Physics-based modeling in the new era of enzyme engineering

Nature Computational Science, Journal Year: 2025, Volume and Issue: 5(4), P. 279 - 291

Published: April 24, 2025

Language: Английский

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Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 277, P. 134530 - 134530

Published: Aug. 5, 2024

Language: Английский

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Advancements in biocatalysis: A comprehensive review of applications in organic synthesis

Synthetic Communications, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: Jan. 9, 2025

Biocatalysis is an essential tool in the green synthesis of compounds. These catalysts exhibit regioselectivity and stereoselectivity toward specific products, enabling nontoxic eco-friendly synthetic routes with high-yield biotransformation enantioselectivity, resulting enantiopure products. Moreover, E-factor, which measures efficiency a process by calculating ratio waste generated to product formed, significantly lower biocatalytic organic than traditional methods. The reusability biocatalysts allows for economically advantageous designs, reproducibility products better yields energy route. In this context, enzymes their modified counterparts have been emphasized as asymmetric bio-reduction various ketones, aldehydes, esters, alcohols, other substrates.

Language: Английский

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Mechanism of influence of nattokinase terminal sequence on catalytic performance and molecular modification

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 141872 - 141872

Published: March 1, 2025

Language: Английский

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IECata: Interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Enzyme catalytic efficiency (k_cat / K_m) is a key parameter for identifying high-activity enzymes. Recently deep learning techniques have demonstrated the potential fast and accurate k_cat K_m prediction. However, three challenges remain: (i) limited size of available dataset hinders development models; (ii) model predictions lacked reliable confidence estimates; (iii) models interpretable insights into enzyme-catalyzed reactions. To address these challenges, we proposed IECata, prediction that provides uncertainty estimation interpretability. IECata collected two datasets from databases literatures. By introducing evidential learning, an predictions. Moreover, it uses bilinear attention mechanism to focused on crucial local interactions interpret residues substrate atoms in Testing results indicate performance exceeds state-of-the-art benchmark models. Case studies further highlight incorporation screening highly active enzymes can effectively reduce false positives, thereby improving experimental validation accelerating directed enzyme evolution. public usage developed online platform: http://mathtc.nscc-tj.cn/cataai/.

Language: Английский

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Advances in the Molecular Modification of Microbial ω-Transaminases for Asymmetric Synthesis of Bulky Chiral Amines

Microorganisms, Journal Year: 2025, Volume and Issue: 13(4), P. 820 - 820

Published: April 3, 2025

ω-Transaminases are biocatalysts capable of asymmetrically synthesizing high-value chiral amines through the reductive amination carbonyl compounds, and they ubiquitously distributed across diverse microorganisms. Despite their broad natural occurrence, industrial utility naturally occurring ω-transaminases remains constrained by limited catalytic efficiency toward sterically bulky substrates. Over recent decades, use structure-guided molecular modifications, leveraging three-dimensional structures, mechanisms, machine learning-driven predictions, has emerged as a transformative strategy to address this limitation. Notably, these advancements have unlocked unprecedented progress in asymmetric synthesis amines, which is exemplified industrial-scale production sitagliptin using engineered ω-transaminases. This review systematically explores structural mechanistic foundations ω-transaminase engineering. We first delineate substrate binding regions enzymes, focusing on defining features such tunnels dual pockets. These elements serve critical targets for rational design enhance promiscuity. Next, we dissect recognition mechanisms (S)- (R)-ω-transaminases. Drawing insights, consolidate advances engineering highlight performance aim guide future research implementation tailored

Language: Английский

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Extending the Substrate Scope of an ω-Amine Transaminase from Aspergillus terreus by Reconstructing and Engineering an Ancestral Enzyme

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 7429 - 7440

Published: April 21, 2025

Language: Английский

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Functional and structural insights into a thermostable (S)-selective amine transaminase and its improved substrate scope by protein engineering

Published: May 13, 2025

A (S)-selective amine transaminase from a Streptomyces strain, Sbv333-ATA is biocatalyst showing both high thermostability with melting temperature of 85^oC and broad substrate specificity for the amino acceptor. This enzyme has been further characterised biochemically structurally. The stable in presence up to 20% (v/v) water-miscible cosolvents methanol, ethanol, acetonitrile, dimethyl sulfoxide, biphasic systems petroleum ether, toluene ethyl acetate as an organic phase. showed also good activity towards different donors, such (S)-methylbenzylamine 2-phenylethylamine, aliphatic mono- or di-amines like propylamine, cadaverine, putrescine, selected acids. However, more sterically hindered aromatic amines are not accepted. Based on knowledge three-dimensional structures obtained rational approach site specific mutagenesis carried out broaden Sbv333-ATA. mutant W89A highest bulky substrates, diaromatic compound 1,2-diphenylethylamine. high-resolution holo inhibitor gabaculine bound forms native Sbv333-ATA, F61C mutants have determined at resolutions 1.49, 1.24 1.31 (both mutants) Å respectively. These important revealing details active binding pockets its mechanism.

Language: Английский

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Cutting-edge Computational Approaches in Enzyme Design and Activity Enhancement

Biochemical Engineering Journal, Journal Year: 2024, Volume and Issue: 212, P. 109510 - 109510

Published: Sept. 26, 2024

Language: Английский

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Machine learning-guided malate dehydrogenase engineering for improved production of L-malic acid in Aspergillus niger

Molecular Catalysis, Journal Year: 2025, Volume and Issue: 578, P. 114990 - 114990

Published: March 6, 2025

Language: Английский

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