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Protein Dynamics with Generalized machine-learned potentials

Objective

After breakthroughs in protein sequencing and structure prediction, the accurate and scalable characterization of biomolecular dynamics and function is the next frontier in molecular biology.

This project aims to transform the simulation of macromolecular systems by developing a general-purpose machine-learned coarse-grained (MLCG) model that enables accurate and efficient molecular dynamics (MD) simulations of large biomolecular complexes over biologically relevant timescales.

While all-atom MD remains fundamental for modeling biomolecular processes, its high computational cost and limited scalability restrict its application to small systems or short simulation timescales. Despite decades of effort, a coarse-grained (CG) approach that matches atomistic accuracy while remaining transferable and predictive is still lacking.

Building on a recent key milestone in MLCG research, this project will advance a physics-informed machine learning framework that learns many-body CG potentials from high-resolution simulations and experimental data. The resulting model will be chemically transferable, physically interpretable, and capable of predicting conformational dynamics, free energy changes, and binding affinities across diverse biomolecular systems.

By integrating statistical physics, graph neural networks, and experimental validation, the project will overcome key limitations of current CG models—such as the treatment of long-range interactions—and demonstrate its power through applications to biomedically relevant systems. Our expertise in MLCG combined with ERC support will enable the focused development and dissemination of a scalable biomolecular modeling framework with broad impact in biophysics and beyond.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2025-ADG

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Host institution

FREIE UNIVERSITAET BERLIN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 3 036 526,00
Address
KAISERSWERTHER STRASSE 16-18
14195 Berlin
Germany

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Region
Berlin Berlin Berlin
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Beneficiaries (1)

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