NatureBiochemistryPeer-reviewed journal article
High-efficiency Kemp eliminases by complete computational design
Dina ListoviD, Eva VosiD, Gyula HoffkaiD, Shlomo Yakir HochiD, Andrej BergiD, Shelly Hamer-RogotneriD, Orly DymiD, Shina Caroline Lynn KamerliniD, Sarel Jacob FleishmaniD
Abstract
We present a fully computational workflow for de novo design of efficient enzymes using backbone fragments from natural proteins and without recourse to iterative experimental optimization. The best designed Kemp eliminase exhibits >140 mutations from any natural protein, high stability (>85 {degrees}C) and unprecedented catalytic efficiency (12,700 M-1s-1), surpassing previous computational designs by two orders of magnitude. We find that mutations both inside and outside the active site contribute synergistically to the high observed activity and stability. Mutation of an aromatic residue used in all prior Kemp eliminase designs increases efficiency to >105 M-1s-1. Our approach addresses critical limitations in design methodology, generating stable, high-efficiency, new-to-nature enzymes in complex folds and enables testing hypotheses on the fundamentals of biocatalysis through a limited experimental effort.
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: statistical model
- Citations
- 59
- FWCI
- 17.64
- Field percentile
- top 1%
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