近日，中国科学院遗传与发育生物学研究所高彩霞团队的最新研究提出了整合结构和进化约束的反折叠模型推进蛋白质进化。这一研究成果于2025年7月7日发表在国际顶尖学术期刊《细胞》上。

研究小组提出了基于人工智能的蛋白质工程约束（AiCE），这是一种促进有效的蛋白质进化主题化通用蛋白质逆折叠模型的方法，减少了对人类启发式和特定任务模型的依赖。通过从反折叠模型中采样序列并整合结构和进化约束，AiCE识别出高适应度的单突变和多突变。课题组将AiCE应用于8个蛋白质工程任务，包括脱氨酶、核定位序列、核酸酶和逆转录酶，涵盖了从几十个到10个残基的蛋白质，成功率为11%-88%。该团队还开发了用于精准医学和农业的碱基编辑器，包括enABE8e （5-bp窗口），enSdd6-CBE（保真度提高1.3倍）和enDdd1-DdCBE（线粒体活性提高14.3倍）。这些结果表明，AiCE是一种通用的、热友好的突变设计方法，在效率、可扩展性和通用性方面优于传统方法。

据悉，蛋白质工程可以通过迭代的序列变化实现人工蛋白质进化，但目前的方法往往存在成功率低和成本效益有限的问题。

附：英文原文

Title: Advancing protein evolution with inverse folding models integrating structural and evolutionary constraints

Author: Hongyuan Fei, Yunjia Li, Yijing Liu, Jingjing Wei, Aojie Chen, Caixia Gao

Issue&Volume: 2025-07-07

Abstract: Protein engineering enables artificial protein evolution through iterative sequence changes, but current methods often suffer from low success rates and limited cost effectiveness. Here, we present AI-informed constraints for protein engineering (AiCE), an approach that facilitates efficient protein evolution using generic protein inverse folding models, reducing dependence on human heuristics and task-specific models. By sampling sequences from inverse folding models and integrating structural and evolutionary constraints, AiCE identifies high-fitness single and multi-mutations. We applied AiCE to eight protein engineering tasks, including deaminases, a nuclear localization sequence, nucleases, and a reverse transcriptase, spanning proteins from tens to thousands of residues, with success rates of 11%–88%. We also developed base editors for precision medicine and agriculture, including enABE8e (5-bp window), enSdd6-CBE (1.3-fold improved fidelity), and enDdd1-DdCBE (up to 14.3-fold enhanced mitochondrial activity). These results demonstrate that AiCE is a versatile, user-friendly mutation-design method that outperforms conventional approaches in efficiency, scalability, and generalizability.

DOI: 10.1016/j.cell.2025.06.014

Source: https://www.cell.com/cell/abstract/S0092-8674(25)00680-4