HITS-SIMPLAIX Joint Colloquium Cecilia Clementi: Modeling Protein Dynamics with Machine Learning and Molecular Simulation

By Cecilia Clementi, Einstein Professor Freie Universität Berlin, Department of Physics, Berlin, Germany

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.

Short Bio:

Prof. Dr. Cecilia Clementi is the Einstein Professor of Physics at Freie Universität 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’s 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), and the Hamill Innovation Award (2007 and 2014).

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