Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots
Gutierrez-Rus, Luis I. ; Vos, Eva ; Pantoja-Uceda, David ; Hoffka, Gyula LU ; Gutierrez-Cardenas, Jose ; Ortega-Muñoz, Mariano ; Risso, Valeria A. ; Jimenez, Maria Angeles ; Kamerlin, Shina C.L. LU
and Sanchez-Ruiz, Jose M.
(2025)
In Journal of the American Chemical Society
147(18).
p.14978-14996
- Abstract
Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp... (More)
Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (kcat ∼ 1700 s-1, kcat/KM ∼ 4.3 × 105 M-1 s-1) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.
(Less)
- author
- Gutierrez-Rus, Luis I.
; Vos, Eva
; Pantoja-Uceda, David
; Hoffka, Gyula
LU
; Gutierrez-Cardenas, Jose
; Ortega-Muñoz, Mariano
; Risso, Valeria A.
; Jimenez, Maria Angeles
; Kamerlin, Shina C.L.
LU
and Sanchez-Ruiz, Jose M.
- organization
- publishing date
- 2025-05
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of the American Chemical Society
- volume
- 147
- issue
- 18
- pages
- 19 pages
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- scopus:105000535983
- pmid:40106785
- ISSN
- 0002-7863
- DOI
- 10.1021/jacs.4c09428
- language
- English
- LU publication?
- yes
- id
- 147b6f95-fa30-4030-aacb-f988c28c6284
- date added to LUP
- 2025-09-15 12:40:34
- date last changed
- 2025-09-29 14:51:34
@article{147b6f95-fa30-4030-aacb-f988c28c6284,
abstract = {{<p>Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (k<sub>cat</sub> ∼ 1700 s<sup>-1</sup>, k<sub>cat</sub>/K<sub>M</sub> ∼ 4.3 × 10<sup>5</sup> M<sup>-1</sup> s<sup>-1</sup>) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.</p>}},
author = {{Gutierrez-Rus, Luis I. and Vos, Eva and Pantoja-Uceda, David and Hoffka, Gyula and Gutierrez-Cardenas, Jose and Ortega-Muñoz, Mariano and Risso, Valeria A. and Jimenez, Maria Angeles and Kamerlin, Shina C.L. and Sanchez-Ruiz, Jose M.}},
issn = {{0002-7863}},
language = {{eng}},
number = {{18}},
pages = {{14978--14996}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Journal of the American Chemical Society}},
title = {{Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots}},
url = {{http://dx.doi.org/10.1021/jacs.4c09428}},
doi = {{10.1021/jacs.4c09428}},
volume = {{147}},
year = {{2025}},
}