Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
J Am Chem Soc. 2022 Jan 19;144(2):901-911. doi: 10.1021/jacs.1c11053. Epub 2022 Jan 5.
Activity-based protein profiling (ABPP) has emerged as a powerful and versatile tool to enable annotation of protein functions and discovery of targets of bioactive ligands in complex biological systems. It utilizes chemical probes to covalently label functional sites in proteins so that they can be enriched for mass spectrometry (MS)-based quantitative proteomics analysis. However, the semistochastic nature of data-dependent acquisition and high cost associated with isotopically encoded quantification reagents compromise the power of ABPP in multidimensional analysis and high-throughput screening, when a large number of samples need to be quantified in parallel. Here, we combine the data-independent acquisition (DIA) MS with ABPP to develop an efficient label-free quantitative chemical proteomic method, DIA-ABPP, with good reproducibility and high accuracy for high-throughput quantification. We demonstrated the power of DIA-ABPP for comprehensive profiling of functional cysteineome in three distinct applications, including dose-dependent quantification of cysteines' sensitivity toward a reactive metabolite, screening of ligandable cysteines with a covalent fragment library, and profiling of cysteinome fluctuation in circadian clock cycles. DIA-ABPP will open new opportunities for in-depth and multidimensional profiling of functional proteomes and interactions with bioactive small molecules in complex biological systems.
活性蛋白质组学分析 (ABPP) 已成为一种强大而通用的工具，可用于注释蛋白质功能，并发现生物活性配体在复杂生物系统中的靶标。它利用化学探针共价标记蛋白质中的功能位点，以便通过基于质谱 (MS) 的定量蛋白质组学分析进行富集。然而，数据依赖采集的半随机性质和与同位素编码定量试剂相关的高成本，在需要并行定量大量样本时，限制了 ABPP 在多维分析和高通量筛选中的应用。在这里，我们将数据非依赖采集 (DIA) MS 与 ABPP 相结合，开发了一种高效的无标记定量化学蛋白质组学方法 DIA-ABPP，该方法具有良好的重现性和高通量定量的高精度。我们通过三个不同的应用证明了 DIA-ABPP 对功能半胱氨酸组全面分析的强大功能，包括对反应性代谢物敏感的半胱氨酸的剂量依赖性定量、用共价片段文库筛选可配体的半胱氨酸、以及在生物钟周期中对半胱氨酸组波动的分析。DIA-ABPP 将为深入和多维分析复杂生物系统中功能蛋白质组及其与生物活性小分子的相互作用提供新的机会。
