Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification

A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixe...

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Autores principales:	Gang Xing, Vishnu Sresht, Zhongyuan Sun, Yuji Shi, Michelle F. Clasquin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	metabolomics LCMS retention time prediction machine learning MPNN Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/e5dddccf0193471bb907c622719dee88
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spelling	oai:doaj.org-article:e5dddccf0193471bb907c622719dee882021-11-25T18:20:48ZCoupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification10.3390/metabo111107722218-1989https://doaj.org/article/e5dddccf0193471bb907c622719dee882021-11-01T00:00:00Zhttps://www.mdpi.com/2218-1989/11/11/772https://doaj.org/toc/2218-1989A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R<sup>2</sup> > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.Gang XingVishnu SreshtZhongyuan SunYuji ShiMichelle F. ClasquinMDPI AGarticlemetabolomicsLCMSretention time predictionmachine learningMPNNMicrobiologyQR1-502ENMetabolites, Vol 11, Iss 772, p 772 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	metabolomics LCMS retention time prediction machine learning MPNN Microbiology QR1-502
spellingShingle	metabolomics LCMS retention time prediction machine learning MPNN Microbiology QR1-502 Gang Xing Vishnu Sresht Zhongyuan Sun Yuji Shi Michelle F. Clasquin Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
description	A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R<sup>2</sup> > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.
format	article
author	Gang Xing Vishnu Sresht Zhongyuan Sun Yuji Shi Michelle F. Clasquin
author_facet	Gang Xing Vishnu Sresht Zhongyuan Sun Yuji Shi Michelle F. Clasquin
author_sort	Gang Xing
title	Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
title_short	Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
title_full	Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
title_fullStr	Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
title_full_unstemmed	Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
title_sort	coupling mixed mode chromatography/esi negative ms detection with message-passing neural network modeling for enhanced metabolome coverage and structural identification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e5dddccf0193471bb907c622719dee88
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_version_	1718411352548048896

Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification

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