Engineering functional thermostable proteins using ancestral sequence reconstruction

Thomson, Raine E. S.; Carrera-Pacheco, Saskya E.; Gillam, E. M. J.

doi:10.1016/j.jbc.2022.102435

Cited by 47 publications

(32 citation statements)

References 131 publications

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“…2,3 Many proteinbased therapeutics and catalysts are limited in their industrial application by low stability, making protein stabilization a research area of increasing interest. 4,5 Experimental methods such as directed evolution have been extensively used to optimize desirable features in proteins but are often prohibitively resource-and labor-intensive. 6,7 Computational tools have been developed to achieve the benefits of directed evolution while minimizing experimental screening.…”

Section: ■ Introductionmentioning

confidence: 99%

Improving Protein Expression, Stability, and Function with ProteinMPNN

Sumida,

Núñez-Franco,

Kalvet

et al. 2024

J. Am. Chem. Soc.

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Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

show abstract

Section: ■ Introductionmentioning

confidence: 99%

Improving Protein Expression, Stability, and Function with ProteinMPNN

Sumida,

Núñez-Franco,

Kalvet

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

show abstract

“…2,3 Many protein-based therapeutics and catalysts are limited in their industrial application by low stability, making protein stabilization a research area of increasing interest. 4,5 Experimental methods such as directed evolution have been extensively used to optimize desirable features in proteins, but are often prohibitively resource- and labor-intensive. 6,7 Computational tools have been developed to achieve the benefits of directed evolution while minimizing experimental screening.…”

Section: Introductionmentioning

confidence: 99%

Improving protein expression, stability, and function with ProteinMPNN

Sumida,

Núñez-Franco,

Kalvet

et al. 2023

Preprint

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Natural proteins are highly optimized for function, but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here we show that the deep neural network ProteinMPNN together with evolutionary and structural information provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

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“…Novel enzymes has been fueled by the diversification of enzymes for billions of years evolution [1]. Therefore, new design method based on the co-evolution of amino acid residues is proposed [2,3]. D-amino acids are key intermediates for pharmaceuticals and agrochemicals [4].…”

Section: Introductionmentioning

confidence: 99%

Design of Motifs Interfacial Interactions by Co-evolved Analysis of D-amino Acid Dehydrogenase for Stability Enhancement

Zhang

Duan

et al. 2023

Preprint

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To design D-amino acid dehydrogenase (DAADH) for enhanced stability, the interactions of the subunit interfaces of DAADH were analyzed. Interaction network analysis of DAADH indicated that there are only weak interactions between the A and B subunits. Several co-evolved residue pairs were selected for mutation to enhance interfacial interactions of subunits, and 11 designed MDHs were obtained. DA06 and DA11 were selected for experimental verification for their salt bridges are 1.4 and 1.2-fold of that of DAwild, respectively. DA11 can maintain 93% activity in 80℃, while it was only 40% for DAwild. Thermostabiliy study indicated the half-life of DA11 was 2-fold of DAwild. Molecular dynamics simulations revealed that the extraordinary stability of the DA11 was due to the formation of extra interfacial salt bridges. The paper provided a strategy of mutations outside the active site of enzyme by co-evolutionary analysis which can reduce the effect of the activity-thermostability trade-off.

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Engineering functional thermostable proteins using ancestral sequence reconstruction

Cited by 47 publications

References 131 publications

Improving Protein Expression, Stability, and Function with ProteinMPNN

Improving Protein Expression, Stability, and Function with ProteinMPNN

Improving protein expression, stability, and function with ProteinMPNN

Design of Motifs Interfacial Interactions by Co-evolved Analysis of D-amino Acid Dehydrogenase for Stability Enhancement

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