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深度学习驱动的图书馆设计为De Novo发现生物活性类的发现

Jun Shi Chang¹, Alexander A Vinogradov¹, Yue Zhang¹

¹Department of Chemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.

ACS central science

|November 30, 2023

概括

我们开发了一个深度学习模型来设计mRNA显示库,用于设计核糖体合成和翻译后修饰 (RiPPs). 这种方法很快发现了针对IRAK4激酶和TLR10的强大类酸连接体,具有潜在的治疗应用.

科学领域:

生物化学生物化学
合成生物学合成生物学
计算生物学计算生物学

背景情况:

由于广泛的酶基质耐受性,核糖体合成和翻译后修饰 (RiPPs) 为工程提供了多功能平台.
精确地定义RiPP酶基质特异性是具有挑战性的,并使多种组合图书馆的设计变得复杂.
在RiPP前体库中优化基质适应性对于成功的工程工作至关重要.

研究的目的:

为RiPP工程开发一个深度学习模型,以简化mRNA显示库的设计.
为了利用体外 thiopeptide 生物合成平台进行基质适应性分析.
发现具有设计生物活动和良好的药理性质的新型伪自然RiPPs.

主要方法:

采用了一个深度学习模型,该模型以mRNA显示为基础,对五种酶 thiopeptide 途径的基质健身概况进行训练.
设计并使用优化的mRNA显示库来对IRAK4激酶和TLR10.0进行亲和性选择.
验证了发现的 thiopeptide 配体用于目标结合,酶抑制,细胞内化和信号调制.

主要成果:

开发了一种准确的深度学习模型,用于预测RiPP生物合成中的基质适应性.
对IRAK4激酶 (K_D高达1.3nM) 和TLR10 (K_D高达300nM) 进行鉴定,发现了强效的硫联体.
证明IRAK4向化合物抑制激酶活性,内化到细胞中,并抑制NF-kB信号传递.

结论:

开发的深度学习方法有效地简化了伪自然RiPP的发现.
这种方法可以设计具有 de novo 生物活性和可取药理学特征的 RiPP.
这些发现为产生基于RiPP支架的新型治疗剂提供了一个强大的策略.

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