Integrated Chemoproteomics and Machine Learning for Accelerated Anti- Klebsiella Drug Discovery
Ente: WT
Scadenza: 2029-04-01
Importo max: 886.978 EUR
Paese: EU
Descrizione
This project presents an innovative strategy to combat multidrug-resistant Klebsiella infections by accelerating novel antimicrobial drug discovery through a multidisciplinary approach. The central aim is to address the rising challenges of treating Klebsiella infections by developing novel small- molecule antibacterial agents that target an evolutionarily conserved class of bacterial specific bioactive enzymes pivotal for virulence and antibiotic resistance.Using a Fluorophosphonate (FP) activity-based probe, the project will generate a comprehensive targeted enzyme bioactivity profile across diverse Klebsiella isolates. These data will inform the development of a Machine Learning (ML) platform to prioritize selective small-molecule inhibitors that inhibit bacterial-specific hydrolase activity. The ML framework built on hybrid deep learning platform and trained with experimental and public datasets will iteratively improve its predictive power using real- time biological validation, allowing prioritization of lead small-molecule candidates. Selected inhibitors will undergo in vitro and in vivo testing, assessing antimicrobial efficacy, toxicity to human cells, and resistance evolution. This project will establish a scalable and accelerated discovery pipeline, bridging high-throughput chemoproteomics, computational prediction, and functional validation. Additionally, this work will strengthen research capacity in Ghana, addresses a critical need for novel antibiotics in LMIC countries disproportionately affected by antimicrobial resistance.
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