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Antibiotic Dereplication Using the Antibiotic Resistance Platform
10:49

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Published on: October 17, 2019

BactProNET: a structural-mechanistic platform for interpreting target-mediated antimicrobial resistance.

Ke Wang1, Yiru Liu1, Xue Wang1

  • 1School of Clinical Medicine, Wannan Medical University, Wuhu, 241002, China.

Scientific Reports
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

BactProNET is a new bioinformatics platform that analyzes mutations in antibiotic targets to understand antimicrobial resistance (AMR). It provides structural and evolutionary insights to aid in designing new drugs to combat resistant bacterial infections.

Keywords:
Amino acid mutationAntibiotic resistanceBactProNETDatabaseMolecular dockingTarget proteinTarget-mediated resistance

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Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

Published on: March 3, 2023

Area of Science:

  • Bioinformatics
  • Structural Biology
  • Drug Discovery

Background:

  • Antimicrobial resistance (AMR) is a major threat to public health, largely driven by mutations in bacterial protein targets.
  • Existing AMR databases focus on gene identification, lacking detailed structural and mechanistic insights into target-mediated resistance.

Purpose of the Study:

  • To develop BactProNET, a bioinformatics platform specifically designed for analyzing target-mediated AMR caused by amino acid substitutions in antibiotic target proteins.
  • To provide structural and evolutionary analysis distinct from broader AMR databases, facilitating mechanistic inference.

Main Methods:

  • Integrated curated resistance mutations with AlphaFold2-predicted 3D structures of wild-type and mutant proteins.
  • Generated molecular docking models to establish an "optimal binding" baseline and assess disruption by resistance mutations.
  • Incorporated phylogenetic and sequence alignment views for comprehensive analysis.

Main Results:

  • BactProNET integrates data on 44 bacterial species, 107 protein targets, 196 mutation sites, 323 antibiotics, and extensive docking models.
  • The platform offers structural and evolutionary analysis, including BLAST and MSA tools.
  • Provides a multi-level data integration establishing a wild-type reference system for comparative analysis.

Conclusions:

  • BactProNET serves as a comprehensive resource for understanding the molecular mechanisms of target-mediated AMR.
  • Facilitates the interpretation of resistance mechanisms and provides a foundation for designing next-generation antimicrobial drugs.