract
Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). We show that SIMMER accurately predicts the responsible species and enzymes for a queried reaction, unlike previous methods. We demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. We validate these predictions on external datasets and provide an in vitro validation of SIMMER's predictions for metabolism of methotrexate, an anti-arthritic drug. After demonstrating its utility and accuracy, we made SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. We present SIMMER as a computational addition to the microbiome researcher's toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds.
Data availability
Data generated and analyzed during this study are provided in Figures 2-10 source data files, Table 1 source data file, supplemental files, and at https://github.com/aebustion/SIMMER. Accession numbers of previously published datasets are provided in the Materials and Methods section. SIMMER code can either be run at the SIMMER website (https://simmer.pollard.gladstone.org/) or downloaded directly from the above-linked GitHub.
Article and author information
Author details
Annamarie E Bustion
Katherine S Pollard
Funding
PhRMA Foundation (Predoctoral Fellowship)
- Annamarie E Bustion
ARCS Foundation (Graduate Student Scholarship)
- Annamarie E Bustion
UCSF Benioff Center for Microbiome Medicine (Trainee Pilot Award)
- Annamarie E Bustion
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Bustion et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations
eLife 12:e82401.
https://doi.org/10.7554/eLife.82401
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