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利用深度神经网络发现小分子衰老细胞清除剂。

Discovering small-molecule senolytics with deep neural networks.

机构信息

Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

出版信息

Nat Aging. 2023 Jun;3(6):734-750. doi: 10.1038/s43587-023-00415-z. Epub 2023 May 4.

DOI:10.1038/s43587-023-00415-z
PMID:37142829
Abstract

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules. Our approach enriched for structurally diverse compounds with senolytic activity; of these, three drug-like compounds selectively target senescent cells across different senescence models, with more favorable medicinal chemistry properties than, and selectivity comparable to, those of a known senolytic, ABT-737. Molecular docking simulations of compound binding to several senolytic protein targets, combined with time-resolved fluorescence energy transfer experiments, indicate that these compounds act in part by inhibiting Bcl-2, a regulator of cellular apoptosis. We tested one compound, BRD-K56819078, in aged mice and found that it significantly decreased senescent cell burden and mRNA expression of senescence-associated genes in the kidneys. Our findings underscore the promise of leveraging deep learning to discover senotherapeutics.

摘要

衰老细胞的积累与衰老、炎症和细胞功能障碍有关。衰老细胞清除药物可以通过选择性地杀死衰老细胞来缓解与年龄相关的共病。在这里,我们在依托泊苷诱导衰老模型中筛选了 2352 种化合物的衰老细胞活性,并训练图神经网络来预测超过 80 万个分子的衰老细胞活性。我们的方法富集了具有衰老细胞活性的结构多样的化合物;其中,三种具有药物样特性的化合物可选择性地靶向不同衰老模型中的衰老细胞,其药物化学性质优于、选择性与已知的衰老细胞清除剂 ABT-737 相当。化合物与几种衰老细胞蛋白靶标结合的分子对接模拟实验,结合时间分辨荧光能量转移实验,表明这些化合物部分通过抑制 Bcl-2 发挥作用,Bcl-2 是细胞凋亡的调节剂。我们在老年小鼠中测试了一种化合物 BRD-K56819078,发现它显著降低了肾脏中衰老细胞的负担和衰老相关基因的 mRNA 表达。我们的研究结果强调了利用深度学习来发现衰老治疗药物的前景。

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Senolytics: Eliminating Senescent Cells and Alleviating Intervertebral Disc Degeneration.衰老细胞裂解剂:清除衰老细胞与缓解椎间盘退变
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Machine learning for antimicrobial peptide identification and design.用于抗菌肽鉴定与设计的机器学习
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