Machine learning-aided design and screening of an emergent protein function in synthetic cells

• 全文链接

机器学习辅助设计和筛选合成细胞中的突现蛋白功能

关键词：

来源：

Nature Communications

全文链接：

//agri.nais.net.cn/topic/downloadFile/cc40a44b-f42c-4a3e-9c6f-f9c0f218bad5

来源地址：

https://www.nature.com/articles/s41467-024-46203-0

资源所属：

饲料用酶工程

类型：

学术文献

语种：

中文

原文发布日期：

2024-03-05

摘要：

Recently, utilization of Machine Learning (ML) has led to astonishing progress in computational protein design, bringing into reach the targeted engineering of proteins for industrial and biomedical applications. However, the design of proteins for emergent functions of core relevance to cells, such as the ability to spatiotemporally self-organize and thereby structure the cellular space, is still extremely challenging. While on the generative side conditional generative models and multi-state design are on the rise, for emergent functions there is a lack of tailored screening methods as typically needed in a protein design project, both computational and experimental. Here we describe a proof-of-principle of how such screening, in silico and in vitro, can be achieved for ML-generated variants of a protein that forms intracellular spatiotemporal patterns. For computational screening we use a structure-based divide-and-conquer approach to find the most promising candidates, while for the subsequent in vitro screening we use synthetic cell-mimics as established by Bottom-Up Synthetic Biology. We then show that the best screened candidate can indeed completely substitute the wildtype gene in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.