AIMC Topic: Enzymes

Clear Filters Showing 1 to 10 of 100 articles

EvoZymePro-Cat: A Protein-Ligand-Aware Deep Learning Framework for Predicting Mutation Effects in Enzyme Function.

ACS synthetic biology
Enzymes are biological catalysts that speed up chemical reactions in an eco-friendly way. Precise enzyme design is hindered by vast sequence space and intricate sequence-structure-function interdependencies. To address these challenges, we developed ...

Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.

Journal of chemical information and modeling
Enzyme kinetic parameters, including , , /, and , are critical for guiding applications in enzyme engineering, metabolic modeling, and synthetic biology by providing quantitative information on enzyme activity under various conditions. Experimental d...

Programmable motion of an enzyme-powered macroscale gel boat: a functional sensing platform.

Materials horizons
An augmented strategy for constructing intelligent soft robots includes the transfer of biogenic features from nature to man-made artificial systems serving a range of life-like functions. Inspired by living technology, we have customized macroscale ...

Protein language models uncover carbohydrate-active enzyme function in metagenomics.

BMC bioinformatics
BACKGROUND: The functional annotation of uncharacterized microbial enzymes from metagenomic data remains a significant challenge, limiting our understanding of microbial metabolic dynamics. Traditional annotation methods often rely on sequence homolo...

Artificial Intelligence-Driven Design of Robust Enzymes to Enhance Their Performance.

ACS synthetic biology
The booming artificial intelligence (AI) technology provides an opportunity to precisely carry out design of enzymes and create new biocatalysts with significantly enhanced performance. In the past decade, successful enzyme design cases, although t...

TCNeKP: A Novel Deep Learning Architecture for Enzyme Catalytic Activity Prediction.

Journal of chemical information and modeling
Accurate prediction of enzyme kinetic parameters ( and ) is crucial for enzyme rational design and engineering research. Based on a heterogeneous data set encompassing 17,893 and 24,585 records across 8911 enzyme sequences from 7 EC classes and 502...

Modeling Enzyme Temperature Stability from Sequence Segment Perspective.

Journal of chemical information and modeling
Developing enzymes with desired thermal properties is crucial for a wide range of industrial and research applications, and determining temperature stability is an essential step in this process. Experimental determination of thermal parameters is la...

Computer-Aided Techniques in the Engineering of Enzyme Binding Pockets: New Perspectives and Frontiers.

Journal of agricultural and food chemistry
Enzymes, recognized for their remarkable catalytic efficiency, play a crucial role in a myriad of biochemical reactions. However, the catalytic performance of natural enzymes frequently does not meet the demands of specific applications. To address t...

A computational pipeline for predicting distal hotspots in an artificial enzyme.

International journal of biological macromolecules
Targeting distal mutations holds promising implications for enzyme engineering. Here, we present an open-source computational workflow designed to explore the functional impact of distal sites, demonstrated on an artificial enzyme built on the widely...

Microbial and enzymatic strategies for aflatoxin control: Integrating intelligent detection and computational design.

Food chemistry
Aflatoxins (AFs), potent carcinogenic mycotoxins, pose a major global threat to human health. This review offers an in-depth summary of microorganisms capable of degrading AFs, including bacteria, probiotics, and fungi, and highlights the key enzymes...