When: 14 November, 2024

Register here: https://indico.ess.eu/event/3694/

Speaker: Andrea Thorn, Institute of Nano-structures and Solid State Physics, Germany

Abstract

Structural biology is key to understanding basic processes of life and a major driver for the development of new therapies. However, molecular structures do not directly result from the experiment, but are merely models which explain the observed data according to a priori knowledge. Consequently, our structures are only as good as our limited understanding of the underlying principles.

In crystallography, the gap between model and reality remains clearly evident in hard to interpret maps and large residual values ("R values"). In cryo-EM, model properties do not align well with the actual specimen. With Artificial Intelligence (AI) based protein fold prediction now revolutionizing the field, it is clear that the application of machine learning could be much wider in scope: biomolecular structure determination and experiments could potentially profit immensely from AI, which may even pave the way to joint analyses of data from different labs and methods, which would significantly advance our understanding of the molecules that govern key biological processes.