Current status of retention time prediction in metabolite identification

Witting, Michael; Böcker, Sebastian

doi:10.1002/jssc.202000060

Cited by 83 publications

(60 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As described above, metabolites with the same chemical formula can generally not be distinguished only based on their MS/MS fragmentation pattern. Chromatographic properties and retention time are difficult to predict for LC-MS-based analysis of similar compounds as a retention index (RI) can currently only be used for gas chromatography coupled to mass spectrometry (GC-MS) analysis [ 34 ]. Sulfated phenolic aromatic compounds with the same chemical formula commonly have different substitution pattern such as ferulic acid sulfate and isoferulic acid sulfate which can be present in all human sample types.…”

Section: Resultsmentioning

confidence: 99%

Rapid Preparation of a Large Sulfated Metabolite Library for Structure Validation in Human Samples

et al. 2020

View full text Add to dashboard Cite

Metabolomics analysis of biological samples is widely applied in medical and natural sciences. Assigning the correct chemical structure in the metabolite identification process is required to draw the correct biological conclusions and still remains a major challenge in this research field. Several metabolite tandem mass spectrometry (MS/MS) fragmentation spectra libraries have been developed that are either based on computational methods or authentic libraries. These libraries are limited due to the high number of structurally diverse metabolites, low commercial availability of these compounds, and the increasing number of newly discovered metabolites. Phase II modification of xenobiotics is a compound class that is underrepresented in these databases despite their importance in diet, drug, or microbiome metabolism. The O-sulfated metabolites have been described as a signature for the co-metabolism of bacteria and their human host. Herein, we have developed a straightforward chemical synthesis method for rapid preparation of sulfated metabolite standards to obtain mass spectrometric fragmentation pattern and retention time information. We report the preparation of 38 O-sulfated alcohols and phenols for the determination of their MS/MS fragmentation pattern and chromatographic properties. Many of these metabolites are regioisomers that cannot be distinguished solely by their fragmentation pattern. We demonstrate that the versatility of this method is comparable to standard chemical synthesis. This comprehensive metabolite library can be applied for co-injection experiments to validate metabolites in different human sample types to explore microbiota-host co-metabolism, xenobiotic, and diet metabolism.

show abstract

Section: Resultsmentioning

confidence: 99%

Rapid Preparation of a Large Sulfated Metabolite Library for Structure Validation in Human Samples

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The underlying factors could be ambiguities in the candidate sets that can be only be resolved by RT or molecular features that cannot be predicted by MS. Stereochemistry is an obvious factor, but annotations of stereochemistry are not always provided for the RT databases limiting the use of this information for training better retention order prediction models. Thus improved modelling of stereochemistry features is an important open problem ( Witting and Böcker, 2020 ). A further research direction could be to include the LC system’s configuration, e.g.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification

et al. 2020

View full text Add to dashboard Cite

Motivation Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectra (MS2). Results We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS2 data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features have MS2 measurements available besides MS1. Availability and implementation Software and data is freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration.

show abstract

“…The underlying factors could be ambiguities in the candidate sets that can be only be resolved by RT or molecular features that cannot be predicted by MS. Stereo-chemistry is an obvious factor, but reliable annotations of stereo-chemistry are generally missing from RT databases and thus cannot currently be used for training better retention order prediction models. Thus improved modelling of stereo-chemistry features is an important open problem (Witting and Böcker, 2020). A further research direction could be to include the liquid chromatography (LC) system's configuration, e.g.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification

Bach

Rogers

Williamson

et al. 2020

Preprint

View full text Add to dashboard Cite

Motivation: Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve the MS based identifications. Results: We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining tandem mass spectrometry data (MS2) and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features have MS2 measurements available besides MS1.

show abstract

Current status of retention time prediction in metabolite identification

Cited by 83 publications

References 47 publications

Rapid Preparation of a Large Sulfated Metabolite Library for Structure Validation in Human Samples

Rapid Preparation of a Large Sulfated Metabolite Library for Structure Validation in Human Samples

Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification

Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification

Contact Info

Product

Resources

About