利用扩展、重叠温度梯度的 PISA-DIA 技术改进药物目标解卷积。

IF 3.4 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Proteomics Pub Date : 2024-05-20 DOI:10.1002/pmic.202300644
Samantha J. Emery-Corbin, Jumana M. Yousef, Subash Adhikari, Fransisca Sumardy, Duong Nhu, Mark F. van Delft, Guillaume Lessene, Jerzy Dziekan, Andrew I. Webb, Laura F. Dagley
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引用次数: 0

摘要

热蛋白质组图谱分析(TPP)是一种强大的药物靶点解构工具。最近,与数据无关的采集质谱(DIA-MS)方法已证明能显著改善蛋白质组数据的深度和缺失率,但传统的TPP(又称CEllular Thermal Shift Assay "CETSA")工作流程通常采用依赖于数据依赖性采集(DDA)的多路复用试剂。在本文中,我们介绍了通过无标记 DIA 方法(PISA-DIA)进行蛋白质组整体溶解度改变的新实验设计。我们强调了在使用 DIA-MS 的同时使用多个重叠的热梯度所实现的蛋白质组覆盖率和灵敏度,这最大限度地提高了 PISA 样品合并的效率,并防止了在高熔点温度下蛋白质目标的遗漏。与使用串联质量标记(TMT)进行 DDA-MS 分析相比,我们的扩展 PISA-DIA 设计具有更高的蛋白质组覆盖率。重要的是,我们利用 BCL-xL 的特异性抑制剂 A-1331852 证明了我们的 PISA-DIA 方法在定量和统计方面的严谨性。由于该蛋白靶点的熔融温度较高,我们采用了扩展的多梯度 PISA-DIA 工作流程来鉴定 BCL-xL。我们断言,我们新颖的重叠梯度 PISA-DIA-MS 方法是无偏药物靶标解旋的理想选择,既能跨越较大的温度范围,又能最大限度地减少梯度间的靶标丢失,从而提高了解析新型化合物蛋白质靶标的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient

Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay “CETSA”) workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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