7MCC image
Entry Detail
PDB ID:
7MCC
Keywords:
Title:
Crystal structure of an AI-designed TIM-barrel F2C
Biological Source:
Source Organism:
PDB Version:
Deposition Date:
2021-04-02
Release Date:
2022-01-19
Method Details:
Experimental Method:
Resolution:
1.46 Å
R-Value Free:
0.21
R-Value Work:
0.17
R-Value Observed:
0.17
Space Group:
P 21 21 21
Macromolecular Entities
Polymer Type:polypeptide(L)
Description:AI-designed TIM-barrel F2C
Chain IDs:A
Chain Length:190
Number of Molecules:1
Biological Source:synthetic construct
Ligand Molecules
Primary Citation
Protein sequence design with a learned potential.
Nat Commun 13 746 746 (2022)
PMID: 35136054 DOI: 10.1038/s41467-022-28313-9

Abstact

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.

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Primary Citation of related structures