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用于离子淌度谱-质谱联用的计算工具和算法。

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

机构信息

Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA.

Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.

出版信息

Proteomics. 2024 Jun;24(12-13):e2200436. doi: 10.1002/pmic.202200436. Epub 2024 Mar 4.

Abstract

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

摘要

离子淌度质谱联用技术(IMS-MS 或 IM-MS)是一种强大的分析技术,它将 IM 的气相分离能力与 MS 的识别和定量能力相结合。IM-MS 可以区分具有相同质量但不同结构的分子(例如,异构体、同量异位素、分子类别和污染物离子)。这种分析技术的重要性反映在各种领域中,分子特征分析应用的数量呈阶段性增加,从基于 MS 的各种组学(蛋白质组学、代谢组学、脂质组学等)到聚糖、有机物、蛋白质和大分子复合物的结构特征分析。随着 IM-MS 的应用越来越广泛,人们迫切需要有效的、易于使用的计算工具。本文概述了最新的免费和开源软件工具,这些工具专门针对从 IM-MS 仪器中获得的数据进行分析和解释。本文列出了这些工具,并概述了它们的主要算法方法,同时突出了不同领域的代表性应用。最后,讨论了当前的局限性和预期的改进。

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