Biochemical and Computational Characterization of Haloalkane
Dehalogenase Variants Designed by Generative AI: Accelerating
the SN2 Step

J 2025

Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step

GELFAND, Natalia; Vojtěch OREL; Wenqiang CUI; Jiří DAMBORSKÝ; Chenglong LI et. al.

Základní údaje

Originální název

Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step

Autoři

GELFAND, Natalia; Vojtěch OREL (203 Česká republika, domácí); Wenqiang CUI; Jiří DAMBORSKÝ (203 Česká republika, domácí); Chenglong LI; Zbyněk PROKOP (203 Česká republika, garant, domácí); Wen Jun XIE a Arieh WARSHEL

Vydání

Journal of the American Chemical Society, WASHINGTON, AMER CHEMICAL SOC, 2025, 0002-7863

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10400 1.4 Chemical sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 14.500 v roce 2023

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1021/jacs.4c15551

UT WoS

001395247500001

EID Scopus

2-s2.0-85214580679

Klíčová slova anglicky

X-RAY-STRUCTURE; CATALYTIC MECHANISM; ENZYME; DYNAMICS; MODEL

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 12. 3. 2025 12:02, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the SN2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model. We then designed lower-order protein variants of haloalkane dehalogenase using the model. Kinetic measurements confirmed the successful design of protein variants that enhance catalytic activity, above that of the wild type, in the overall reaction and in particular in the SN2 step. On the simulation side, we provided molecular insights into these designs for the SN2 step using the empirical valence bond (EVB) and metadynamics simulations. The EVB calculations showed activation barriers consistent with experimental reaction rates, while examining the effect of amino acid replacements on the electrostatic effect on the activation barrier and the consequence of water penetration, as well as the extent of ground state destabilization/stabilization. Metadynamics simulations emphasize the importance of the substrate positioning in enzyme catalysis. Overall, our AI-guided approach successfully enabled the design of a variant with a faster rate for the SN2 step than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized through natural evolution.

Návaznosti

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

LM2023055, projekt VaV

Název: Česká národní infrastruktura pro biologická data

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, ELIXIR-CZ: Česká národní infrastruktura pro biologická data

LM2023069, projekt VaV

Název: Výzkumná infrastruktura RECETOX

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Výzkumná infrastruktura RECETOX

LX22NPO5107, projekt VaV

Název: Národní ústav pro neurologický výzkum

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Národní ústav pro neurologický výzkum, 5.1 EXCELES

857560, interní kód MU
(Kód CEP: EF17_043/0009632)

Název: CETOCOEN Excellence (Akronym: CETOCOEN Excellence)

Investor: Evropská unie, CETOCOEN Excellence, Spreading excellence and widening participation

Citovat

GELFAND, Natalia; Vojtěch OREL; Wenqiang CUI; Jiří DAMBORSKÝ; Chenglong LI; Zbyněk PROKOP; Wen Jun XIE a Arieh WARSHEL. Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step. Journal of the American Chemical Society. WASHINGTON: AMER CHEMICAL SOC, 2025, roč. 147, č. 3, s. 2747-2755. ISSN 0002-7863. Dostupné z: https://dx.doi.org/10.1021/jacs.4c15551.

@article{2483241,
   author = {Gelfand, Natalia and Orel, Vojtěch and Cui, Wenqiang and Damborský, Jiří and Li, Chenglong and Prokop, Zbyněk and Xie, Wen Jun and Warshel, Arieh},
   article_location = {WASHINGTON},
   article_number = {3},
   doi = {http://dx.doi.org/10.1021/jacs.4c15551},
   keywords = {X-RAY-STRUCTURE; CATALYTIC MECHANISM; ENZYME; DYNAMICS; MODEL},
   language = {eng},
   issn = {0002-7863},
   journal = {Journal of the American Chemical Society},
   title = {Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step},
   url = {https://pubs.acs.org/doi/10.1021/jacs.4c15551},
   volume = {147},
   year = {2025}
}

TY  - JOUR
ID  - 2483241
AU  - Gelfand, Natalia - Orel, Vojtěch - Cui, Wenqiang - Damborský, Jiří - Li, Chenglong - Prokop, Zbyněk - Xie, Wen Jun - Warshel, Arieh
PY  - 2025
TI  - Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step
JF  - Journal of the American Chemical Society
VL  - 147
IS  - 3
SP  - 2747-2755
EP  - 2747-2755
PB  - AMER CHEMICAL SOC
SN  - 00027863
KW  - X-RAY-STRUCTURE
KW  - CATALYTIC MECHANISM
KW  - ENZYME
KW  - DYNAMICS
KW  - MODEL
UR  - https://pubs.acs.org/doi/10.1021/jacs.4c15551
N2  - Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the SN2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model. We then designed lower-order protein variants of haloalkane dehalogenase using the model. Kinetic measurements confirmed the successful design of protein variants that enhance catalytic activity, above that of the wild type, in the overall reaction and in particular in the SN2 step. On the simulation side, we provided molecular insights into these designs for the SN2 step using the empirical valence bond (EVB) and metadynamics simulations. The EVB calculations showed activation barriers consistent with experimental reaction rates, while examining the effect of amino acid replacements on the electrostatic effect on the activation barrier and the consequence of water penetration, as well as the extent of ground state destabilization/stabilization. Metadynamics simulations emphasize the importance of the substrate positioning in enzyme catalysis. Overall, our AI-guided approach successfully enabled the design of a variant with a faster rate for the SN2 step than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized through natural evolution.
ER  -

GELFAND, Natalia; Vojtěch OREL; Wenqiang CUI; Jiří DAMBORSKÝ; Chenglong LI; Zbyněk PROKOP; Wen Jun XIE a Arieh WARSHEL. Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step. \textit{Journal of the American Chemical Society}. WASHINGTON: AMER CHEMICAL SOC, 2025, roč.~147, č.~3, s.~2747-2755. ISSN~0002-7863. Dostupné z: https://dx.doi.org/10.1021/jacs.4c15551.

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