EnzyKR:一种具有性意识的深度学习模型,用于预测酶催化动态分辨率的结果
Xinchun Ran1, Yaoyukun Jiang1, Qianzhen Shao1
1Department of Chemistry, Vanderbilt University Nashville Tennessee 37235 USA zhongyue.yang@vanderbilt.edu +1-343-9849.
Chemical science
|November 16, 2023
概括
一个新的深度学习框架,EnzyKR,自动化了用于立体选择生物催化物的酶选择. 它准确地预测了酶催化动态分辨率中的反体结果,超过了现有的方法.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 计算化学和化学信息学
- 在化学科学中的机器学习.
背景情况:
- 酶催化动态分辨率对于分离反体至关重要,但缺乏预测计算工具.
- 现有的模型通常依赖于有限的数据或更简单的表示,阻碍了精确的酶支架选择.
- 开发先进的计算方法对于提高立体选择性生物催化剂的效率至关重要.
研究的目的:
- 开发和验证EnzyKR,这是一个深度学习框架,用于预测动态分辨率中的酶性能.
- 为了自动选择适合用于立体选择性生物催化物的化剂.
- 改进在酶反应中预测反体过量比率的方法.
主要方法:
- 在204个基质-酶复合体上使用分类器-回归器架构训练了一个深度学习框架 (EnzyKR).
- 采用基于结构的编码策略来表示酶和基质之间的奇拉相互作用.
- 验证了EnzyKR在预测各种酶催化反应的激活自由能量和反体过剩比率方面的性能.
主要成果:
- 为了预测激活的自由能量,EnzyKR获得了0.72的皮尔森相关系数 (R) 和0.72的斯皮尔曼R.
- 该框架在预测酶体过量比率方面表现强,在64%的测试案例中表现优于DLKcat预测器.
- EnzyKR成功地预测了在28个测试的水解动力学分辨反应中的18个中受青的反体.
结论:
- EnzyKR代表了生物催化剂中酶选择计算工具的重大进步.
- 该框架提供了一种可靠的方法,用于预测酶催化动态分辨率中的酶体结果.
- EnzyKR有可能加速发现和优化用于奇拉合成的酶.
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