ProtGPT2 is a deep unsupervised language model for protein design.
ProtGPT2 is a deep unsupervised language model for protein design.
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DOI：
10.1038/s41467-022-32007-7
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发表时间：
2022-07-27
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
蛋白质设计旨在为特定目的定制新的蛋白质，从而具有解决许多环境和生物医学问题的潜力。基于transformer的体系结构的最新进展使得能够生成具有类似人类功能的文本的语言模型的实现成为可能。在这里，受这一成功的启发，我们描述了ProtGPT2，这是一种基于蛋白质空间训练的语言模型，可以按照自然蛋白质序列的原则生成从头生成的蛋白质序列。生成的蛋白质显示天然氨基酸倾向，而无序预测表明88%的protgpt2生成的蛋白质是球状的，与自然序列一致。蛋白质数据库的敏感序列搜索表明，ProtGPT2序列与天然序列有远亲关系，相似网络进一步表明，ProtGPT2正在对蛋白质空间的未开发区域进行采样。AlphaFold对protgpt2序列的预测产生了具有实施例和大循环的折叠良好的非理想结构，并揭示了当前结构数据库中未捕获的拓扑结构。ProtGPT2在几秒钟内生成序列，并且是免费提供的。蛋白质设计旨在为特定目的定制新的蛋白质，从而具有解决许多环境和生物医学问题的潜力。在这里，作者应用了自然语言处理的一些最新进展，生成式变形金刚，来训练ProtGPT2，这是一种语言模型，在设计具有自然属性的蛋白质时，探索蛋白质空间的未知区域。
Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available. Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Here the authors apply some of the latest advances in natural language processing, generative Transformers, to train ProtGPT2, a language model that explores unseen regions of the protein space while designing proteins with nature-like properties.
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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
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Hassabis D
影响因子：
14.8
作者：
Huang PS;Feldmeier K;Parmeggiani F;Velasco DAF;Höcker B;Baker D
通讯作者：
Baker D
影响因子：
5.6
作者：
Ferruz, Noelia;Lobos, Francisco;Hoecker, Birte
通讯作者：
Hoecker, Birte
影响因子：
14.9
作者：
Buchan, Daniel W. A.;Jones, David T.
通讯作者：
Jones, David T.
DOI：
10.1109/tpami.2021.3095381
发表时间：
2022-10-01
影响因子：
23.6
作者：
Elnaggar, Ahmed;Heinzinger, Michael;Rost, Burkhard
通讯作者：
Rost, Burkhard