.png){kind=icon}
返回
Protein engineering using variational free energy approximation
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-024-54814-w.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Engineering proteins is a challenging task requiring the exploration of a vast design space. Traditionally, this is achieved using Directed Evolution (DE), which is a laborious process. Generative deep learning, instead, can learn biological features of functional proteins from sequence and structural datasets and return novel variants. However, most models do not generate thermodynamically stable proteins, thus leading to many non-functional variants. Here we propose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which generates stable and functional variants by learning the sequence and thermodynamic landscape of a protein. We evaluate PREVENT by designing 40 variants of the conditionally essential E. coli phosphotransferase N-acetyl-L-glutamate kinase (EcNAGK). We find 85% of the variants to be functional, with 55% of them showing similar growth rate compared to the wildtype enzyme, despite harbouring up to 9 mutations. Our results support a new approach that can significantly accelerate protein engineering. Generative deep learning models can generate novel proteins, but these are often not functional due to thermodynamic instability. Here, authors present a method to learn both the sequence and thermodynamic landscape of a protein and generate new functional variants.
Keyword:
ESCHERICHIA-COLI
DESIGN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.3W
被引数:
91.2W
机构
引用论文
.png){kind=icon}
One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products使用PCR产物一步法灭活大肠杆菌K-12中的染色体基因OAAI
OAAI分享收藏分享收藏
Research on the Efficiency of Wireless Power Transfer System Based on Multi-Auxiliary Transmitting Coils基于多辅助发射线圈的无线电能传输系统效率研究
PREAI
分享收藏
查看更多内容