蛋白质设计
计算生物学
亲缘关系
表面蛋白
蛋白质结构
蛋白质工程
血浆蛋白结合
结构生物学
蛋白质-蛋白质相互作用
合理设计
氨基酸
生物物理学
结合位点
结合亲和力
靶蛋白
纳米技术
化学
生物系统
生物
生物化学
材料科学
酶
病毒学
受体
基因
作者
Longxing Cao,Brian Coventry,Inna Goreshnik,Buwei Huang,William Sheffler,Joon Sung Park,Kevin M. Jude,Iva Marković,Rameshwar U. Kadam,Koen H. G. Verschueren,Kenneth Verstraete,Scott Thomas Russell Walsh,Nathaniel R. Bennett,Ashish Phal,Aerin Yang,Lisa Kozodoy,Michelle DeWitt,Lora K. Picton,L. M. Miller,Eva‐Maria Strauch
出处
期刊:Nature
[Nature Portfolio]
日期:2022-03-24
卷期号:605 (7910): 551-560
被引量:386
标识
DOI:10.1038/s41586-022-04654-9
摘要
Abstract The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge 1–5 . Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder–target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein–protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI