Loop engineering and activity improvement of TEV protease by a phagemid-based selection system
Abstract
Abactstr: Loop engineering of enzymes remains challenging due to high flexibility and conformational complexity, posing a bottleneck for deep-learning-based design. Here, we constructed mutant libraries for three loops of TEV protease to assess combining directed evolution with deep learning. Using an M13 phagemid-based selection system, the three libraries were screened, resulting in a Loop 1 variant (HyperTEV60/L1) that significantly enhanced the Michaelis constant ( K m ) of the HyperTEV60 scaffold, a highly active mutant identified by ProteinMPNN. Structural modeling suggested that a single-residue deletion and substitution in Loop 1 expands the substrate binding pocket, accounting for the improved K m . Although the catalytic efficiency k cat / K m of HyperTEV60/L1 was only marginally higher than HyperTEV60, due to a k cat decrease, our results reveal that the phagemid-based selection system tended to find variants optimizing K m . This study demonstrates that combining deep-learning-based global optimization with localized directed evolution maximizes the probability of discovering distinct, high-performance enzyme variants.
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BibTeX
@article{yamaguchi2026loop,
title = {{Loop engineering and activity improvement of TEV protease by a phagemid-based selection system}},
author = {Hiroki Yamaguchi and Mark Isalan},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-74102-5},
url = {https://www.nature.com/articles/s41598-026-74102-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Loop engineering and activity improvement of TEV protease by a phagemid-based selection system
AU - Hiroki Yamaguchi
AU - Mark Isalan
PY - 2026
DA - 2026/10/04
JO - Scientific Reports
DO - 10.1038/s41598-026-74102-5
UR - https://www.nature.com/articles/s41598-026-74102-5
AB - Abactstr: Loop engineering of enzymes remains challenging due to high flexibility and conformational complexity, posing a bottleneck for deep-learning-based design. Here, we constructed mutant libraries for three loops of TEV protease to assess combining directed evolution with deep learning. Using an M13 phagemid-based selection system, the three libraries were screened, resulting in a Loop 1 variant (HyperTEV60/L1) that significantly enhanced the Michaelis constant ( K m ) of the HyperTEV60 scaffold, a highly active mutant identified by ProteinMPNN. Structural modeling suggested that a single-residue deletion and substitution in Loop 1 expands the substrate binding pocket, accounting for the improved K m . Although the catalytic efficiency k cat / K m of HyperTEV60/L1 was only marginally higher than HyperTEV60, due to a k cat decrease, our results reveal that the phagemid-based selection system tended to find variants optimizing K m . This study demonstrates that combining deep-learning-based global optimization with localized directed evolution maximizes the probability of discovering distinct, high-performance enzyme variants.
SN - 2045-2322
ER -