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VERSION:2.0
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DTSTAMP:20260905T100551Z
UID:a5aee595-b580-4e79-b696-af495e98db8c
DTSTART:20261007T090000Z
DTEND:20261007T170000Z
DESCRIPTION:AlphaFold and related Artificial Intelligence (AI) methods now 
 predict the structures of many protein complexes with near-experimental ac
 curacy\, and they are increasingly used to model how pathogen proteins int
 eract with host proteins. However\, for host-pathogen interactions\, the s
 uccess rate remains low. In this webinar\, we will explain how AlphaFold-s
 tyle methods predict protein complexes\, and why they depend on evolutiona
 ry information that host and pathogen protein pairs largely do not provide
 . We will then go through what recent large-scale studies have achieved\, 
 where the predictions worked and where they failed\, and how the reported 
 success rates should be read. Finally\, we will describe strategies that c
 an improve the results\, including extensive sampling\, modified sequence 
 alignments\, and the use of experimental data such as crosslinking mass sp
 ectrometry\, illustrated with our work on influenza A virus.This event is 
 part of the webinar series “Integrating structural biology and bioinform
 atics to study infection”. You can follow the link for more information 
 about the series and its webinars.
LOCATION:\, 
SUMMARY:AI-based structural modelling of host-pathogen protein interactions
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/ai-based-structural-mod
 elling-host-pathogen-protein-interactions
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