8D09
Hallucinated C4 protein assembly HALC4_136
Summary for 8D09
| Entry DOI | 10.2210/pdb8d09/pdb |
| Descriptor | HALC4_136 (2 entities in total) |
| Functional Keywords | de novo design hallucination cyclic oligomer proteinmpnn, de novo protein |
| Biological source | synthetic construct |
| Total number of polymer chains | 2 |
| Total formula weight | 15217.94 |
| Authors | Ragotte, R.J.,Bera, A.K.,Wicky, B.I.M.,Milles, L.F.,Baker, D. (deposition date: 2022-05-25, release date: 2022-09-28, Last modification date: 2024-04-03) |
| Primary citation | Wicky, B.I.M.,Milles, L.F.,Courbet, A.,Ragotte, R.J.,Dauparas, J.,Kinfu, E.,Tipps, S.,Kibler, R.D.,Baek, M.,DiMaio, F.,Li, X.,Carter, L.,Kang, A.,Nguyen, H.,Bera, A.K.,Baker, D. Hallucinating symmetric protein assemblies. Science, 378:56-61, 2022 Cited by PubMed Abstract: Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here, we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of seven designs are very similar to the computational models (median root mean square deviation: 0.6 angstroms), as are three cryo-electron microscopy structures of giant 10-nanometer rings with up to 1550 residues and symmetry; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be generated using deep learning and pave the way for the design of increasingly complex components for nanomachines and biomaterials. PubMed: 36108048DOI: 10.1126/science.add1964 PDB entries with the same primary citation |
| Experimental method | X-RAY DIFFRACTION (1.9 Å) |
Structure validation
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