Topic: AlphaFold

José Molina Mora presents an integrative study of the core stress-response mechanisms, or perturbome, of Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus. Transcriptomic data are analysed using machine learning, systems biology, functional enrichment and ortholog comparisons to prioritise stress-associated genes. Experimentally determined protein structures are combined with AlphaFold predictions where structural data are missing. Molecular docking and further computational analyses then identify preliminary compounds and conditions that might inhibit selected targets. These

Jan Kosinski explains how AlphaFold-style methods predict protein complexes and why their reliance on evolutionary information makes interactions between host and pathogen proteins especially difficult. The talk reviews large-scale studies, interpreting both successful predictions and failures rather than treating reported success rates as universal performance. It then examines ways to improve predictions through broader sampling, altered sequence alignments and experimental restraints, including crosslinking mass spectrometry, with influenza A virus as a worked research example. The session

This webinar uses research on Chagas disease to explain how predicted protein structures can support drug discovery when experimental structures are unavailable. In the example study, AlphaFold models of Trypanosoma cruzi proteins supported a virtual screen of roughly 30,000 compounds; subsequent experiments identified antiparasitic activity in two approved drugs. The session introduces AlphaFold confidence measures and ways to obtain models, then follows a screening workflow through binding-pocket prediction, chemical-library selection, docking, and interpretation of results. It discusses dif