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PreprintCC BY-NC-NDChemRxiv

AI-Generated Peptide Vectors: Advancing Fluorescent Detection of Bacteria

  • Anthony Augé1
  • Evgeniia Milova2
  • Alexey Orlov2
  • Hugo Debelvalet1
  • Sergio Ortiz1
  • Dmytro Dziuba3
  • Dragos Horvath2
  • Alexandre Varnek2,4
  • Julie Karpenko1,5
5 affiliations
  1. 1Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS/Université de Strasbourg, Strasbourg Drug Discovery and Development Institute (IMS)
  2. 2Chimie de la Matière Complexe, UMR 7140 CNRS/Université de Strasbourg
  3. 3Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS/Université de Strasbourg
  4. 4Institute for Chemical Reaction Design and Discovery (WPI-ICReDD, Hokkaido University, Kita 21, Sapporo, Hokkaido, Japan, 001-0021
  5. 5Institut Universitaire de France (IUF)
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Abstract

Molecular probes derived from antimicrobial peptides are powerful tools for diagnosing bacterial infectious diseases and guiding antibiotic therapy. However, existing probes rely on a limited repertoire of natural antimicrobial peptides, leaving the vast sequence space of linear peptides largely unexplored. Here, we used explainable artificial intelligence to design AIGP-3, a de novo peptide vector for fluorescent probes. Conjugated to Nile Red, NR-AIGP-3 exhibits high selectivity for Gram-positive bacteria, combining the environmental sensitivity of the fluorophore with the strong membrane binding of the vector and favorable MIC values for live-cell imaging. NR-AIGP-3 outperforms a ubiquicidin-derived probe in both flow cytometry and confocal microscopy, demonstrating its high potential for developing molecular probes for the diagnosis of bacterial infections.

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Cite

BibTeXRIS
BibTeX
@misc{auge2026aigenerated,
  title = {{AI-Generated Peptide Vectors: Advancing Fluorescent Detection of Bacteria}},
  author = {Anthony Augé and Evgeniia Milova and Alexey Orlov and Hugo Debelvalet and Sergio Ortiz and Dmytro Dziuba and Dragos Horvath and Alexandre Varnek and Julie Karpenko},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15009498/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009498/v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - AI-Generated Peptide Vectors: Advancing Fluorescent Detection of Bacteria
AU  - Anthony Augé
AU  - Evgeniia Milova
AU  - Alexey Orlov
AU  - Hugo Debelvalet
AU  - Sergio Ortiz
AU  - Dmytro Dziuba
AU  - Dragos Horvath
AU  - Alexandre Varnek
AU  - Julie Karpenko
PY  - 2026
DA  - 2026/09/27
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009498/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009498/v1
AB  - Molecular probes derived from antimicrobial peptides are powerful tools for diagnosing bacterial infectious diseases and guiding antibiotic therapy. However, existing probes rely on a limited repertoire of natural antimicrobial peptides, leaving the vast sequence space of linear peptides largely unexplored. Here, we used explainable artificial intelligence to design AIGP-3, a de novo peptide vector for fluorescent probes. Conjugated to Nile Red, NR-AIGP-3 exhibits high selectivity for Gram-positive bacteria, combining the environmental sensitivity of the fluorophore with the strong membrane binding of the vector and favorable MIC values for live-cell imaging. NR-AIGP-3 outperforms a ubiquicidin-derived probe in both flow cytometry and confocal microscopy, demonstrating its high potential for developing molecular probes for the diagnosis of bacterial infections.
ER  -