Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

Bach, E.; Schymanski, E.; Rousu, J.

doi:10.1101/2022.02.11.480137

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bioRxiv · 10.1101/2022.02.11.480137

Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

Bach, E.·Schymanski, E.·Rousu, J.

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Abstract

We present LC-MS2Struct, a machine learning framework for structural annotation of small molecule data arising from liquid chromatography-tandem mass spectrometry (LC-MS2) measurements. LC-MS2Struct jointly predicts the annotations for a set of mass spectrometry features in a sample, using a novel structured prediction model trained to optimally combine the output of state-of-the-art MS2 scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed phase LC-MS2 data in the MassBank reference database, including 4327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS2 scorer evaluations. LC-MS2Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS2 scorers by up to 106%. The use of stereochemistry-aware molecular fingerprints improves prediction performance, which highlights limitations in existing approaches and has strong implications for future computational LC-MS2 developments.

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Bach, E., Schymanski, E., Rousu, J.. 2022-02-14. Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data. https://doi.org/10.1101/2022.02.11.480137

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