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使用数据非依赖型采集方法实现氢/氘交换质谱数据分析的自动化

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology.

作者信息

Filandr Frantisek, Sarpe Vladimir, Raval Shaunak, Crowder D Alex, Khan Morgan F, Douglas Pauline, Coales Stephen, Viner Rosa, Syed Aleem, Tainer John A, Lees-Miller Susan P, Schriemer David C

机构信息

Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, T2N 4N1, Canada.

Department of Chemistry, University of Calgary, Calgary, AB, T2N 4N1, Canada.

出版信息

Nat Commun. 2024 Mar 11;15(1):2200. doi: 10.1038/s41467-024-46610-3.

Abstract

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS-based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

摘要

我们展示了一种与串联质谱联用的氢/氘交换工作流程(HX-MS),它能够在获取肽前体的同时获取肽片段离子。该方法通过挖掘由碰撞诱导解离(CID)产生的大量氘代片段,实现了HX数据的真正自动校正,从而同时确认肽的身份并验证基于质谱的氘化计算。片段提供的高冗余性支持使用组合策略对氘化计算进行置信度评估。该方法需要大多数质谱平台上都有的数据非依赖型采集(DIA)方法,这使得向HX-MS的转换变得简单直接。重要的是,我们发现HX-DIA能够实现蛋白质组学级别的方法和广泛的应用。通过自动校正节省了大量时间,现在可以对复杂样品进行表征,并且通量更高。我们在直接从哺乳动物细胞中分离出的超大型蛋白激酶DNA-PKcs的药物结合分析中展示了这些优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9007/10928179/1f9fab61d4b6/41467_2024_46610_Fig1_HTML.jpg

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