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通过mRNA显示启用机器学习模型评估循环脱水酶LyndD的基质范围

Emma G Steude^1,2, Henry Dieckhaus^1,3, Jarrett M Pelton^1,2

¹Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.

|June 10, 2025

概括

研究人员研究了LynD酶.

科学领域:

生物化学生物化学
酶学是一种酶学.
自然产品的合成自然产品的合成

背景情况:

YcaO酶将阿佐林异循环安装到中,增强生物活性.
亚烯成分有助于自然产品的结构刚性和强大的生物活性.
酶基质散乱性为合成新型基于的抑制剂提供了潜力.

研究的目的:

使用mRNA显示器调查YcaO循环脱水酶,Lynd的基质杂交性.
为了确定LyndD的基质加工和耐受性的趋势.
开发一个LyndD活动的预测模型,并了解酶进化.

主要方法:

利用mRNA显示,一个高吞吐量库技术.
测试了大量的潜在LynD基板库,以确定活动趋势.
采用深度学习来建模基质处理和表观相互作用.

主要成果:

确定了特定的氨基酸偏好和对LynD修改不利的残留物.
发现带电残留物和多个相邻的异环化是不利的.
开发了一个深度学习模型,准确预测LynD基底处理.

结论:

LynD表现出特定的基质偏好,不利于带电残留物和多重异环化.
一个深度学习模型可以预测和解释LynD的基修饰.
了解YcaO基质范围有助于设计新疗法和探针.

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