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Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function.

ABSTRACT: Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/αKG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.

SUBMITTER: King BR

PROVIDER: S-EPMC11290999 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function.

King Brianne R BR Sumida Kiera H KH Caruso Jessica L JL Baker David D Zalatan Jesse G JG

bioRxiv : the preprint server for biology 20240725

Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, ...[more]

PMID: 39091854

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Project description:Orthogonal ribosomes are unnatural ribosomes that are directed towards orthogonal messenger RNAs in Escherichia coli, through an altered version of the 16S ribosomal RNA of the small subunit1. Directed evolution of orthogonal ribosomes has provided access to new ribosomal function, and the evolved orthogonal ribosomes have enabled the encoding of multiple non-canonical amino acids into proteins2-4. The original orthogonal ribosomes shared the pool of 23S ribosomal RNAs, contained in the large subunit, with endogenous ribosomes. Selectively directing a new 23S rRNA to an orthogonal mRNA, by controlling the association between the orthogonal 16S rRNAs and 23S rRNAs, would enable the evolution of new function in the large subunit. Previous work covalently linked orthogonal 16S rRNA and a circularly permuted 23S rRNA to create orthogonal ribosomes with low activity5,6; however, the linked subunits in these ribosomes do not associate specifically with each other, and mediate translation by associating with endogenous subunits. Here we discover engineered orthogonal 'stapled' ribosomes (with subunits linked through an optimized RNA staple) with activities comparable to that of the parent orthogonal ribosome; they minimize association with endogenous subunits and mediate translation of orthogonal mRNAs through the association of stapled subunits. We evolve cells with genomically encoded stapled ribosomes as the sole ribosomes, which support cellular growth at similar rates to natural ribosomes. Moreover, we visualize the engineered stapled ribosome structure by cryo-electron microscopy at 3.0 Å, revealing how the staple links the subunits and controls their association. We demonstrate the utility of controlling subunit association by evolving orthogonal stapled ribosomes which efficiently polymerize a sequence of monomers that the natural ribosome is intrinsically unable to translate. Our work provides a foundation for evolving the rRNA of the entire orthogonal ribosome for the encoded cellular synthesis of non-canonical biological polymers7.

Dataset Information

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function.

Publications

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function.

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