Physicist Cecilia Clementi at Freie Universität Berlin is set to receive an Advanced Grant worth roughly 3.037 million euros from the European Research Council (ERC) over the course of the next five years for her research project ProDyGe (“Protein Dynamics with Generalized machine-learned potentials.”) By means of the ERC Advanced Grant, the European Union aims to support exceptional researchers with a strong research track record who want to pursue groundbreaking, ambitious projects in new fields of research.

Physicist Cecilia Clementi will receive an ERC Advanced Grant for her research on the simulation of biomolecular dynamics.
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Cecilia Clementi is a professor of theoretical and computational biophysics at Freie Universität Berlin and was appointed to the university as an Einstein Professor. Her research combines methods from statistical physics, computational biophysics, and machine learning to gain a better understanding of complex biological processes on a molecular level.

“Our goal with ProDyGe is to make biomolecular processes that were previously difficult or nearly impossible to simulate predictable, regardless of size and across longer timescales. If we succeed, we will be much better equipped to understand complex biological mechanisms and open up new methods of investigating the effects of different medications or how large molecular machines function. The ERC Advanced Grant gives us the opportunity to pursue a totally novel approach that lies at the intersection of physics, biology, and artificial intelligence,” says Clementi.

New Methods of Simulating Biological Molecules

Proteins and other biomolecules are considered the building blocks of life. The function of proteins are not merely determined by their structures; protein dynamics and interactions actually play an even greater role in terms of their function. While major breakthroughs have been achieved in protein sequencing and structure prediction, the accurate characterization of biomolecular dynamics is considered one of the central challenges in modern molecular biology.

This is where the ERC-funded project ProDyGe comes in. Its primary objective is to develop a universally applicable, machine-learning-based model that makes the simulation of large biomolecular systems significantly more efficient without compromising accuracy. These computer simulations will allow insights into processes that are crucial to understanding health and disease, such as how protein folding, pharmaceutical substance binding, or large molecular complexes work.

To this end, Clementi’s research team will develop a new physics-based machine-learning process that combines information from high-resolution simulations with experimental data. The model will not only be transferable to different biomolecular systems, but will also facilitate the prediction of structural changes, free energy landscapes, and binding affinities among biological molecules.