PreprintArticleVersion 1This version is not peer-reviewed
Application of Molecular Modeling and Machine Learning Models to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections
Version 1
: Received: 4 September 2024 / Approved: 5 September 2024 / Online: 5 September 2024 (12:36:13 CEST)
How to cite:
Samanta, P.; Doerksen, R. J. Application of Molecular Modeling and Machine Learning Models to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections. Preprints2024, 2024090437. https://doi.org/10.20944/preprints202409.0437.v1
Samanta, P.; Doerksen, R. J. Application of Molecular Modeling and Machine Learning Models to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections. Preprints 2024, 2024090437. https://doi.org/10.20944/preprints202409.0437.v1
Samanta, P.; Doerksen, R. J. Application of Molecular Modeling and Machine Learning Models to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections. Preprints2024, 2024090437. https://doi.org/10.20944/preprints202409.0437.v1
APA Style
Samanta, P., & Doerksen, R. J. (2024). Application of Molecular Modeling and Machine Learning Models to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections. Preprints. https://doi.org/10.20944/preprints202409.0437.v1
Chicago/Turabian Style
Samanta, P. and Robert J. Doerksen. 2024 "Application of Molecular Modeling and Machine Learning Models to Predict Novel FmlH-Binding Glycomimetics with Improved Pharmacokinetic Properties for the Prevention of Urinary Tract Infections" Preprints. https://doi.org/10.20944/preprints202409.0437.v1
Abstract
Urinary tract infections (UTIs) affect nearly 50% of women in their lifetime. Uropathogenic Escherichia coli (UPEC) expresses F9/Fml pili tipped with protein FmlH that specifically bind to terminal galactoside and galactosaminoside units in glycoproteins in kidney and bladder cells and colonize host tissues. Traditional UTI treatment using excessive antibiotics has led to the rise of 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 have used computer-aided drug design techniques to identify novel glycomimetics that are predicted to bind strongly to UPEC FmlH and inhibit binding to host glycoproteins. We have performed in-silico receptor-based and ligand-based scaffold hopping, and molecular docking to predict novel FmlH-binding glycomimetics with high chemical synthesizability. We have replaced the two major scaffolds of the most potent known FmlH-binding ligand to obtain novel compounds. Additionally, we have applied global machine-learning models to predict ADMET properties of the molecules. Compounds with calculated low ADMET risks were subjected to molecular dynamics simulations and 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.
Chemistry and Materials Science, Medicinal Chemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
