AIMC Topic: Substrate Specificity

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A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...

De novo design of high-affinity binders of bioactive helical peptides.

Nature
Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity ...

De novo design of luciferases using deep learning.

Nature
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds, but has been limited by a lack of suitable protein structur...

Using Steady-State Kinetics to Quantitate Substrate Selectivity and Specificity: A Case Study with Two Human Transaminases.

Molecules (Basel, Switzerland)
We examined the ability of two human cytosolic transaminases, aspartate aminotransferase (GOT1) and alanine aminotransferase (GPT), to transform their preferred substrates whilst discriminating against similar metabolites. This offers an opportunity ...

Machine learning modeling of family wide enzyme-substrate specificity screens.

PLoS computational biology
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their nat...

Deep learning allows genome-scale prediction of Michaelis constants from structural features.

PLoS biology
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estima...

Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale.

Nature communications
Mass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with dat...

A Machine Learning Study on the Thermostability Prediction of (R)--Selective Amine Transaminase from Aspergillus terreus.

BioMed research international
Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants i...

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

Molecules (Basel, Switzerland)
The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacologi...