Journal of the American Chemical SocietyBiochemistryPeer-reviewed journal article
De Novo Design, Directed Evolution and Computational Study of Heme-Binding Helical Bundle Protein Catalysts for Biocatalytic Enantioselective Ge-H Insertion
Wei HuangiD, Gessica M. Adornato, Maggie HorstiD, Turki M. AlturaifiiD, Kai-Peng HouiD, Peng LiuiD, William F. DeGradoiD, Yang YangiD
Abstract
De novo designed proteins offer a malleable platform for the development of stereoselective transformations not found in biochemistry. Here, we report the de novo design and directed evolution of helical bundle protein catalysts for enantioselective germylation through Ge-H insertion, a transformation not previously achieved by enzymatic catalysis. Comparative computational analysis revealed that, relative to Si-H insertion, the Ge-H insertion reaction proceeds through an earlier and more flexible transition state, introducing distinct challenges for stereocontrol. Using a fully de novo designed truncated four-helix bundle scaffold as the starting point, directed evolution afforded a quadruple mutant that catalyzes Ge-H insertion with high efficiency, enantioselectivity, and broad substrate scope. Molecular dynamics simulations indicated that beneficial mutations introduced from directed evolution enhanced active-site preorganization and modulated local back-bone flexibility, contributing to improved transition-state complementarity with fine-tuned binding pocket size and more stable cofactor positioning regulated by hydrogen bonding interactions. These findings showcase the excellent potential for de novo proteins to achieve stereoselective transformations previously unknown to biocatalysts and underscore the importance of active-site remodeling of de novo protein scaffolds via directed evolution in achieving selective catalysis involving flexible transition states. Table of Contents artwork O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/679279v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@11d5beaorg.highwire.dtl.DTLVardef@7d6559org.highwire.dtl.DTLVardef@88d0fborg.highwire.dtl.DTLVardef@501945_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: protein structure
- research focus: protein design
- research focus: protein folding & dynamics
- approach: deep learning
- approach: experimental assay
- Citations
- 11
- FWCI
- 3.29
- Field percentile
- top 6%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry
Zi‐Qi Li, Ruijie K. Zhang, … Frances H. Arnold·bioRxiv·Oct 2, 2026
- Computational design of a thermostable α-helical barrel protein with tunable active-site positioning
Wael Elaily, David B. Stoll, … Gustav Oberdorfer·bioRxiv·Oct 7, 2024
- Evolution promotes transition-state-like conformations with enhanced basicity and electric fields in the substrate complex of a designer enzyme
Abbie Lear, H. Adrian Bunzel, Adrian John Mulholland·bioRxiv·Sep 11, 2026
- Interplay of stability and dynamics in the optimization of a highly proficient de novo enzyme
Sagar Bhattacharya, Gessica M. Adornato, … William F. DeGrado·bioRxiv·Sep 10, 2026
- Design of enantioselective photoenzymes by scaffolding mirror image catalytic sites
Florence J. Hardy, Rebecca Crawshaw, … David A. Baker·bioRxiv·Oct 19, 2026
- Evolution of an enzyme conformational ensemble guides design of an efficient biocatalyst
A. Broom, R. V. Rakotoharisoa, … R. A. Chica·bioRxiv·Mar 19, 2020