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Updated: Sep 13, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

RxnCluster: A Web-Based Tool for Exploring Reaction Clusters Leading to Target Molecules by Digitalizing Typical

Shaozhen Ding¹, Yu Tian¹, Dongliang Liu¹

¹Department of Chemical and Biomolecular Engineering, School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

ACS Synthetic Biology

|July 29, 2025

Summary

This summary is machine-generated.

This study introduces RxnCluster, a novel platform for digitalizing biosynthetic patterns. It identifies multi-step reaction pathways for molecule biosynthesis, aiding in discovering new synthetic strategies.

Keywords:

biosynthesis big data reaction clusters typical biosynthetic patterns

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Last Updated: Sep 13, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

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Published on: March 12, 2020

Optimization of Radiochemical Reactions using Droplet Arrays

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Published on: February 12, 2021

From a Natural Product to Its Biosynthetic Gene Cluster: A Demonstration Using Polyketomycin from Streptomyces diastatochromogenes Tü6028

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Published on: January 13, 2017

Area of Science:

Biotechnology
Computational Chemistry
Synthetic Biology

Background:

Biosynthetic pathways are sequential reactions crucial for molecule production.
Current in silico methods often overlook multi-step synthetic strategies.
Molecular structures can be divided into substructures, suggesting similar biosynthetic approaches.

Purpose of the Study:

To develop a user-friendly platform for digitalizing typical biosynthetic patterns.
To enable the identification of multi-step reaction pathways for molecule biosynthesis.
To facilitate pathway mining for novel biosynthetic strategies.

Main Methods:

Constructed a platform named RxnCluster based on gene cluster concepts.
Digitalized typical biosynthetic patterns into reaction rule clusters.
Developed a database containing 14,378 biosynthetic patterns and 37,317 reaction combinations.

Main Results:

RxnCluster successfully identifies reaction clusters with 1 to 4 steps.
Identified pathways align with experimental findings from wet laboratories.
The platform also reveals novel, previously unreported reaction clusters.

Conclusions:

RxnCluster represents a novel approach to pathway mining for molecule biosynthesis.
The platform facilitates the discovery of diverse and efficient synthetic strategies.
This tool has the potential to accelerate the identification of new biosynthetic pathways.