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De novo 蛋白质竞争对手的设计,用于小分子免疫传感

Yosta de Stigter^1,2, Tallie Godschalk^1,2, Maarten Merkx^1,2

¹Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

bioRxiv : the preprint server for biology

|December 25, 2025

概括

深度学习模型可以快速设计用于竞争免疫试验的蛋白质结合剂,取代耗时的化学合成. 这些新型结合剂使得生物传感器中的小分子,如狄戈辛,能够灵敏地检测到它们.

科学领域:

生物化学生物化学
蛋白质工程是指蛋白质工程.
生物传感器技术技术

背景情况:

基于竞争的免疫试验对于检测小分子生物标志物至关重要.
传统的方法需要复杂的合成竞争分子.
新的蛋白质结合剂提供可调节的特性和更容易的整合.

研究的目的:

开发一种用于设计小分子竞争性蛋白质结合剂的快速方法.
为了证明BindCraft管道和in silico选的有效性.
在基于竞争的免疫传感器中创建用于迪戈辛检测的新型结合剂.

主要方法:

利用BindCraft管道进行新的蛋白质结合剂设计,针对抗原结合部位.
在过中使用,以选择与小分子分析物发生绝缘碰撞的结合物.
通过生物发光试验对抗迪戈辛的设计结合剂进行了实验选,并将其集成到基于竞争的免疫传感器中.

主要成果:

在细菌溶解酸中成功设计和识别了10个迪戈辛竞争对手结合剂中的7个.
具有低至中等亲和度 (Kd = 42 nM - 1.1 μM) 的特征性结合剂,表现为被狄戈辛所取代.
在现有的免疫传感器中改造了两个de novo结合剂,实现了敏感的迪戈辛检测 (Kd = 109 nM).

结论:

基于深度学习的设计快速产生有效的竞争对手绑定剂用于小分子传感.
De novo绑定器简化和优化基于竞争的传感平台.
这种方法加速了生物传感器的发展,用于各种小分子点.

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