Applying Molecular Modeling to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections

Abstract

Urinary tract infections (UTIs) affect nearly 50% of women in their lifetime. Uropathogenic Escherichia coli (UPEC) expresses F9/Fml pili tipped with the protein FmlH that specifically bind to terminal galactoside and galactosaminoside units in glycoproteins on kidney and bladder cells and colonize host tissues. The traditional UTI treatment using excessive antibiotics has led to the rise in various UPEC antibiotic-resistant strains. An alternative therapeutic approach prevents the initial bacterial attachment on the host cells using competitive FmlH-binding inhibitors. In this study, we used computer-aided drug design techniques to identify novel glycomimetics that are predicted to bind strongly to and inhibit the UPEC FmlH. We performed in silico receptor-based and ligand-based scaffold hopping, and molecular docking to predict novel FmlH-binding glycomimetics with high chemical synthesizability. We replaced the two major scaffolds of the most potent known FmlH-binding ligand to obtain novel compounds. Additionally, we applied global machine-learning models to predict the ADMET properties of the molecules. Compounds with low ADMET risks were subjected to molecular dynamics simulations and a detailed investigation of the FmlH–glycomimetic interactions was performed. We have prepared and supplied a library of 58 novel glycomimetics that can be subjected to further biological activity studies.

Keywords

• molecular docking
• molecular dynamics
• toxicity
• ADMET
• matched molecular pairs