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.

中文翻译：

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含 WD40 重复序列蛋白的靶标类别配体评价


以靶标类别为重点的药物发现在药物研究中有着良好的记录，但公共领域数据表明，蛋白质家族的许多成员仍未配体。在这里，我们提出了一种系统的方法，以扩大 WD40 重复序列 （WDR） 蛋白家族小分子配体的发现和表征。我们开发了一套全面的蛋白质生产、晶体学以及生物物理、生化和细胞检测方案。使用 DNA 编码的化学库选择，然后进行机器学习 （DEL-ML） 来预测虚拟库中的配体的试点苗头化合物发现活动为筛选的 16 个 WDR 结构域中的 7 个产生了同类首创的药物样配体，从而证明了 WDR 更广泛的配体性。本研究建立了评估蛋白质家族范围配体的模板，并提供了 WDR 蛋白质生化和化学工具、知识和方案的广泛资源，以发现这一高度疾病相关但未充分探索的靶标类别的潜在治疗方法。