Dataset Information

A Data Set of Ion Mobility Collision Cross Sections and Liquid Chromatography Retention Times from 71 Pyridylaminated N-Linked Oligosaccharides.

ABSTRACT: Determination of the glycan structure is an essential step in understanding structure-function relationships of glycans and glycoconjugates including biopharmaceuticals. Mass spectrometry, because of its high sensitivity and mass resolution, is an excellent means of analyzing glycan structures. We previously proposed a method for rapid and precise identification of N-glycan structures by ultraperformance liquid chromatography-connected ion mobility mass spectrometry (UPLC/IM-MS). To substantiate this methodology, we here examine 71 pyridylaminated (PA-) N-linked oligosaccharides including isomeric pairs. A data set on collision drift times, retention times, and molecular mass was collected for these PA-oligosaccharides. For standardization of the observables, LC retention times were normalized into glucose units (GU) using pyridylaminated α-1,6-linked glucose oligomers as reference, and drift times in IM-MS were converted into collision cross sections (CCS). To evaluate the CCS value of each PA-oligosaccharide, we introduced a CCS index which is defined as a CCS ratio of a target PA-glycan to the putative standard PA-glucose oligomer of the same m/z. We propose a strategy for practical structural analysis of N-linked glycans based on the database of m/z, CCS index, and normalized retention time (GU).

SUBMITTER: Manabe N

PROVIDER: S-EPMC9460764 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Data Set of Ion Mobility Collision Cross Sections and Liquid Chromatography Retention Times from 71 Pyridylaminated N-Linked Oligosaccharides.

Manabe Noriyoshi N Ohno Shiho S Matsumoto Kana K Kawase Taiji T Hirose Kenji K Masuda Katsuyoshi K Yamaguchi Yoshiki Y

Journal of the American Society for Mass Spectrometry 20220823 9

Determination of the glycan structure is an essential step in understanding structure-function relationships of glycans and glycoconjugates including biopharmaceuticals. Mass spectrometry, because of its high sensitivity and mass resolution, is an excellent means of analyzing glycan structures. We previously proposed a method for rapid and precise identification of <i>N</i>-glycan structures by ultraperformance liquid chromatography-connected ion mobility mass spectrometry (UPLC/IM-MS). To subst ...[more]

PMID: 35997275

Similar Datasets

Project description:In untargeted metabolomics, structures of small molecules are annotated using liquid chromatography-mass spectrometry by leveraging information from the molecular retention time (RT) in the chromatogram and m/z (formerly called ''mass-to-charge ratio'') in the mass spectrum. However, correct identification of metabolites is challenging due to the vast array of small molecules. Therefore, various in silico tools for mass spectrometry peak alignment and compound prediction have been developed; however, the list of candidate compounds remains extensive. Accurate RT prediction is important to exclude false candidates and facilitate metabolite annotation. Recent advancements in artificial intelligence (AI) have led to significant breakthroughs in the use of deep learning models in various fields. Release of a large RT dataset has mitigated the bottlenecks limiting the application of deep learning models, thereby improving their application in RT prediction tasks. This review lists the databases that can be used to expand training datasets and concerns the issue about molecular representation inconsistencies in datasets. It also discusses the application of AI technology for RT prediction, particularly in the 5 years following the release of the METLIN small molecule RT dataset. This review provides a comprehensive overview of the AI applications used for RT prediction, highlighting the progress and remaining challenges. SCIENTIFIC CONTRIBUTION: This article focuses on the advancements in small molecule retention time prediction in computational metabolomics over the past five years, with a particular emphasis on the application of AI technologies in this field. It reviews the publicly available datasets for small molecule retention time, the molecular representation methods, the AI algorithms applied in recent studies. Furthermore, it discusses the effectiveness of these models in assisting with the annotation of small molecule structures and the challenges that must be addressed to achieve practical applications.

Dataset Information

A Data Set of Ion Mobility Collision Cross Sections and Liquid Chromatography Retention Times from 71 Pyridylaminated N-Linked Oligosaccharides.

Publications

A Data Set of Ion Mobility Collision Cross Sections and Liquid Chromatography Retention Times from 71 Pyridylaminated N-Linked Oligosaccharides.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets