Robust deep learning-based protein sequence design using ProteinMPNN.
Robust deep learning-based protein sequence design using ProteinMPNN.
复制标题
DOI：
10.1126/science.add2187
复制
发表时间：
2022-10-07
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
虽然深度学习已经彻底改变了蛋白质结构预测，但几乎所有实验表征的从头蛋白质设计都是使用基于物理的方法（如Rosetta）生成的。在这里，我们描述了一种基于深度学习的蛋白质序列设计方法，ProteinMPNN，在计算机模拟和实验测试中都具有出色的性能。不同位置的氨基酸序列可以在单链或多链之间偶联，从而能够应用于广泛的当前蛋白质设计挑战。在天然蛋白质骨架上，ProteinMPNN的序列回收率为52.4%，而Rosetta为32.9%。在训练过程中引入噪声改善了蛋白质结构模型上的序列恢复，并产生了使用结构预测算法评估的更鲁棒地编码其结构的序列。我们使用X射线晶体学，cryoEM和功能研究，通过挽救以前失败的设计，使用Rosetta或AlphaFold，蛋白质单体，环状同源寡聚物，四面体纳米颗粒和靶结合蛋白质，证明了ProteinMPNN的广泛实用性和高准确性。描述了一种基于深度学习的蛋白质序列设计方法，该方法广泛适用于当前的设计挑战，并在计算机模拟和实验测试中表现出出色的性能。
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. 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. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. 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. A deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.
登录
查看更多内容
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
64.5
作者：
Walls AC;Fiala B;Schäfer A;Wrenn S;Pham MN;Murphy M;Tse LV;Shehata L;O'Connor MA;Chen C;Navarro MJ;Miranda MC;Pettie D;Ravichandran R;Kraft JC;Ogohara C;Palser A;Chalk S;Lee EC;Guerriero K;Kepl E;Chow CM;Sydeman C;Hodge EA;Brown B;Fuller JT;Dinnon KH 3rd;Gralinski LE;Leist SR;Gully KL;Lewis TB;Guttman M;Chu HY;Lee KK;Fuller DH;Baric RS;Kellam P;Carter L;Pepper M;Sheahan TP;Veesler D;King NP
通讯作者：
King NP
DOI：
10.1126/science.add1964
发表时间：
2022-10-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
通讯作者：
--
影响因子：
64.8
作者：
King, Neil P.;Bale, Jacob B.;Sheffler, William;McNamara, Dan E.;Gonen, Shane;Gonen, Tamir;Yeates, Todd O.;Baker, David
通讯作者：
Baker, David
影响因子：
16.6
作者：
Anand N;Eguchi R;Mathews II;Perez CP;Derry A;Altman RB;Huang PS
通讯作者：
Huang PS