机器学习可以预测Pyrrolysyl-tRNA合成酶基质的特异性
Qunfeng Zhang1, Wenlong Zheng2, Zhongdi Song3
1Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China.
ACS synthetic biology
|July 24, 2023
概括
机器学习模型准确地预测了pyrrolysyl-tRNA合成酶 (PylRS) 基质特异性,使得蛋白质工程的新非正规氨基酸 (NCAAs) 的发现成为可能.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 了解酶基质范围对于生物化学路径阐明和酶应用扩展至关重要.
- 解-tRNA合成酶 (PylRS) 对于将非正规氨基酸 (NCAAs) 纳入蛋白质至关重要,但需要对新基质进行广泛的选择.
- 需要一种杂乱的氨基-tRNA合成酶来扩大NCAAs的遗传结合.
研究的目的:
- 开发机器学习模型来预测PylRS基质对新型NCAAs的特异性.
- 为了促进新NCAAs的识别,这些NCAAs可以使用PylRS突变体被纳入蛋白质中.
- 建立一个框架来预测其他PylRS变体中的基质特异性.
主要方法:
- 训练有素的机器学习模型使用来自三个PylRS突变 (IFRS,BtaRS,MFRS) 和95个NCAAs的285个酶基质对.
- 在1474个NCAAs的虚拟选中使用了BaggingTree (BT) 模型.
- 实验验证的预测基质,测试27个NCAAs与PylRS突变物的活性.
主要成果:
- 该BT模型预测156个NCAA被至少一个PylRS突变体接受.
- 实验验证证证实了24个预测阳性基质中的20个的活性,其中包括11个新的NCAAs.
- BT模型实现了0.69的三类和0.86的二元分类准确度.
结论:
- 开发的机器学习方法成功地预测了新型NCAAs的PylRS基底特异性.
- 这项研究显著扩大了三种PylRS变体的已知基质范围.
- 这些发现为加速在蛋白质工程中发现和应用NCAAs提供了有价值的工具.
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