Dysregulation of histone methyl transferase nuclear receptor-binding SET domain 2 (NSD2) has been implicated in several hematological and solid malignancies. NSD2 is a large multidomain protein that carries histone writing and histone reading functions. To date, identifying inhibitors of the enzymatic activity of NSD2 has proven challenging in terms of potency and SET domain selectivity. Inhibition of the NSD2-PWWP1 domain using small molecules has been considered as an alternative approach to reduce NSD2-unregulated activity. In this article, we present novel computational chemistry approaches, encompassing free energy perturbation coupled to machine learning (FEP/ML) models as well as virtual screening (VS) activities, to identify high-affinity NSD2 PWWP1 binders. Through these activities, we have identified the most potent NSD2-PWWP1 binder reported so far in the literature: compound 34 (pIC₅₀ = 8.2). The compounds identified herein represent useful tools for studying the role of PWWP1 domains for inhibition of human NSD2.

中文翻译：

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使用基于结构的设计和计算方法鉴定新型有效 NSD2-PWWP1 配体

组蛋白甲基转移酶核受体结合 SET 结构域 2 (NSD2) 的失调与多种血液和实体恶性肿瘤有关。 NSD2 是一种大型多结构域蛋白，具有组蛋白写入和组蛋白读取功能。迄今为止，鉴定 NSD2 酶活性抑制剂在效力和 SET 结构域选择性方面具有挑战性。使用小分子抑制 NSD2-PWWP1 结构域被认为是减少 NSD2 不受调控的活性的替代方法。在本文中，我们提出了新颖的计算化学方法，包括与机器学习 (FEP/ML) 模型耦合的自由能扰动以及虚拟筛选 (VS) 活动，以识别高亲和力 NSD2 PWWP1 结合物。通过这些活动，我们确定了迄今为止文献中报道的最有效的 NSD2-PWWP1 结合剂：化合物34 (pIC ₅₀ = 8.2)。本文鉴定的化合物代表了研究 PWWP1 结构域抑制人 NSD2 的作用的有用工具。