期刊
NATURE STRUCTURAL & MOLECULAR BIOLOGY
卷 -, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41594-023-01112-6
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This study developed a deep-learning approach to design closed repeat proteins with central binding pockets, which can be used for specific binding of small molecules. By constructing 38 structurally diverse pseudocyclic designs and validating them with biophysical data, the authors found that the designed structures closely resembled the actual structures. Docking studies suggested that the diversity of folds and central pockets in these proteins provide effective starting points for designing small-molecule binders and enzymes.
In pseudocyclic proteins, such as TIM barrels, beta barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes. Here, the authors constructed a deep-learning approach to design closed repeat proteins with central binding pockets-a step towards designing proteins to specifically bind small molecules.
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