基于转录调控蛋白的生物传感器的智能设计构建  

Intelligent design of transcription factor-based biosensors

作　　者：梁朝宁[1] 向腊 唐双焱[1] LIANG Chaoning;XIANG La;TANG Shuangyan(Department of Microbial Physiological&Metabolic Engineering,State Key Laboratory of Microbial Diversity and Innovative Utilization,Institute of Microbiology,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[1]中国科学院微生物研究所微生物多样性与资源创新利用全国重点实验室微生物生理与代谢工程研究室,北京100101

出　　处：《生物工程学报》2025年第3期1011-1022,共12页Chinese Journal of Biotechnology

基　　金：国家重点研发计划(2021YFC2103901);中国科学院科研仪器设备研制项目(YJKYYQ20210032)。

摘　　要：基于转录调控蛋白的生物传感器已在代谢工程、合成生物学、代谢物监测等领域发挥重要作用。该工具具有高度模块化、正交性、易于构建及操作等优良特性,但在实际应用中,天然调控蛋白对目标化合物的响应仍然存在响应程度较低、特异性不符合需求等缺陷。本文综述了近年来在计算机模拟和人工智能技术辅助下,采用蛋白质工程手段,智能设计、改造转录调控蛋白以提升其工作性能所取得的研究进展。主要包括利用蛋白质结构预测、配体结合模拟等优化改造策略快速获取目标突变体,或借助对突变体数据分析并经机器学习等构建数学模型,预测转录调控蛋白突变响应效果等。相较于传统方式,计算机模拟和人工智能辅助技术可实现对生物元件更精准快捷地设计构建,将推动新型生物传感器的创制研发,更好地满足实际应用的需求。Transcription factor(TF)-based biosensors have been widely applied in metabolic engineering,synthetic biology,metabolites monitoring,etc.These biosensors are praised for the high orthogonality,modularity,and operability.However,most natural TFs with weak responses and low specificity still demand optimization for desired performance in applications.Herein,we comprehensively summarize the recent advances in the engineering and optimization of TF-based biosensors with the assistance of computational simulation and artificial intelligence.This review includes the regulatory protein engineering aided by protein structure prediction and ligand binding simulation and the regulatory protein responses predicted by a mathematical model obtained from machine learning of mutagenesis data.In comparison with conventional tools,computational simulation and artificial intelligence enable more accurate and rapid design and construction of biosensors.Thus,these technologies will greatly promote the development of novel biosensors for applications.

关 键 词：转录调控蛋白 生物传感器 计算模拟 蛋白质工程 机器学习 

分 类 号：Q81[生物学—生物工程]