Modeling Protein Dynamics With Machine Learning and Molecular Simulation
Event details
| Date | 13.02.2025 |
| Hour | 15:30 › 16:30 |
| Speaker | Prof. Cecilia Clementi, Free University of Berlin (D) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
BIOENGINEERING SEMINAR
Abstract:
The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.
Bio:
Cecilia Clementi is an Italian-American scientist who specialises in the simulation of biomolecules. She is a Professor of Computational Biophysics at the Free University of Berlin. She was previously a Professor of Chemistry at the Rice University and co-director of the National Science Foundation Molecular Sciences Software Institute. From 2017 to 2019 she held an Einstein Foundation fellowship.
Zoom link for attending remotely: https://epfl.zoom.us/j/69492794190
Abstract:
The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.
Bio:
Cecilia Clementi is an Italian-American scientist who specialises in the simulation of biomolecules. She is a Professor of Computational Biophysics at the Free University of Berlin. She was previously a Professor of Chemistry at the Rice University and co-director of the National Science Foundation Molecular Sciences Software Institute. From 2017 to 2019 she held an Einstein Foundation fellowship.
Zoom link for attending remotely: https://epfl.zoom.us/j/69492794190
Practical information
- Informed public
- Free
Organizer
- Prof. Bruno Correia, Laboratory of Protein Design and Immunoengineering (LPDI), Institute of Bioengineering (IBI), École Polytechnique Fédérale de Lausanne (EPFL)
Contact
- Arne Schneuing, Laboratory of Protein Design and Immunoengineering (LPDI), Institute of Bioengineering (IBI), École polytechnique fédérale de Lausanne (EPFL)