蛋白质设计
序列(生物学)
计算机科学
深度学习
计算生物学
蛋白质结构
人工智能
化学
生物
生物化学
作者
Nathaniel R. Bennett,Brian Coventry,Inna Goreshnik,Buwei Huang,Aza Allen,Dionne Vafeados,Ying Po Peng,Justas Dauparas,Minkyung Baek,Lance Stewart,Frank DiMaio,Steven De Munck,Savvas N. Savvides,David Baker
标识
DOI:10.1038/s41467-023-38328-5
摘要
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
科研通智能强力驱动
Strongly Powered by AbleSci AI