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MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data.

Nami Sakamoto1, Takaki Oka1, Yuki Matsuzawa1

  • 1Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan.

Metabolites
|November 26, 2024
PubMed
Summary

MS2Lipid, a new machine learning model, accurately predicts lipid subclasses from tandem mass spectrometry (MS/MS) data. This tool enhances lipid metabolite annotation, offering an independent criterion for biological and clinical research.

Keywords:
human fecal sampleslipid class predictionmachine learningmicrobiota-dependent lipidstandem mass spectrumuntargeted lipidomics

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Computational Biology

Background:

  • Untargeted lipidomics relies on tandem mass spectrometry (MS/MS) for biological and clinical insights.
  • Current automated software for lipid subclass annotation often requires manual curation, impacting confidence.
  • There is a need for more reliable and automated methods for lipid identification.

Purpose of the Study:

  • To develop and validate MS2Lipid, a novel machine learning model for predicting lipid subclasses from MS/MS spectra.
  • To provide an orthogonal and independent method for lipid subclass classification.
  • To enhance the accuracy and confidence of lipidomic data analysis.

Main Methods:

  • Developed MS2Lipid, a machine learning model utilizing MS/MS spectral data.
  • Introduced a new descriptor, MCH (mode of carbon and hydrogen), for improved specificity in nominal mass resolution.
  • Trained the model on extensive manually curated MS/MS spectra for positive and negative ion modes.

Main Results:

  • MS2Lipid achieved 97.4% accuracy in predicting lipid subclasses on a test set.
  • Validated across diverse datasets, the model demonstrated an average accuracy exceeding 87.2%.
  • Successfully annotated microbiota-derived esterified bile acids in human cohort samples, correlating with obesity.

Conclusions:

  • MS2Lipid is a highly accurate machine learning tool for lipid subclass annotation.
  • The model offers an independent criterion, improving the reliability of lipidomic analyses.
  • MS2Lipid enhances the identification of lipid metabolites, particularly in complex biological samples.