Zhang Xiaolei, Wang Keyun, Wu Sijin, Ruan Chengfei, Li Kejia, Wang Yan, Zhu He, Liu Xiaoyan, Liu Zhen, Li Guohui, Hu Lianghai, Ye Mingliang
CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
Center for Supramolecular Chemical Biology, State Key Laboratory of Supramolecular Structure and Materials, School of Life Sciences, Jilin University Changchun 130012 China
Chem Sci. 2022 Sep 30;13(42):12403-12418. doi: 10.1039/d2sc03326g. eCollection 2022 Nov 2.
Fully understanding the target spaces of drugs is essential for investigating the mechanism of drug action and side effects, as well as for drug discovery and repurposing. In this study, we present an energetics-based approach, termed pH-dependent protein precipitation (pHDPP), to probe the ligand-induced protein stability shift for proteome-wide drug target identification. We demonstrate that pHDPP works for a diverse array of ligands, including a folate derivative, an ATP analog, a CDK inhibitor and an immunosuppressant, enabling highly specific identification of target proteins from total cell lysates. This approach is compared to thermal and solvent-induced denaturation approaches with a pan-kinase inhibitor as the model drug, demonstrating its high sensitivity and high complementarity to other approaches. Dihydroartemisinin (DHA), a dominant derivative of artemisinin to treat malaria, is known to have an extraordinary effect on the treatment of various cancers. However, the anti-tumor mechanisms remain unknown. pHDPP was applied to reveal the target space of DHA and 45 potential target proteins were identified. Pathway analysis indicated that these target proteins were mainly involved in metabolism and apoptosis pathways. Two cancer-related target proteins, ALDH7A1 and HMGB1, were validated by structural simulation and AI-based target prediction methods. And they were further validated to have strong affinity to DHA by using cellular thermal shift assay (CETSA). In summary, pHDPP is a powerful tool to construct the target protein space to reveal the mechanism of drug action and would have broad application in drug discovery studies.
全面了解药物的作用靶点对于研究药物作用机制和副作用以及药物发现和重新利用至关重要。在本研究中，我们提出了一种基于能量学的方法，称为pH依赖性蛋白质沉淀（pHDPP），用于在全蛋白质组范围内识别药物靶点时探测配体诱导的蛋白质稳定性变化。我们证明pHDPP适用于多种配体，包括叶酸衍生物、ATP类似物、CDK抑制剂和免疫抑制剂，能够从全细胞裂解物中高度特异性地识别靶蛋白。以泛激酶抑制剂为模型药物，将该方法与热诱导和溶剂诱导变性方法进行比较，证明了其高灵敏度和与其他方法的高度互补性。双氢青蒿素（DHA）是青蒿素治疗疟疾的主要衍生物，已知对各种癌症的治疗有显著效果。然而，其抗肿瘤机制仍不清楚。应用pHDPP揭示了DHA的作用靶点空间，并鉴定出45种潜在的靶蛋白。通路分析表明，这些靶蛋白主要参与代谢和凋亡通路。通过结构模拟和基于人工智能的靶点预测方法验证了两种与癌症相关的靶蛋白ALDH7A1和HMGB1。并通过细胞热位移分析（CETSA）进一步验证它们与DHA具有很强的亲和力。总之，pHDPP是构建靶蛋白空间以揭示药物作用机制的有力工具，在药物发现研究中将有广泛的应用。
