通过基于机器学习的增强采样工作流程靶向内在无序的AR-NTD
Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.
摘要(中文译文)
靶向雄激素受体内在无序N端结构域（AR-NTD）是克服前列腺癌耐药的一种有前景的策略。然而，其固有的缺乏稳定三级结构以及高度动态的构象集合，为合理药物设计带来了巨大挑战。本研究引入了一套集成计算工作流程，该流程结合增强采样技术与机器学习集合变量，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，并揭示配体识别由π-π堆积和结构化水介导的氢键驱动。基于这些见解，我们基于所识别的可成药构象进行了基于结构的虚拟筛选，并鉴定出K53——一种合理设计的AR-NTD拮抗剂，其在恩杂鲁胺耐药前列腺癌细胞中表现出强效的抗增殖活性。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合理设计范式，并为耐药前列腺癌提供了一种治疗候选物。
Abstract
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.
如何引用
AMA
Kai Zhu, Huating Wang, Jintu Zhang, Renling Hu, Linlong Jiang, Hui Zhang, et al. Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.. Nature communications. 2026; doi:10.1038/s41467-026-73863-x.
APA
Kai Zhu, Huating Wang, Jintu Zhang, Renling Hu, Linlong Jiang, Hui Zhang, et al (2026). Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.. Nature communications. https://doi.org/10.1038/s41467-026-73863-x
GB/T 7714
Kai Zhu, Huating Wang, Jintu Zhang, Renling Hu, Linlong Jiang, Hui Zhang, et al. Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.[J]. Nature communications, 2026 doi:10.1038/s41467-026-73863-x.
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