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CORDIS

Protein Dynamics with Generalized machine-learned potentials

Obiettivo

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.

Campo scientifico (EuroSciVoc)

Parole chiave

Programma(i)

Argomento(i)

Meccanismo di finanziamento

HORIZON-ERC - HORIZON ERC Grants

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Invito a presentare proposte

(si apre in una nuova finestra) ERC-2025-ADG

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Istituzione ospitante

FREIE UNIVERSITAET BERLIN
Contributo netto dell'UE
€ 3 036 526,00
Indirizzo
KAISERSWERTHER STRASSE 16-18
14195 Berlin
Germania
Regione
Berlin Berlin Berlin
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Costo totale
Nessun dato

Beneficiari (1)