What question did this study set out to answer?

This research aims to enhance the annotation of untargeted LC–MS/MS metabolic features by developing a computational framework that generates candidate enzyme and gene hypotheses.

July 13, 2026Journal of Proteome Research

Reverse Biotransformation-Guided Annotation of Untargeted MS/MS Features: A Computational Framework for Candidate Enzyme and Gene Hypothesis Generation

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Authors

SRSmaroki Smruti Rekha PAPalok Aich

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Overview

Randomized trial evaluates metabolic feature annotations in various experimental datasets, indicating a reliable framework for generating enzyme and gene hypotheses.

Key Points

This research aims to enhance the annotation of untargeted LC–MS/MS metabolic features by developing a computational framework that generates candidate enzyme and gene hypotheses.
Developed a modular workflow for identifying structural analogs from MS/MS features.
Applied rule-based reverse biotransformation for inferring plausible precursor reactions linked to candidate enzymes and genes.
Conducted systematic sensitivity analyses on reference datasets to validate the annotation process.
Generated gene sets showed significant pathway enrichment, surpassing baseline associations.
For a lesser-characterized metabolite, genes inferred were highly enriched in glutathione metabolism and oxidative stress pathways (adjusted p < 2 × 10–4).
The approach provided interpretable results across various experimental datasets, enhancing biological insights.

Cite This Study

Rekha et al. (2026) studied this question.

synapsesocial.com/papers/6a547ff4475c38bf615a52a6 https://doi.org/10.1021/acs.jproteome.6c00138

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July 13, 2026Journal of Proteome Research

Reverse Biotransformation-Guided Annotation of Untargeted MS/MS Features: A Computational Framework for Candidate Enzyme and Gene Hypothesis Generation

View Full Paper

Ask AI

Bookmark

Authors

SRSmaroki Smruti Rekha PAPalok Aich

Discussion

Member takes

Overview

Randomized trial evaluates metabolic feature annotations in various experimental datasets, indicating a reliable framework for generating enzyme and gene hypotheses.

Key Points

This research aims to enhance the annotation of untargeted LC–MS/MS metabolic features by developing a computational framework that generates candidate enzyme and gene hypotheses.
Developed a modular workflow for identifying structural analogs from MS/MS features.
Applied rule-based reverse biotransformation for inferring plausible precursor reactions linked to candidate enzymes and genes.
Conducted systematic sensitivity analyses on reference datasets to validate the annotation process.
Generated gene sets showed significant pathway enrichment, surpassing baseline associations.
For a lesser-characterized metabolite, genes inferred were highly enriched in glutathione metabolism and oxidative stress pathways (adjusted p < 2 × 10–4).
The approach provided interpretable results across various experimental datasets, enhancing biological insights.

Cite This Study

Rekha et al. (2026) studied this question.

synapsesocial.com/papers/6a547ff4475c38bf615a52a6 https://doi.org/10.1021/acs.jproteome.6c00138

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