Seminar "Geometric deep learning for functional protein design"
Speaker: Michael Bronstein, Professor, Chair of Machine Learning and Pattern Recognition, Imperial College London / Head of Graph Learning Research, Twitter
Protein-based drugs are becoming some of the most important drugs of the XXI century. The typical mechanism of action of these drugs is a strong protein-protein interaction (PPI) between surfaces with complementary geometry and chemistry. Over the past three decades, large amounts of structural data on PPIs has been collected, creating opportunities for differentiable learning on the surface geometry and chemical properties of natural PPIs. Since the surface of these proteins has a non-Euclidean structure, it is a natural fit for geometric deep learning, a novel class of machine learning techniques generalizing successful neural architectures to manifolds and graphs. In the talk, I will show how geometric deep learning methods can be used to address various problems in functional protein design such as interface site prediction, pocket classification, and search for surface motifs. I will present results of our ongoing work with Bruno Correia, Pablo Gainza-Cirauqui, and others from the EPFL Lab of Protein Design and Immunoengineering.
Location: room 319, Bolshoy Tryokhsvyatitelsky Pereulok, 3 (Kitai-Gorod Station)
Seminar working language – English