Integrating Structure-Based and Data-Driven Approaches to Achieve Selectivity in Xylella fastidiosa Inhibitors.
Scientific-Disciplinary Group
03/CHEM-07 - Medicinal, Toxicological, Nutraceutical-Food, Fermentation And Health And Wellbeing Products Chemistry
Description
The project aims to computationally optimize an in-house chemical series of small molecules targeting Xylella fastidiosa. These compounds bind a selected protein target and inhibit bacterial growth and biofilm formation but also affect beneficial endophytic bacteria. The objective is to improve selectivity toward Xylella fastidiosa while minimizing off-target effects. The candidate will assess target conservation across Xylella and representative endophytes through sequence and structural analyses, generating AlphaFold models when needed. Structure-and ligand-based approaches, including binding-site analysis, induced-fit docking, molecular dynamics simulations and MD-derived pharmacophore/Dynophore models, will identify selectivity determinants. Computational tools will support data analysis, prioritization and structure-activity relationships (SAR). New analogues will be designed considering xylem mobility and environmental safety, for subsequent synthesis and validation.
Job posting website
Funding body
Università degli Studi di Perugia
How to apply
View the original posting on the MUR website: Go to MUR website