Published June 22, 2026 | Version 2.0.0
Dataset Open

PanRes: A database of latent and acquired antimicrobial resistance allowing 3D-based protein homology search

Description

PanRes is a structure-informed antimicrobial resistance database that integrates resistance genes, protein translations, predicted protein structures, structure-based clusters, multiple sequence alignments, and 3D-based Hidden Markov Models. 

The database can also be explored through the PanRes web service: https://panres.rambio.dk/ 

PanRes (version 2.0) includes the following files:

File Description
panres2_genes.fa FASTA file containing nucleotide sequences of PanRes genes.
panres2_proteins.faa FASTA file containing translated protein sequences.
PanGenes.tsv Metadata table for PanRes genes, including database origin and ontology relationships.
PanProteins.tsv Metadata table for PanRes proteins, including links to genes, clusters, and structures where available.
PanStructures.tsv Metadata table for predicted structures and structure-cluster membership.
README_metadata.md Description of the metadata tables.
panres2_ontology.owl OWL ontology file describing the PanRes data model and relationships.
README_ontology.md Description of the PanRes ontology, including the main classes, object properties and annotation fields.
PDBs.zip Predicted AlphaFold protein structures in PDB format, for example PAN1_struct.pdb.
MSAs.zip Multiple sequence alignments for PanStructureClusters, used to build the HMM profiles, for example PANCL1_struct.full.aln.fasta.
HMMs.zip Profile Hidden Markov Models for PanStructureClusters, for example PANCL1_struct.hmm.

 

A number of previously published collections of AMR genes were used in the creation of the original PanRes gene collection bundled with ARGprofiler(See references):

  • ResFinder (downloaded 2023-01-20, (Bortolaia et al. 2020)),
  • ResFinderFG (version 2.0, (Gschwind et al. 2023))
  • CARD (version 3.2.5, (Alcock et al. 2023))
  • MegaRes (version 3.0.0, (Bonin et al. 2023))
  • AMRFinderPlus (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))
  • ARGANNOT (V6_July2019, (Gupta et al. 2014))
  • The 'CsabaPal' collection (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))
  • BacMet (version 1.1, (Pal et al. 2014))

Files

README_metadata.md

Files (127.6 MB)

Name Size Download all
md5:7a36a046aecc549b0840471adec08579
6.7 MB Preview Download
md5:e591d1de7cab4d506e06ecf0a6bec05c
1.2 MB Preview Download
md5:7ef5b80b130efe9bbe6327fd1d1802d7
2.0 MB Download
md5:eb5611270e4b6e24457dc30a38a370da
391.8 kB Download
md5:57a102be4591091adcdd673a5313dcd4
11.3 MB Download
md5:597c533491d560abff4ce553cf6c1f0e
30.1 MB Download
md5:77d1a725eeb19d5499b3886a49f39485
3.7 MB Download
md5:857bf52e3f4d18c705f8d7c35889b29d
76.8 kB Download
md5:9a52c9b27dd6e6d107f9c8442f17a240
72.1 MB Preview Download
md5:3d4e8c181bcca991843aad2a05dbdbfe
3.2 kB Preview Download
md5:eafa1d4cb3d32e27daa06083614ed70d
6.9 kB Preview Download

Additional details

Related works

Is published in
Preprint: https://doi.org/10.64898/2026.06.22.733705 (Other)

Funding

Novo Nordisk Foundation
MULTIBIOMINE NNF24SA0094147

Software

Repository URL
https://github.com/rambiolab/PanResOntology/

References

  • Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 51, D690–D699 (2022).
  • Bonin, N. et al. MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing. Nucleic Acids Res. 51, D744–D752 (2023).
  • Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75, 3491–3500 (2020).
  • Daruka, L. et al. ESKAPE pathogens rapidly develop resistance against antibiotics in development in vitro. Nat. Microbiol. 10, 313–331 (2025).
  • Feldgarden, M. et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci. Rep. 11, 12728 (2021).
  • Gschwind, R. et al. ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Nucleic Acids Res. 51, W493–W500 (2023).
  • Gupta, S. K. et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother. 58, 212–220 (2014)
  • Pal, C., Bengtsson-Palme, J., Rensing, C., Kristiansson, E. & Larsson, D. G. J. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res. 42, D737–D743 (2014).
  • Martiny, H.-M. et al. ARGprofiler—a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets. Bioinformatics 40, btae086 (2024)