机器学习辅助下的氨酶催化对映选择性预测以及提高对映选择性的合理变体设计

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Zi-Lin Li, Shuxin Pei, Ziying Chen, Teng-Yu Huang, Xu-Dong Wang, Lin Shen, Xuebo Chen, Qi-Qiang Wang, De-Xian Wang, Yu-Fei Ao
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引用次数: 0

摘要

生物催化是合成手性药物和精细化学品的一种极具吸引力的方法,但评估和/或提高生物催化剂对目标底物的对映选择性往往需要耗费大量的时间和资源。尽管机器学习已被用于揭示蛋白质序列与生物催化对映体选择性之间的潜在关系,但底物适配空间的建立通常被化学家所忽视,这仍然是一个挑战。我们利用以前工作中收集的 240 个数据集,采用化学和几何描述符,建立了随机森林分类模型,用于预测酰胺酶对新底物的对映选择性。我们在这些模型的基础上进一步提出了一种启发式策略,通过这种策略可以有效地进行合理的蛋白质工程,合成ee值更高的手性化合物,与野生型酰胺酶相比,优化变体的E值提高了53倍。这种数据驱动的方法有望拓宽机器学习在生物催化研究中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity

Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.

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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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