bioRxivBiochemistryPreprint
In vivo selection for formate dehydrogenases with high efficiency and specificity towards NADP+
L. Calzadiaz Ramirez, C. Calvo-Tusell, G. M. M. Stoffel, S. Lindner, S. Osuna, T. J. Erb, M. Garcia-Borras, A. Bar-Even, C. G. Acevedo-Rocha
Abstract
Efficient regeneration of cofactors is vital for the establishment of continuous biocatalytic processes. Formate is an ideal electron donor for cofactor regeneration due to its general availability, low reduction potential, and benign byproduct (CO2). However, formate dehydrogenases (FDHs) are usual specific to NAD+, such that NADPH regeneration with formate is challenging. Previous studies reported naturally occurring FDHs or engineered FDHs that accept NADP+, but these enzymes show low kinetic efficiencies and specificities. Here, we harness the power of natural selection to engineer FDH variants to simultaneously optimize three properties: kinetic efficiency with NADP+, specificity towards NADP+, and affinity towards formate. By simultaneously mutating multiple residues of FDH from Pseudomonas sp. 101, which exhibits no initial activity towards NADP+, we generate a library of >106 variants. We introduce this library into an E. coli strain that cannot produce NADPH. By selecting for growth with formate as sole NADPH source, we isolate several enzyme variants that support efficient NADPH regeneration. We find that the kinetically superior enzyme variant, harboring five mutations, has 5-fold higher efficiency and 13-fold higher specificity than the best enzyme previously engineered, while retaining high affinity towards formate. By using molecular dynamics simulations, we reveal the contribution of each mutation to the superior kinetics of this variant. We further determine how non-additive epistatic effects improve multiple parameters simultaneously. Our work demonstrates the capacity of in vivo selection to identify superior enzyme variants carrying multiple mutations which would be almost impossible to find using conventional screening methods.
- organism: E. coli
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: experimental assay
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