AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Enzymes

Showing 11 to 20 of 81 articles

Clear Filters

[AcidBasePred: a protein acid-base tolerance prediction platform based on deep learning].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
The structures and activities of enzymes are influenced by pH of the environment. Understanding and distinguishing the adaptation mechanisms of enzymes to extreme pH values is of great significance for elucidating the molecular mechanisms and promoti...

Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery.

Journal of chemical information and modeling
Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, rem...

Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy.

Nature communications
The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (i...

PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number.

Computational biology and chemistry
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitati...

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.

Genomics, proteomics & bioinformatics
The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of c...

CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.

Nature communications
Estimation of enzymatic activities still heavily relies on experimental assays, which can be cost and time-intensive. We present CatPred, a deep learning framework for predicting in vitro enzyme kinetic parameters, including turnover numbers (k), Mic...

A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network.

Methods (San Diego, Calif.)
It is a vital step to identify the enzyme turnover number (kcat) in synthetic biology and early-stage drug discovery. Recently, deep learning methods have achieved inspiring process to predict kcat with the development of multi-species enzyme-substra...

EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations.

Journal of chemical information and modeling
Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine lea...

LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design.

Database : the journal of biological databases and curation
Enzymes, serving as eco-friendly catalysts, are progressively supplanting traditional chemical catalysts in light industry sectors such as feed, papermaking, textiles, detergents, leather, and sugar production. Despite this advancement, the variabili...

DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.

Bioinformatics (Oxford, England)
MOTIVATION: Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information i...