8CYK
Crystal structure of hallucinated protein HALC1_878
8CYK の概要
| エントリーDOI | 10.2210/pdb8cyk/pdb |
| 分子名称 | HALC1_878 (2 entities in total) |
| 機能のキーワード | de novo design, hallucination protein mpnn, de novo protein |
| 由来する生物種 | synthetic construct |
| タンパク質・核酸の鎖数 | 2 |
| 化学式量合計 | 31292.31 |
| 構造登録者 | Ragotte, R.J.,Bera, A.K.,Milles, L.F.,Wicky, B.I.M.,Baker, D. (登録日: 2022-05-23, 公開日: 2022-09-28, 最終更新日: 2024-04-03) |
| 主引用文献 | Dauparas, J.,Anishchenko, I.,Bennett, N.,Bai, H.,Ragotte, R.J.,Milles, L.F.,Wicky, B.I.M.,Courbet, A.,de Haas, R.J.,Bethel, N.,Leung, P.J.Y.,Huddy, T.F.,Pellock, S.,Tischer, D.,Chan, F.,Koepnick, B.,Nguyen, H.,Kang, A.,Sankaran, B.,Bera, A.K.,King, N.P.,Baker, D. Robust deep learning-based protein sequence design using ProteinMPNN. Science, 378:49-56, 2022 Cited by PubMed Abstract: Although 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, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 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, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins. PubMed: 36108050DOI: 10.1126/science.add2187 主引用文献が同じPDBエントリー |
| 実験手法 | X-RAY DIFFRACTION (1.65 Å) |
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