Topic: AlphaFold

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

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