Proteome-Wide Deconvolution of Drug Targets and Binding Sites by Lysine Reactivity Profiling

Recently,
numerous efforts have been devoted to identifying drug
targets and binding sites in complex proteomes, which is of great
importance in modern drug discovery. In this study, we developed a
robust lysine reactivity profiling method to systematically study
drug-binding targets and binding sites at the proteome level. This
method is based on the principle that binding of a drug to a specific
region of target proteins will change the reactivity of lysine residues
that are located at this region, and these changes can be detected
with an enrichable and lysine reactive probe. Coupled with data-independent
acquisition (DIA), the known target proteins and corresponding binding
sites were successfully revealed from K562 cell lysates for three
model drugs: geldanamycin, staurosporine, and dasatinib. In addition,
the drug-induced conformational changes of certain targets were also
revealed by our method during the screening of staurosporine. The
screening sensitivity of our method revealed from the screening of
stuarosporine and dasatinib was comparable with that of thermal proteome
profiling (TPP) or machine learning-based limited proteolysis (LiP-Quant).
Overall, 21 and 4 kinase targets, including adenosine 5′-triphosphate
(ATP)-binding targets, were identified for staurosporine and dasatinib
in K562 cell lysates, respectively. We found that target proteins
identified by TPP, LiP-Quant, and our method were complementary, emphasizing
that the development of new methods that probe different properties
of proteins is of great importance in drug target deconvolution. We
also envision further applications of our method in proteome-wide
probing multiple events that involve lysine reactivity changes.

近期，众多研究致力于在复杂的蛋白质组中识别药物靶点和结合位点，这对于现代药物发现具有重要意义。本研究中，我们开发了一种稳健的赖氨酸反应性分析技术，以系统性地研究蛋白质组水平上的药物结合靶点和结合位点。该技术基于以下原理：药物与靶蛋白特定区域的结合将改变该区域赖氨酸残基的反应性，而这些变化可以通过一种可富集的赖氨酸反应性探针进行检测。结合数据无关采集（DIA）技术，我们成功从K562细胞裂解物中揭示出三种模型药物：鬼臼毒素、丝裂霉素和白消安的已知靶蛋白及其相应的结合位点。此外，在丝裂霉素的筛选过程中，我们通过该方法还揭示了某些靶蛋白的药物诱导构象变化。我们的方法在筛选丝裂霉素和白消安时的灵敏度与热蛋白质组分析（TPP）或基于机器学习的有限蛋白水解（LiP-Quant）相当。总体而言，在K562细胞裂解物中，我们分别识别出21和4个激酶靶点，包括腺苷三磷酸（ATP）结合靶点。我们发现，通过TPP、LiP-Quant和我们的方法识别的靶蛋白相互补充，这强调了开发探查蛋白质不同特性的新方法对于药物靶点解析的重要性。我们亦展望该方法在蛋白质组水平上探测涉及赖氨酸反应性变化的多种事件的应用前景。