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MARMOTTE

Financiado

Tessellation-based analysis of dynamic protein structures and their complexes -...

Tessellation-based analysis of dynamic protein structures and their complexes - MoleculAR MOTions meet TEssellations (MARMOTTE). This year has seen a breakthrough in structural bioinformatics - deep learning-based methods, most notably Google DeepMind's AlphaFold2, have demonstrated near-experimental accuracy of protein structure predictions. However, even... ver más

31/08/2025

CNRS

196K€

Presupuesto del proyecto: 196K€

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-08-02

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021

I+D

Cerrada hace 3 años

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2 Participantes

CNRS

195.91K€ | Lider

EPFL

N.D. | Participante

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Duración del proyecto: 36 meses Fecha Inicio: 2022-08-02
Fecha Fin: 2025-08-31

Presupuesto del proyecto 196K€

Descripción del proyecto This year has seen a breakthrough in structural bioinformatics - deep learning-based methods, most notably Google DeepMind's AlphaFold2, have demonstrated near-experimental accuracy of protein structure predictions. However, even the best protein structure prediction methods do not automatically provide knowledge about protein dynamics and protein interactions, which is often essential to understand or predict the biological functions of proteins. Those functions are performed via intermolecular interactions, and such interactions almost always involve conformational changes of engaged partners. The problem of modeling dynamic protein structures and their complexes is still largely unsolved - this project aims to significantly contribute towards its future solution by exploring the link between computational geometry, statistical physics, and machine learning. The postdoctoral researcher will develop novel methods that: given a dynamic (moving) molecular structure, efficiently compute tessellation-derived contact areas; given a starting structure and its tessellation-derived contacts areas, predict (using a graph neural network) how the interatomic contact areas will change upon motion; given a protein complex model generated by docking, use the predicted statistical properties of the contact areas to estimate (using a graph neural network) the protein-protein binding energy score. If successfully developed, such methods will provide unique data about the dynamics of tessellation-derived interatomic contact areas. Most importantly, they will provide effective dynamics-aware scores for assessing and ranking structural models of protein complexes.

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