Ene reductases (EREDs) are enzymes that catalyze the asymmetric reduction of C═C bonds. EREDs are potentially useful in the large-scale synthesis of pharmaceutical compounds, but their application as biocatalysts is limited because they are often unstable under process conditions. Previous work addressed this limitation by identifying stabilized EREDs with ancestral sequence reconstruction (ASR), a bioinformatic method that predicts evolutionary ancestors based on a set of homologous sequences. In this work, we sought to apply ASR to design enzyme libraries and leverage machine learning to predict the most stable library variants. We generated an ERED library that targeted residues based on uncertainty in the ASR prediction. Screening data from a portion of the library were used to build a machine learning model that could accurately predict variants with improved thermostability. The most stabilized enzyme outperformed the wild-type and ancestral parent enzymes under process-like conditions with a panel of substrates. We envision that the combination of ASR and machine learning could be generally applied to other classes of enzymes, facilitating the development of high-quality industrial biocatalysts.

中文翻译：

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祖先序列重建与机器学习相结合：烯还原酶热稳定产生具有改进反应性特征的酶


烯还原酶 （ERED） 是催化 C═C 键不对称还原的酶。ERED 在药物化合物的大规模合成中可能有用，但它们作为生物催化剂的应用受到限制，因为它们在工艺条件下通常不稳定。以前的工作通过使用祖先序列重建 （ASR） 识别稳定的 ERED 来解决这一限制，ASR 是一种基于一组同源序列预测进化祖先的生物信息学方法。在这项工作中，我们试图应用 ASR 来设计酶库，并利用机器学习来预测最稳定的文库变体。我们生成了一个 ERED 库，该库根据 ASR 预测中的不确定性靶向残基。来自文库一部分的筛选数据用于构建机器学习模型，该模型可以准确预测具有更高热稳定性的变体。最稳定的酶在具有一组底物的类似过程条件下优于野生型和祖先亲本酶。我们设想 ASR 和机器学习的结合可以普遍应用于其他类别的酶，促进高质量工业生物催化剂的开发。