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7MCC

Crystal structure of an AI-designed TIM-barrel F2C

Summary for 7MCC
Entry DOI10.2210/pdb7mcc/pdb
DescriptorAI-designed TIM-barrel F2C, SULFATE ION (3 entities in total)
Functional Keywordsde novo protein, tim-barrel
Biological sourcesynthetic construct
Total number of polymer chains1
Total formula weight20973.93
Authors
Mathews, I.I.,Anand-Achim, N.,Perez, C.P.,Huang, P.S. (deposition date: 2021-04-02, release date: 2022-01-19, Last modification date: 2024-04-03)
Primary citationAnand, N.,Eguchi, R.,Mathews, I.I.,Perez, C.P.,Derry, A.,Altman, R.B.,Huang, P.S.
Protein sequence design with a learned potential.
Nat Commun, 13:746-746, 2022
Cited by
PubMed Abstract: The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.
PubMed: 35136054
DOI: 10.1038/s41467-022-28313-9
PDB entries with the same primary citation
Experimental method
X-RAY DIFFRACTION (1.46 Å)
Structure validation

226707

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