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.

近年来，学界已投入大量精力开展复杂蛋白质组中的药物靶点与结合位点识别研究，该领域在现代药物研发中具有举足轻重的地位。本研究中，我们开发了一种稳健的赖氨酸反应性谱分析方法，可在蛋白质组层面系统探究药物结合靶点及其结合位点。该方法的核心原理为：当药物与靶蛋白的特定区域结合时，该区域内的赖氨酸残基反应性会发生改变，此类变化可通过可富集型赖氨酸反应性探针进行检测。将该方法与数据非依赖采集（data-independent acquisition, DIA）技术联用时，我们成功从K562细胞裂解液中鉴定出三种模型药物——格尔德霉素（geldanamycin）、星形孢菌素（staurosporine）与达沙替尼（dasatinib）的已知靶蛋白及其对应结合位点。此外，在星形孢菌素的筛选过程中，本方法还揭示了部分靶蛋白由药物诱导产生的构象变化。通过星形孢菌素与达沙替尼的筛选实验测得，本方法的筛选灵敏度可与热蛋白质组分析（thermal proteome profiling, TPP）或基于机器学习的有限蛋白水解技术（LiP-Quant）相媲美。总体而言，在K562细胞裂解液中，我们分别为星形孢菌素与达沙替尼鉴定出21个和4个激酶靶点，其中包含腺苷5'-三磷酸（adenosine 5′-triphosphate, ATP）结合靶点。我们发现，通过TPP、LiP-Quant与本方法分别鉴定得到的靶蛋白具有互补性，这凸显出开发能够探测蛋白质不同特性的新方法，在药物靶标解析工作中的重要价值。我们还展望了本方法在蛋白质组范围内探测多种涉及赖氨酸反应性变化的事件中的进一步应用前景。