A generalized platform for artificial intelligence-powered autonomous enzyme engineering.

IF 15.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Nature Communications Pub Date : 2025-07-01 DOI:10.1038/s41467-025-61209-y

Nilmani Singh, Stephan Lane, Tianhao Yu, Jingxia Lu, Adrianna Ramos, Haiyang Cui, Huimin Zhao

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引用次数: 0

Abstract

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-fold improvement in substrate preference and 16-fold improvement in ethyltransferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

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人工智能驱动的自主酶工程通用平台。

蛋白质是生命的分子机器，在能源、健康和可持续发展方面有着广泛的应用。然而，具有实际应用所需功能的工程蛋白仍然缓慢、昂贵且依赖于专家。在这里，我们报告了一个普遍适用的自主酶工程平台，该平台将机器学习和大型语言模型与生物铸造自动化相结合，以消除对人工干预、判断和领域专业知识的需求。只需要输入一个蛋白质序列和一种可量化的方法来测量适应度，这个自动化平台可以应用于设计各种蛋白质。为了验证这一概念，我们设计拟南芥卤代甲基转移酶（AtHMT），使底物偏好提高90倍，乙烯基转移酶活性提高16倍，同时开发出一种摩尔氏耶尔森菌植酸酶（YmPhytase）变体，在中性ph下活性提高26倍。这项工作在4周内完成了4轮，每种酶需要构建和表征不到500个变体。这个自主实验平台为从医学和生物技术到可再生能源和可持续化学等不同行业的快速发展铺平了道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Communications Biological Science Disciplines-

CiteScore

24.90

自引率

2.40%

发文量

6928

审稿时长

3.7 months

期刊介绍： Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.