使用深度学习,精确设计高亲和度蛋白质结合宏循环
Stephen A Rettie1,2,3, David Juergens2,4, Victor Adebomi1,2
1Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
RF是一种新的AI工具,可以设计宏环来结合治疗性蛋白质. 它迅速产生具有已知的结合模式的高亲和度结合剂,加速药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 生物化学 生物化学
- 药物发现 药物发现
背景情况:
- 设计用于治疗蛋白质的宏环结合剂的传统方法资源密集,对结合模式的控制有限.
- 现有的基于物理和深度学习的方法缺乏可靠的方法来*de novo*设计蛋白质结合宏循环.
研究的目的:
- 引入RFpeptides,这是一个基于扩散的新管道,用于*de novo*设计针对蛋白质标的宏环结合剂.
- 为了证明RF在产生各种治疗标的高亲和度结合剂中的有效性.
主要方法:
- 采用了化扩散模型 (RF) 进行宏环结合剂的*de novo*设计.
- 对四种不同的蛋白质标 (MCL1,MDM2,GABARAP,RbtA) 进行了设计宏循环的测试.
- 经过验证的结合亲和度 (K_D,IC50) 和通过X射线结晶学确定复杂结构.
主要成果:
- 针对所有测试的标,RF类成功产生了中高亲和度的结合剂.
- 对于MCL1和MDM2结合剂,已达到1-10μM之间的K_D值.
- 在抗GABARAP宏循环中获得6nM K_D和亚纳米IC50,而在抗RbtA结合剂中获得<10nM K_D.
- X射线结构证实了计算设计模型的高精度 (Ca RMSD <1.5 Å对于3/4结构).
结论:
- RF提供了一个强大而快速的框架,用于定制的宏环的设计.
- 具有已知的结合模式的*de novo*设计方法可为诊断和治疗应用提供下游优化.
- 这种方法克服了传统查方法的局限性,加速了新型蛋白质结合剂的开发.
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