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Related Experiment Video

Updated: May 17, 2025

Uracil-DNA Glycosylase Assay by Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry Analysis

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Published on: April 22, 2022

An Effective Computational Strategy for UGTs Catalytic Function Prediction.

Nianhang Chen¹, Zhennan Jiang², Zhekai Xie¹

¹School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.

ACS Synthetic Biology

|May 16, 2025

Summary

This summary is machine-generated.

Glycosyltransferases (UGTs) are key in natural product synthesis. A new computational method accurately predicts UGT function and substrate specificity, differentiating between similar triterpenoid and steroidal saponins.

Keywords:

ESM2 N-terminal domain PCA glycosyltransferases molecular simulation substrate specificity

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A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants

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Area of Science:

Biochemistry
Structural Biology
Computational Biology

Background:

Glycosyltransferases (GTs) are vital enzymes in synthesizing pharmacologically active natural products like saponins.
Distinguishing substrate specificity among GT-B family enzymes, particularly for structurally similar triterpenoid and steroidal saponins, is challenging.
Understanding enzyme mechanisms and substrate interactions is crucial for drug discovery and enzyme engineering.

Purpose of the Study:

To elucidate the substrate transport mechanism and N-terminal domain (NTD) role in GT-B glycosyltransferases.
To develop a computational method for classifying plant-derived UGTs and predicting their substrate specificity.
To differentiate between UGTs acting on structurally similar triterpenoid and steroidal substrates.

Main Methods:

Molecular dynamics simulations to reveal substrate transport tunnels and mechanisms in PpUGT73CR1.
Analysis of binding pocket residues in 44 plant-derived UGTs.
Development of a fast sequence-based classification method using ESM2 protein model features and PCA clustering.
Experimental validation of the computational predictions.

Main Results:

A plausible substrate transport mechanism for GT-B enzymes was proposed, involving NTD regulation.
Distinct patterns in active site residues were identified for sterol UGTs.
The sequence-based method accurately classified UGTs and differentiated substrate specificity, even for closely related compounds.
Computational predictions were successfully validated through experimental assays.

Conclusions:

The study provides novel insights into GT-B enzyme substrate binding and transport mechanisms.
A computationally efficient and accurate method for predicting UGT function and substrate specificity has been developed.
This work offers a valuable tool for the functional annotation of unknown UGTs and aids in the discovery of novel natural products.