敌手
生物信息学
连接器
融合蛋白
计算生物学
蛋白酶
蛋白质工程
蛋白质设计
化学
离解常数
计算机科学
人工智能
生物
生物化学
酶
蛋白质结构
受体
基因
操作系统
重组DNA
作者
Odessa J. Goudy,Amrita Nallathambi,Tomoaki Kinjo,Nicholas Z. Randolph,Brian Kuhlman
标识
DOI:10.1073/pnas.2307371120
摘要
There has been considerable progress in the development of computational methods for designing protein–protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1, and the top-performing AiDs were selected for further characterization as single-domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (K D s) below 150 nM, with the lowest K D equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high-affinity protein binders.
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