CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
ACS Chem Biol. 2022 Jan 21;17(1):252-262. doi: 10.1021/acschembio.1c00936. Epub 2022 Jan 6.
Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed after thermal treatment, while the precipitate fractions that also contain abundant information of drug-induced stability shifts are discarded; the sigmoid melting curve fitting strategy used for data processing suffers from discriminations for a part of human proteome with multiple transitions. In this study, a precipitate-supported TPP (PSTPP) assay was presented for unbiased and comprehensive analysis of protein-drug interactions at the proteome level. In PSTPP, only these temperatures where significant precipitation is observed were applied to induce protein denaturation and the complementary information contained in both supernatant fractions and precipitate fractions was used to improve the screening specificity and sensitivity. In addition, a novel image recognition algorithm based on deep learning was developed to recognize the target proteins, which circumvented the problems that exist in the sigmoid curve fitting strategy. PSTPP assay was validated by identifying the known targets of methotrexate, raltitrexed, and SNS-032 with good performance. Using a promiscuous kinase inhibitor, staurosporine, we delineated 99 kinase targets with a specificity up to 83% in K562 cell lysates, which represented a significant improvement over the existing thermal shift methods. Furthermore, the PSTPP strategy was successfully applied to analyze the binding targets of rapamycin, identifying the well-known targets, FKBP1A, as well as revealing a few other potential targets.
虽然热蛋白质组谱分析（TPP）是一种流行的无修饰方法，可用于药物靶标解析，但一些关键问题仍限制了筛选的灵敏度。在当前的 TPP 工作流程中，仅在热处理后分析可溶部分，而沉淀部分含有丰富的药物诱导稳定性变化信息，但会被丢弃；用于数据处理的 S 形熔化曲线拟合策略在对具有多个转变的部分人类蛋白质组进行区分时存在问题。在这项研究中，提出了一种沉淀支持的 TPP（PSTPP）测定法，用于在蛋白质组水平上进行无偏和全面的蛋白质-药物相互作用分析。在 PSTPP 中，仅应用那些观察到明显沉淀的温度来诱导蛋白质变性，并利用上清部分和沉淀部分中包含的互补信息来提高筛选的特异性和灵敏度。此外，开发了一种基于深度学习的新型图像识别算法来识别目标蛋白，从而避免了 S 形曲线拟合策略中存在的问题。通过识别甲氨蝶呤、雷替曲塞和 SNS-032 的已知靶标，验证了 PSTPP 测定法的良好性能。使用一种混杂的激酶抑制剂，司莫司汀，我们在 K562 细胞裂解物中鉴定出 99 个激酶靶标，特异性高达 83%，这比现有的热位移方法有了显著的改进。此外，PSTPP 策略成功地应用于分析雷帕霉素的结合靶标，鉴定出了已知的靶标 FKBP1A，以及揭示了一些其他潜在的靶标。
