核心要点
- 问题/背景
- 这篇 Nature Communications 论文面向一个典型药物发现难点：intrinsically disordered AR-NTD 缺少稳定结构，传统结构药物设计很难直接定位可成药构象。
- 方法/机制
- 作者提出 machine-learning-based enhanced sampling workflow，用于探索 AR-NTD 构象空间、识别可靶向构象，并建立可迁移的 ligand binding 建模。
- 结果/证据
- 它的价值不只是做一个预测器，而是把增强采样、构象发现和配体结合建模组织成 rational drug discovery workflow，服务于无序蛋白靶点。
- 收录价值
- 收录价值在于它符合本库 AI x biopharma 标准：AI/ML 直接改变难靶点的构象采样与药物设计流程，而不是只做窄属性预测。
原始摘要与中文对照
中文对照翻译
靶向雄激素受体(AR)的内在无序N端结构域(AR-NTD)是克服前列腺癌耐药性的一种有前景的策略。然而，其固有的缺乏稳定三级结构和高度动态的构象集合对合理药物设计构成了巨大挑战。本研究引入了一种集成的计算工作流程，该流程结合了增强采样技术和机器学习集体变量，以识别AR-NTD的可成药构象并阐明其调节剂EPI-002的结合机制。我们表征了Tau-5区域的九个亚稳态，并揭示配体识别是由π–π堆叠和结构化水介导的氢键驱动的。利用这些见解，我们基于已识别的可成药构象进行了基于结构的虚拟筛选，并识别出K53，这是一种合理设计的AR-NTD拮抗剂，其在恩扎卢胺耐药的前列腺癌细胞中表现出强大的抗增殖活性。K53直接结合AR-NTD，抑制AR转录活性，并对癌细胞表现出高选择性。这项工作为靶向内在无序蛋白提供了一个合理设计范式，并为耐药性前列腺癌提供了一个治疗候选物。
原始摘要
Targeting the intrinsically disordered N-terminal domain of the androgen receptor (AR-NTD) represents a promising strategy to overcome resistance in prostate cancer. However, its inherent lack of a stable tertiary structure and highly dynamic conformational ensemble pose formidable challenges for rational drug design. This study introduces an integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002. We characterize nine metastable states of the Tau-5 region and reveal that ligand recognition is driven by π–π stacking and structured water-mediated hydrogen bonds. Leveraging these insights, we perform structure-based virtual screening based on the identified druggable conformations and identify K53, a rationally designed AR-NTD antagonist, which exhibits potent anti-proliferative activity in enzalutamide-resistant prostate cancer cells. K53 directly binds the AR-NTD, suppresses AR transcriptional activity, and demonstrates high selectivity for cancer cells. This work provides a rational design paradigm for targeting intrinsically disordered proteins and offers a therapeutic candidate for resistant prostate cancer.