利用人工智能预测配体-受体相互作用,评估通过BDNF-TrkB途径作用的神经保护剂

Jing Zhu, Jun Zou, Fei Li, Yuanxu Gao, Lijun Wang, Yi Sun, Jie Zhu, Xiaomeng Zhang, Kanmin Xue, Gen Li, Nga M. Cheng, Juan Guo, Xiulan Zhang, Kang Zhang
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引用次数: 0

摘要

青光眼是全球不可逆失明的主要原因,与视网膜神经节细胞(RGC)死亡有关。脑源性神经营养因子(BDNF)是一种有效的神经营养因子,通过其受体原肌球蛋白受体激酶B (TrkB)编码NTRK2来促进神经元存活。我们目前对BDNF通路的作用机制和治疗潜力的理解受到其与TrkB在原子分辨率上相互作用的缺乏知识的限制。我们开发了一个人工智能(AI)模型来预测BDNF和TrkB的三维蛋白质结构,以及它们之间的相互作用。AI模型进一步应用于比较模拟BDNF-TrkB相互作用的小分子药物,从而确定7,8-二羟黄酮(DHF)是TrkB的激动剂。我们在体内急性青光眼模型中验证了DHF的神经保护作用,在该模型中,眼内应用DHF可以预防急性眼压升高引起的RGC凋亡,BDNF在较小程度上可以预防。我们的研究结果在原子水平上为BDNF和TrkB之间的配体-受体相互作用提供了人工智能预测,并展示了人工智能药物发现的巨大潜力。
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Evaluation of neuroprotective agents acting via the BDNF–TrkB pathway using AI-enabled predictions of ligand–receptor interactions

Glaucoma is the leading cause of irreversible blindness globally and is associated with retinal ganglion cell (RGC) death. Brain-derived neurotrophic factor (BDNF) is a potent neurotrophin that promotes neuronal survival via its receptor, tropomyosin receptor kinase B (TrkB) encoded by NTRK2. Our current understanding of the mechanism of action and therapeutic potential of the BDNF pathway is limited by the lack of knowledge of its interaction with TrkB at atomic resolution. We developed an artificial intelligence (AI) model to predict the three-dimensional protein structures of BDNF and TrkB, as well as their interaction. The AI model was further applied to compare small-molecule drugs that mimic BDNF–TrkB interaction, leading to the identification of 7,8-dihydroxyflavone (DHF) as an agonist of TrkB. We verified the neuroprotective effects of DHF in an in vivo acute glaucoma model in which RGC apoptosis caused by acute elevation of intraocular pressure was prevented by the intraocular application of DHF and to a lesser extent by BDNF. Our results provide AI-enabled prediction of ligand–receptor interactions between BDNF and TrkB at the atomic level and demonstrate the great potential for AI-enabled drug discovery.

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