Probing the sequence variability tolerance in a de novo α-helical barrel biocatalyst
- 1Institute of Biochemistry, Graz University of Technology
Abstract
De novo-designed enzymes have recently achieved high catalytic activity and stereoselectivity while demonstrating exceptional thermostability in entirely novel protein scaffolds. Among these, -helical barrel protein scaffolds are attractive structures for biocatalysis due to their structural simplicity, high thermostability, and rationalizable sequence patterning. However, enabling major structural reengineering of these scaffolds while maintaining the structure, stability and catalytic activity while also improving soluble protein production remain major challenges and pose the fundamental question how engineerable a de novo backbone-sequence pair is. Here, we combine deep learning based and classic computational protein design to modify and optimize de novo -helical barrel biocatalysts. Using the previously reported six-helical barrel 6H5L as a model scaffold, AlphaFold2-guided RosettaRemodel enabled the design of a truncated variant, whose crystal structure closely matches the design model. Additional sequence-redesign using ProteinMPNN generated a variant with a tenfold increase of soluble protein yield in Escherichia coli. Biochemical, biophysical, and structural analyses showed that both variants retained the overall barrel architecture, high thermal stability, and catalytic activity for both purified protein and whole-cell systems. Detailed kinetic analysis on the variants showed both variation in kcat and Km, reflecting changes in catalytic turnover and substrate binding. Together, these approaches provide new insights and possibilities for the further engineering of functional de novo -helical barrels, their ability to withstand dramatically large sequence changes and their broader application in biocatalysis and biotechnology.
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BibTeX
@misc{elaily2026probing,
title = {{Probing the sequence variability tolerance in a de novo α-helical barrel biocatalyst}},
author = {W. Elaily and M. Braun and D. Stoll and M. Chakatok and M. Aleotti and B. Grill and A. Bijelic and M. Hall and G. Oberdorfer},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.28.754888},
url = {https://www.biorxiv.org/content/10.64898/2026.09.28.754888v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Probing the sequence variability tolerance in a de novo α-helical barrel biocatalyst
AU - W. Elaily
AU - M. Braun
AU - D. Stoll
AU - M. Chakatok
AU - M. Aleotti
AU - B. Grill
AU - A. Bijelic
AU - M. Hall
AU - G. Oberdorfer
PY - 2026
DA - 2026/09/28
PB - bioRxiv
DO - 10.64898/2026.09.28.754888
UR - https://www.biorxiv.org/content/10.64898/2026.09.28.754888v1
AB - De novo-designed enzymes have recently achieved high catalytic activity and stereoselectivity while demonstrating exceptional thermostability in entirely novel protein scaffolds. Among these, -helical barrel protein scaffolds are attractive structures for biocatalysis due to their structural simplicity, high thermostability, and rationalizable sequence patterning. However, enabling major structural reengineering of these scaffolds while maintaining the structure, stability and catalytic activity while also improving soluble protein production remain major challenges and pose the fundamental question how engineerable a de novo backbone-sequence pair is. Here, we combine deep learning based and classic computational protein design to modify and optimize de novo -helical barrel biocatalysts. Using the previously reported six-helical barrel 6H5L as a model scaffold, AlphaFold2-guided RosettaRemodel enabled the design of a truncated variant, whose crystal structure closely matches the design model. Additional sequence-redesign using ProteinMPNN generated a variant with a tenfold increase of soluble protein yield in Escherichia coli. Biochemical, biophysical, and structural analyses showed that both variants retained the overall barrel architecture, high thermal stability, and catalytic activity for both purified protein and whole-cell systems. Detailed kinetic analysis on the variants showed both variation in kcat and Km, reflecting changes in catalytic turnover and substrate binding. Together, these approaches provide new insights and possibilities for the further engineering of functional de novo -helical barrels, their ability to withstand dramatically large sequence changes and their broader application in biocatalysis and biotechnology.
ER -