AI-based structural modelling of host-pathogen protein interactions
Abstract
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 is suitable for students and researchers interested in host-pathogen interactions and structural bioinformatics; basic knowledge of protein structures and sequence alignments is useful, but prior AlphaFold experience is unnecessary.
Topics
Related Seminars
From transcriptomics to structural modelling with AlphaFold: analysis of the central response to stress in bacteria using machine learning and structural bioinformatics
More on alphafold and structural bioinformatics
Exploring proteins in infection biology with EMBL-EBI resources
Related research
Automated X-ray fragment and ligand screening for therapeutic innovation in infection biology
Related research