Department of Biochemistry, University of Washington, Seattle, WA, USA.
Institute for Protein Design, University of Washington, Seattle, WA, USA.
Nat Commun. 2023 May 6;14(1):2625. doi: 10.1038/s41467-023-38328-5.
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.
最近，仅从目标结构信息出发，就有可能从头设计出高亲和力的蛋白质结合蛋白。然而，由于整体设计成功率较低，仍有很大的改进空间。在这里，我们探索使用深度学习来增强基于能量的蛋白质结合物设计。我们发现，使用 AlphaFold2 或 RoseTTAFold 来评估设计序列采用设计单体结构的概率，以及该结构按设计与目标结合的概率，可以将设计成功率提高近 10 倍。我们还发现，使用 ProteinMPNN 而不是 Rosetta 进行序列设计可以大大提高计算效率。
