Evidence map›Paper›PMID 42162213›Full record

ArticleScientific reports2026

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

Ke Wang, Yiru Liu, Xue Wang, Haitao Zhou, Longfei Chen, Jingyun Xu

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ke Wang *School of Clinical Medicine, Wannan Medical University, Wuhu, 241002, China.
Yiru Liu *School of Clinical Medicine, Wannan Medical University, Wuhu, 241002, China.
Xue WangSchool of Clinical Medicine, Wannan Medical University, Wuhu, 241002, China.
Haitao ZhouSchool of Clinical Medicine, Wannan Medical University, Wuhu, 241002, China.
Longfei ChenSchool of Basic Medical Sciences, Wannan Medical University, Wuhu, 241002, China. chenlf2019@zju.edu.cn.
Jingyun XuSchool of Basic Medical Sciences, Wannan Medical University, Wuhu, 241002, China. xujingyun@wnmc.edu.cn.

Funding

Key Project in National Science Reserach in High Education Insititutions of Anhui Province 2022AH051236NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) NHCKFKT2025-2
6 · The paper itself

Abstract

Antibiotics are the frontline therapy for bacterial infections, yet their efficacy is critically threatened by antimicrobial resistance (AMR), a crisis largely driven by mutations in protein targets. While this mechanism is prevalent, data on its structural impact remains highly dispersed, and existing resources such as CARD, ResFinder, MEGARes, and NDARO prioritize gene identification over mechanistic insight. To address this gap, we developed BactProNET, a bioinformatics platform focused specifically on target-mediated resistance caused by amino acid substitutions in antibiotic target proteins. It provides structural and evolutionary analysis distinct from broader AMR databases. Its core innovation is a multi-level data integration that establishes a wild-type reference system and enables comparative analysis with mutant proteins to facilitate mechanistic inference. The platform integrates curated resistance mutations with AlphaFold2-predicted three-dimensional (3D) structures, both wild-type and mutant molecular docking models defining an "optimal binding" baseline and its potential disruption by resistance mutations, and integrated phylogenetic and sequence alignment views. BactProNET currently houses data on 44 bacterial species, 107 protein targets, 196 mutation sites (representing 243 unique amino acid substitutions), 323 antibiotics, and 640 wild-type plus 1,697 mutant docking models. The platform is accessible via an interface with embedded basic local alignment search tool (BLAST) and multiple sequence alignment (MSA) tools. As a one-stop platform for target-mediated resistance, it facilitates the interpretation of AMR's molecular mechanisms and provides a data-driven foundation for the rational design of next-generation antimicrobial drugs.

Indexed as

Anti-Bacterial AgentsBacteriaBacterial ProteinsComputational BiologyDrug Resistance, BacterialSoftwareAmino Acid SubstitutionMolecular Docking SimulationMutationPhylogenyAnti-Bacterial AgentsBacterial ProteinsAmino acid mutationAntibiotic resistanceBactProNETDatabaseMolecular dockingTarget-mediated resistanceTarget protein

Identifiers

PMID42162213
PMCPMC13392442

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.