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.
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Acknowledgements
The authors would like to thank the NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (Grant No. NHCKFKT2025-2) and the Key Project in National Science Research in Higher Education Institutions of Anhui Province (Grant No. 2022AH051236) for financial support. We also thank all colleagues and collaborators who contributed to this study.
Funding
This project was supported by NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (NHCKFKT2025-2) and Key Project in National Science Research in Higher Education Institutions of Anhui Province (2022AH051236).
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Wang, K., Liu, Y., Wang, X. et al. BactProNET: a structural-mechanistic platform for interpreting target-mediated antimicrobial resistance. Sci Rep (2026). https://doi.org/10.1038/s41598-026-53667-1
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DOI: https://doi.org/10.1038/s41598-026-53667-1
