This Thursday, 25.11 at 11.00 am we will have a FH seminar where Dr. Dina Schneidman from the Hebrew University of Jerusalem will give a lecture titled "Integrative Structure Modeling of Protein Assemblies in the Age of Deep Learning". See the details in the file attached.

Integrative structure modeling is often used to characterize structures and dynamics of large
macromolecular assemblies by relying on multiple types of input information. The individual
proteins or domains are represented by atomic resolution structures or low-resolution sphere
models and data from a variety of sources, such as cross-linking mass spectrometry, cryo-
Electron Microscopy, Small Angle x-ray scattering is used to assemble the subunits. Recent
progress in protein folding enabled by deep learning by AlphaFold2 and RosettaFold provided
an improved structural coverage for domains, and even protein-protein interactions used in
Integrative Structure Modeling. However, these methods depend on multiple sequence
alignment, that is not available for immune response complexes, such as antibody-antigen
interactions. Recently, we began utilizing deep learning approaches for a range of integrative
modeling tasks, including development of scoring functions for prediction of protein-protein
or protein-peptide interactions, modeling and docking of antibodies and nanobodies to the
antigens, binding sites identification, and learning scoring functions for experimental data. I
will describe the progress and current challenges in deep learning applications for modeling of
complexes.