Learning the shape of the protein universe
Event details
| Date | 21.11.2022 |
| Hour | 16:00 › 16:30 |
| Speaker | Armita Nourmohammad is an Assistant Professor of Physics and Applied Mathematics at the University of Washington (Seattle), and an Affiliate Investigator at the Fred Hutchinson Cancer Research Center. Dr. Nourmohammad works at the interface of statistical physics and biology and develops theoretical and data driven approaches to study evolutionary processes across scales. Dr. Nourmohammad obtained her Ph.D in 2012 from the University of Cologne, and then joined Princeton University as a James S. McDonnell postdoctoral fellow, where she started working on immunological problems from a biophysical and an evolutionary perspective. In 2017 Dr. Nourmohammad joined the Max Planck Society as Max Planck Research Group Leader and the University of Washington as an Assistant Professor of Physics. Dr. Nourmohammad is a recipient of an NSF CAREER award, NIH MIRA award, and the APS-DBIO Early Career Award. |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Proteins are the machinery of life facilitating the key processes that drive living organisms. The physical arrangement of amino acids dictates how proteins fold and interact with their environment. Recent advances have increased the number of experimentally resolved or computationally predicted tertiary structures, however we still lack a practical understanding of how 3D structure determines the function of a protein. While machine learning has been at the forefront of protein science, the inferred models are often hard to interpret physically. In this talk I will introduce physically motivated machine learning approaches to learn interpretable models of protein micro-environments, reflecting the underlying biophysics. With these models we infer amino acid preferences given a surrounding atomic neighborhood, and predict the impact of evolutionary substitutions in proteins. Our computational approach establishes an interpretable model for how biological function emerges from protein micro-environments. The flexibility and efficiency of this approach also show promise for building generative models to design novel protein structures with desired function.
Practical information
- Informed public
- Free
Organizer
- Prof. Sahand Rahi
Contact
- Sahand Jamal Rahi <[email protected]>