Everyone is invited to a special ISMB seminar given by Dr Thomas Löhr from the Molecular AI group at AstraZeneca. This seminar is hosted by Gabriella Heller.
Speaker: Dr Thomas Löhr
Title: Computational tools to study disordered proteins, small molecules, and their interactions
Date & Time: 19th June, 3-4pm
Location: Anatomy Building, Room 249
Abstract: Disordered proteins and regions are highly prevalent in the human proteome, and are often implicated in disease. However, methods to study these systems in detail are lacking, and the potential for thermodynamic and kinetic characterisation using experimental methods is limited. Molecular simulations and associated analysis methods have advanced to the point where investigating disordered proteins and their interactions with other (bio-) molecules on an atomistic scale is now possible. I will first talk about the use of integrative structural methods to study systems ranging from small disordered peptides to large amyloid fibril fragments using data from nuclear magnetic resonance and cryo-EM. By combining a Bayesian approach (Metainference) with enhanced sampling techniques (Metadynamics) we are able to efficiently acquire a conformational ensemble of systems that would otherwise remain elusive. Next, I will present work to determine the kinetics of Amyloid-β 42, an aggregation-prone biomolecule implicated in Alzheimer’s disease, and its interactions with small molecules. By dynamically binding to the disordered monomeric state of the protein, a drug-like molecule can slow downstream aggregation processes, demonstrating the feasibility of directly drugging dynamic biomolecules. This was accomplished using ultra-long timescale molecular dynamics simulations combined with a deep-learning based Markov model approach. Finally, I will explain ongoing efforts to integrate molecular dynamics and similar approaches into automated drug discovery pipelines to improve our coverage of chemical space and make the design-make-test-analyze cycle more efficient by guiding small molecule generative models.
