美国华盛顿大学
研究人员描述了一种基于深度学习的蛋白质序列设计方法ProteinMPNN，在计算和实验测试中都有出色的表现。在本地蛋白质骨架上，ProteinMPNN的序列恢复率为52.4%，而Rosetta为32.9%。不同位置的氨基酸序列可以在单链或多链之间进行耦合，从而能够应用于当前广泛的蛋白质设计挑战。研究人员利用X射线晶体学、冷冻电镜和功能研究证明了ProteinMPNN的广泛实用性和高准确性，它挽救了以前使用Rosetta或AlphaFold进行的蛋白质单体、环状同源寡聚体、四面体纳米颗粒和靶标结合蛋白的失败设计。
据悉，虽然深度学习已经彻底改变了蛋白质结构预测，但几乎所有实验特征的新蛋白质设计都是使用基于物理的方法，如Rosetta产生的。
附：英文原文
Title: Robust deep learning–based protein sequence design using ProteinMPNN
Author: J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker
Issue&Volume: 2022-09-15
Abstract: While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning–based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins.
DOI: add2187
Source: https://www.science.org/doi/10.1126/science.add2187