蛋白质复合体的逆折叠与结构信息化的语言模型使得无监督的抗体进化.
Varun R Shanker1,2,3, Theodora U J Bruun2,3,4, Brian L Hie3,4
1Stanford Biophysics Program, Stanford University School of Medicine, Stanford, CA 94305, USA.
bioRxiv : the preprint server for biology
|January 8, 2024
概括
用3D结构数据增强的蛋白质语言模型可以指导蛋白质进化. 这种反向折叠方法显著改善了针对SARS-CoV-2变种的抗体功能.
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.
- 机器学习在药物发现中的作用
背景情况:
- 蛋白质的功能是由3D结构决定的,而不仅仅是序列.
- 现有的蛋白质语言模型主要使用序列数据.
- 3D结构信息对于理解蛋白质活动和进化至关重要.
结论:
- 这种结构导向的蛋白质语言模型可以提高蛋白质的功能,而不需要特定任务的训练.
- 与其他ML引导的定向进化方法相比,反向折叠显示了领先的实验成功率.
- 这种方法为设计各种蛋白质和治疗抗体提供了强大的工具.
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