德国海德堡大学Dominik Niopek小组的一项最新研究显示，通过计算机预测结构域插入位点，实现变构蛋白开关的合理工程。相关论文发表在2025年8月4日出版的《自然—方法学》杂志上。

研究小组提出了ProDomino，这是一个机器学习管道，用于合理化结构域重组，该管道在来自自然发生的结构域内插入事件的半合成蛋白质序列数据集上进行训练。ProDomino可以自动识别与生物技术相关的蛋白质中的结构域插入位点，该团队在大肠杆菌和人类细胞中进行了实验验证。最后，课题组研究人员将光和化学调节的受体结构域作为插入物，并展示了快速，模型引导的高效，单组分光和化学发生蛋白开关的创建。其中包括用于人类细胞诱导基因组工程的新型CRISPR-Cas9和-Cas12a变体。他们的工作使一次性结构域插入工程成为可能，并大大加快了去甲基化变构蛋白的设计。

据了解，结构域插入工程是一种强大的方法，可以将不同的生物功能并置，从而产生具有新自然活性的蛋白质。一个突出的例子是可切换的蛋白质变体，通过受体结构域插入到效应蛋白中产生。然而，确定合适的变构位点进行结构域插入通常需要广泛的筛选和优化。

附：英文原文

Title: Rational engineering of allosteric protein switches by in silico prediction of domain insertion sites

Author: Wolf, Benedict, Shehu, Pegi, Brenker, Luca, von Bachmann, Anna-Lisa, Kroell, Ann-Sophie, Southern, Nicholas, Holderbach, Stefan, Eigenmann, Joshua, Aschenbrenner, Sabine, Mathony, Jan, Niopek, Dominik

Issue&Volume: 2025-08-04

Abstract: Domain insertion engineering is a powerful approach to juxtapose otherwise separate biological functions, resulting in proteins with new-to-nature activities. A prominent example are switchable protein variants, created by receptor domain insertion into effector proteins. Identifying suitable, allosteric sites for domain insertion, however, typically requires extensive screening and optimization. We present ProDomino, a machine learning pipeline to rationalize domain recombination, trained on a semisynthetic protein sequence dataset derived from naturally occurring intradomain insertion events. ProDomino robustly identifies domain insertion sites in proteins of biotechnological relevance, which we experimentally validated in Escherichia coli and human cells. Finally, we used light- and chemically regulated receptor domains as inserts and demonstrate the rapid, model-guided creation of potent, single-component opto- and chemogenetic protein switches. These include novel CRISPR–Cas9 and –Cas12a variants for inducible genome engineering in human cells. Our work enables one-shot domain insertion engineering and substantially accelerates the design of customized allosteric proteins.

DOI: 10.1038/s41592-025-02741-z

Source: https://www.nature.com/articles/s41592-025-02741-z