End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution

Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein–ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution. The analysis of samples via label-free, data-independent acquisition (DIA) techniques led to significant improvements on a workflow time per sample basis over current standard practices. Experiments utilizing three established PAL ligands with known targets, (+)-JQ-1, lenalidomide, and dasatinib, demonstrated the utility of having the flexibility to design experiments with a myriad of variables. Data revealed that this workflow can enable the confident identification and rank ordering of known and putative targets with outstanding protein signal-to-background enrichment sensitivity. This unified end-to-end throughput strategy for processing and analyzing these complex samples could greatly facilitate efficient drug discovery efforts and open up new opportunities in the chemical proteomics field.光亲和标记（Photoaffinity labeling，PAL）技术已被证实可在生理相关系统中无偏倚地解析蛋白质-配体结合事件。然而，与其他化学蛋白质组学工作流程类似，该类技术仍受限于耗时的样品处理操作与数据采集技术。在此，我们报道一种应对该挑战的方法：创新开发基于羧酸盐磁珠的蛋白质纯化流程，以去除过量小分子污染物，并将其与基于微孔板的蛋白质组学样品处理相结合，形成半自动化解决方案。通过无标记、数据非依赖性采集（data-independent acquisition，DIA）技术对样品进行分析，相较当前标准操作流程，单份样品的工作流程耗时得到显著优化。实验选用三种已得到验证的PAL配体——(+)-JQ-1、来那度胺与达沙替尼，三者均带有已知靶点——验证了该流程可灵活设计包含多种变量的实验。数据分析表明，该工作流程能够可靠鉴定并排序已知靶点与潜在靶点，同时具备出色的蛋白质信号本底富集灵敏度。这种用于复杂样品处理与分析的统一端到端通量策略，可极大助力高效药物研发工作，并为化学蛋白质组学领域开辟全新机遇。