含 WD40 重复蛋白的目标类配体性评估。

IF 6.8 1区 医学 Q1 CHEMISTRY, MEDICINAL
Suzanne Ackloo, Fengling Li, Magda Szewczyk, Almagul Seitova, Peter Loppnau, Hong Zeng, Jin Xu, Shabbir Ahmad, Yelena A Arnautova, A J Baghaie, Serap Beldar, Albina Bolotokova, Paolo A Centrella, Irene Chau, Matthew A Clark, John W Cuozzo, Saba Dehghani-Tafti, Jeremy S Disch, Aiping Dong, Antoine Dumas, Jianwen A Feng, Pegah Ghiabi, Elisa Gibson, Justin Gilmer, Brian Goldman, Stuart R Green, Marie-Aude Guié, John P Guilinger, Nathan Harms, Oleksandra Herasymenko, Scott Houliston, Ashley Hutchinson, Steven Kearnes, Anthony D Keefe, Serah W Kimani, Trevor Kramer, Maria Kutera, Haejin A Kwak, Cristina Lento, Yanjun Li, Jenny Liu, Joachim Loup, Raquel A C Machado, Christopher J Mulhern, Sumera Perveen, Germanna L Righetto, Patrick Riley, Suman Shrestha, Eric A Sigel, Madhushika Silva, Michael D Sintchak, Belinda L Slakman, Rhys D Taylor, James Thompson, Wen Torng, Carl Underkoffler, Moritz von Rechenberg, Ryan T Walsh, Ian Watson, Derek J Wilson, Esther Wolf, Manisha Yadav, Aliakbar K Yazdi, Junyi Zhang, Ying Zhang, Vijayaratnam Santhakumar, Aled M Edwards, Dalia Barsyte-Lovejoy, Matthieu Schapira, Peter J Brown, Levon Halabelian, Cheryl H Arrowsmith
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引用次数: 0

摘要

以靶点类别为重点的药物发现在药物研究领域有着良好的记录,但公共领域的数据表明,许多蛋白家族成员仍然没有配体。在这里,我们介绍了一种系统方法,用于扩大 WD40 重复(WDR)蛋白家族小分子配体的发现和表征。我们开发了一整套用于蛋白质生产、晶体学、生物物理、生物化学和细胞检测的方案。通过使用 DNA 编码化学文库选择和机器学习(DEL-ML)从虚拟文库中预测配体,在筛选出的 16 个 WDR 结构域中,有 7 个获得了第一类类药物配体,从而证明了 WDR 具有更广泛的配体可配性。这项研究为评估蛋白家族的广泛配体性建立了一个模板,并提供了广泛的 WDR 蛋白生化和化学工具、知识和规程资源,以发现这一与疾病高度相关但探索不足的靶标类别的潜在疗法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Target Class Ligandability Evaluation of WD40 Repeat-Containing Proteins.

Target class-focused drug discovery has a strong track record in pharmaceutical research, yet public domain data indicate that many members of protein families remain unliganded. Here we present a systematic approach to scale up the discovery and characterization of small molecule ligands for the WD40 repeat (WDR) protein family. We developed a comprehensive suite of protocols for protein production, crystallography, and biophysical, biochemical, and cellular assays. A pilot hit-finding campaign using DNA-encoded chemical library selection followed by machine learning (DEL-ML) to predict ligands from virtual libraries yielded first-in-class, drug-like ligands for 7 of the 16 WDR domains screened, thus demonstrating the broader ligandability of WDRs. This study establishes a template for evaluation of protein family wide ligandability and provides an extensive resource of WDR protein biochemical and chemical tools, knowledge, and protocols to discover potential therapeutics for this highly disease-relevant, but underexplored target class.

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来源期刊
Journal of Medicinal Chemistry
Journal of Medicinal Chemistry 医学-医药化学
CiteScore
4.00
自引率
11.00%
发文量
804
审稿时长
1.9 months
期刊介绍: The Journal of Medicinal Chemistry is a prestigious biweekly peer-reviewed publication that focuses on the multifaceted field of medicinal chemistry. Since its inception in 1959 as the Journal of Medicinal and Pharmaceutical Chemistry, it has evolved to become a cornerstone in the dissemination of research findings related to the design, synthesis, and development of therapeutic agents. The Journal of Medicinal Chemistry is recognized for its significant impact in the scientific community, as evidenced by its 2022 impact factor of 7.3. This metric reflects the journal's influence and the importance of its content in shaping the future of drug discovery and development. The journal serves as a vital resource for chemists, pharmacologists, and other researchers interested in the molecular mechanisms of drug action and the optimization of therapeutic compounds.
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