Proteome integral solubility alteration via label-free DIA approach (PISA-DIA), game changer in drug target deconvolution.

IF 3.4 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Proteomics Pub Date : 2024-11-07 DOI:10.1002/pmic.202400147

Zheng Ser, Radoslaw M Sobota

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引用次数: 0

Abstract

Drug protein-target identification in past decades required screening compound libraries against known proteins to determine drugs binding to specific protein. Protein targets used in drug-target screening were selected predominantly used laborious genetic manipulation assays. In 2013, a team led by Pär Nordlund from Karolinska Institutet (Stockholm, Sweden) developed Cellular Thermal Shift Assay (CETSA), a method which, for the first time, enabled the possibility of drug protein-target identification in the complex cellular proteome. High throughput, quantitative mass spectrometry (MS) proteomics appeared as a compatible analytical method of choice to complement CETSA, aka Thermal Protein Profiling assay (TPP). Since the seminal CETSA-MS/ TPP-MS publications, different protein-target deconvolution strategies emerged including Proteome Integral Solubility Alteration (PISA). The work of Emery-Corbin et al. (Proteomics 2024, 2300644), titled Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA), introduces Data-Independent Acquisition (DIA) as a quantification method, opening new avenues in drug target-deconvolution field. Application of DIA for target deconvolution offers attractive alternative to widely used data dependent methodology.

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通过无标记 DIA 方法改变蛋白质组整体溶解度（PISA-DIA），改变药物靶点解旋的游戏规则。

过去几十年中，药物蛋白质靶点鉴定需要针对已知蛋白质筛选化合物库，以确定药物与特定蛋白质的结合情况。用于药物靶点筛选的蛋白质靶点主要是通过费力的基因操作试验筛选出来的。2013年，瑞典斯德哥尔摩卡罗林斯卡医学院的Pär Nordlund领导的团队开发出细胞热转移分析法（CETSA），首次实现了在复杂的细胞蛋白质组中鉴定药物蛋白质靶标的可能性。高通量、定量质谱（MS）蛋白质组学是对 CETSA（又称热蛋白质轮廓分析法（TPP））的补充，是一种兼容的分析方法。自开创性的 CETSA-MS/ TPP-MS 出版以来，出现了不同的蛋白质目标解卷积策略，包括蛋白质组整体溶解度改变（PISA）。Emery-Corbin 等人的研究（Proteomics 2024, 2300644）题为 "通过无标记 DIA 方法进行蛋白质组整体溶解度改变（PISA-DIA）"，引入了数据独立获取（DIA）作为一种定量方法，为药物靶标解卷积领域开辟了新途径。应用 DIA 进行靶标解卷积为广泛使用的数据依赖方法提供了极具吸引力的替代方法。

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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来源期刊

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.