#IDIBELLseminars: Structural Bioinformatics and Machine Learning Illuminate Protein Interactions Underpinning Infectious Diseases

Viruses rely on interactions with host proteins to infect, replicate, and evade immunity. The molecular details of these interactions shed light on the underlying biology of viral infection and its phenotypic outcomes. As such, virus–host protein–protein interactions (PPIs) hold great promise not only as therapeutic targets but also as “biomarkers” to help prevent future pandemics.
In this talk, I will discuss how we integrate dry-lab experiments, protein structure information and machine learning to study virus–host PPIs and (i) identify potential host reservoirs, (ii) uncover molecular features that reduce susceptibility to vaccine-induced antibodies, and (iii) predict PPIs that play a key role with viral infection and pathogenesis.

Hosted by Ruth Rodriguez Barrueco – Breast cancer group

Gorka Lasso Cabrera recently joined UCL as an Associate Professor in Infection and Artificial Intelligence, where he leads a research group working at the intersection of computational biology and virology. He completed his PhD at the University of Wales Swansea (UK) and held research positions at CICbioGUNE (Spain), Columbia University (USA), and the Albert Einstein College of Medicine (USA). His research integrates structural bioinformatics, network biology, and machine learning to investigate sequence–structure–function relationships in viral and host proteins that drive infection by (re)emerging zoonotic viruses.