Cecilia Clementi - Materials Seminar

Modelling Protein Dynamics with Machine Learning, Molecular Simulation, and Experimental Data

Cecilia Clementi 
Einstein Professor
Department of Physics, Freie Universität Berlin

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 multiscale 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.

Prof. Dr. Cecilia Clementi is the Einstein Professor of Physics at FU Berlin since 2020, after 19 years as a Professor of Chemistry at Rice University in Houston, Texas, USA. Clementi obtained her Ph.D. in Physics at SISSA, Italy, and was a postdoctoral fellow at the University of California, San Diego. Her research focuses on the development and application of methods for the modeling of complex biophysical processes, by means of molecular dynamics, statistical mechanics, coarse-grained models, experimental data, and machine learning. Clementi is a fellow of the American Physical Society and of the Biophysical Society. Her research has been recognized by a US National Science Foundation (NSF) CAREER Award (2004), the Robert A. Welch Foundation Norman Hackerman Award in Chemical Research (2009), the Hamill Innovation Award (2007 and 2014), and a recent ERC Advanced grant (2027-2031).

Host: Professor Laura Gagliardi