Dataset Information

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

ABSTRACT:

Introduction

Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.

Objective

The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.

Methods

We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study.

Results

We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag.

Conclusion

MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.

SUBMITTER: Fan Z

PROVIDER: S-EPMC9547616 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

Fan Ziling Z Alley Amber A Ghaffari Kian K Ressom Habtom W HW

Metabolomics : Official journal of the Metabolomic Society 20200930 10

<h4>Introduction</h4>Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.<h4>Objective</h4>The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference M ...[more]

PMID: 32997169

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Dataset Information

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

Introduction

Objective

Methods

Results

Conclusion

Publications

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

Similar Datasets

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