OxTalks Change Freeze Starts 2 March
Oxford Events, the new replacement for OxTalks, will launch on 16th March. The two-week OxTalks freeze period starts on Monday 2nd March. During this time, there will be no facility to publish or edit events. The existing OxTalks site will remain available to view during this period. Once Oxford Events launches, you will need a Halo login to submit events. Full details are available on the Staff Gateway.
Machine learning vs protein conformational spaces
Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. I will discuss how a neural network can learn a continuous conformational space representation from example structures produced by molecular dynamics simulations. I will then show how such representation, obtained via our software molearn (1), can be leveraged to predict putative protein transition states (2), or to generate conformations useful in the context of flexible protein-protein docking (3).
1. github.com/Degiacomi-Lab/molearn
2. V.K. Ramaswamy et al., Learning Protein Conformational Space with Convolutions and Latent Interpolations. Physical Review X (2021).
3. M.T. Degiacomi, Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space. Structure (2019)
Date:
24 November 2022, 15:00
Venue:
Dorothy Crowfoot Hodgkin Building, off South Parks Road OX1 3QU
Venue Details:
20-138 Phase 2 Ground Floor Main Seminar Room
Speaker:
Matteo Degiacomi (Durham University)
Organising department:
Department of Biochemistry
Organiser:
William Rochira (University of Oxford)
Organiser contact email address:
william.rochira@msdtc.ox.ac.uk
Host:
William Rochira (University of Oxford)
Part of:
SBCB Seminar Series
Booking required?:
Not required
Audience:
Members of the University only
Editor:
William Rochira
