机器学习训练的可切换蛋白结构域插入设计。

IF 32.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods Pub Date : 2025-08-04 DOI:10.1038/s41592-025-02766-4

Noah Holzleitner, Julian Grünewald

引用次数: 0

摘要

ProDomino是一种机器学习模型，可以仅根据氨基酸序列有效地预测宿主蛋白质中的结构域插入位点。该模型大大加快了功能性多结构域蛋白的设计，如光触发或药物触发的蛋白质开关。

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-trained protein domain insertion for the design of switchable proteins

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Machine learning-trained protein domain insertion for the design of switchable proteins

ProDomino is a machine-learning model that efficiently predicts domain insertion sites in host proteins on the basis of amino acid sequence alone. The model enables the greatly accelerated design of functional multi-domain proteins, such as light-triggered or drug-triggered protein switches.

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来源期刊

Nature Methods 生物-生化研究方法

CiteScore

58.70

自引率

1.70%

发文量

326

审稿时长

1 months

期刊介绍： Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.