生物
蛋白质设计
概括性
计算生物学
蛋白质结构预测
最大化
卡斯普
蛋白质结构
合成生物学
人工神经网络
人工智能
计算机科学
机器学习
生化工程
工程类
生物化学
经济
微观经济学
心理治疗师
心理学
作者
Jue Wang,J. P. Watson,Sidney L. Lisanza
出处
期刊:Cold Spring Harbor Perspectives in Biology
[Cold Spring Harbor Laboratory]
日期:2024-03-04
卷期号:: a041472-a041472
标识
DOI:10.1101/cshperspect.a041472
摘要
Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.
科研通智能强力驱动
Strongly Powered by AbleSci AI