AIMC Topic: Enzymes

Clear Filters Showing 71 to 80 of 100 articles

Finding the dark matter: Large language model-based enzyme kinetic data extractor and its validation.

Protein science : a publication of the Protein Society
Despite the vast number of enzymatic kinetic measurements reported across decades of biochemical literature, the majority of relational enzyme kinetic data-linking amino acid sequence, substrate identity, kinetic parameters, and assay conditions-rema...

Engineering catalytically promiscuous enzymes to serve new functions.

Biotechnology advances
Catalytic promiscuity in enzymes refers to their ability to catalyze multiple chemically distinct reactions in addition to their native activity. The increasing discovery of additional enzymes exhibiting catalytic promiscuity has underscored the sign...

AI-driven de novo enzyme design: Strategies, applications, and future prospects.

Biotechnology advances
Enzymes are indispensable for biological processes and diverse applications across industries. While top-down modification strategies, such as directed evolution, have achieved remarkable success in optimizing existing enzymes, bottom-up de novo enzy...

Bioinspired rational design of nanozymes.

Materials horizons
Nanozymes, an emerging class of artificial enzymes, have attracted increasing attention for their potential in environmental monitoring, industrial catalysis, food safety, and biomedicine. To date, more than 1500 nanomaterials have been identified wi...

DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.

Nucleic acids research
DeepMolecules is an easily accessible web server for predicting protein-small molecule interactions. It integrates four state-of-the-art models: ESP and SPOT for identifying substrates of enzymes and transporters, respectively, TurNuP for predicting ...

A multimodal deep learning framework for enzyme turnover prediction with missing modality.

Computers in biology and medicine
Accurate prediction of the turnover number (k), which quantifies the maximum rate of substrate conversion at an enzyme's active site, is essential for assessing catalytic efficiency and understanding biochemical reaction mechanisms. Traditional wet-l...

DeepMBEnzy: An AI-Driven Database of Mycotoxin Biotransformation Enzymes.

Journal of agricultural and food chemistry
Mycotoxins are toxic fungal metabolites that pose significant health risks. Enzyme biotransformation is a promising option for detoxifying mycotoxins and for elucidating their intracellular metabolism. However, few mycotoxin-biotransformation enzymes...

Tackling a textbook example of multistep enzyme catalysis with deep learning-driven design.

Molecular cell
Enzyme design has struggled to emulate the complexity and catalytic proficiency of natural enzymes. Lauko et al. show that with the help of deep learning, the design of serine hydrolases that rival nature's ingenuity is possible.

[Intelligent mining, engineering, and design of proteins].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. ...

DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks.

Briefings in bioinformatics
The prediction of enzyme kinetic parameters is crucial for screening enzymes with high catalytic efficiency and desired characteristics to catalyze natural or non-natural reactions. Data-driven machine learning models have been explored to reduce exp...