使用深度学习的高亲和度蛋白质结合宏循环的准确 de novo 设计
Stephen A Rettie1,2,3, David Juergens2,4, Victor Adebomi1,2
1Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
Nature chemical biology
|June 20, 2025
概括
基于扩散的新型管道RFpeptides快速设计用于治疗蛋白质的宏环结合剂. 这种方法实现了针对不同目标的高亲和度结合剂,使得用于诊断和治疗的定制设计成为可能.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 药物发现 药物发现
背景情况:
- 开发用于治疗蛋白质的宏环结合剂通常需要资源密集的选,对结合模式的控制有限.
- 目前用于蛋白质结合宏循环的de novo设计方法缺乏稳定性.
研究的目的:
- 引入RFpeptides,一种基于无毒扩散的管道,用于针对蛋白质标的宏环结合剂的de novo设计.
- 为了证明RF在设计用于治疗应用的高亲和度结合物的有效性.
主要方法:
- 采用一种消毒扩散模型 (RF) 进行宏环的计算设计.
- 对四种不同的蛋白质标进行了测试,包括 Rhombotarget A (RbtA).
- 使用实验方法验证的结合亲和力和结构准确性,包括X射线晶体学.
主要成果:
- 成功获得了对所有四个测试的蛋白质标具有中高 afinity 的宏环结合剂.
- 从预测的结构设计了一个RbtA的高亲和度结合剂 (Kd<10nM).
- 宏环蛋白复合体的实验结构与计算模型非常相匹配 (Cα RMSD < 1.5 Å).
结论:
- RF提供了一个强大的框架,用于快速定制的宏环结合剂的设计.
- 该管道促进了用于诊断和治疗应用的新型宏环的开发.
- 这种方法克服了传统选方法在蛋白质结合宏循环开发中的局限性.
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