Description du projet
Un nouveau cadre pour des prédictions efficaces de co-assemblage de peptides
L’auto-assemblage supramoléculaire, en particulier le co-assemblage peptidique multicomposant, suscite depuis peu un intérêt croissant. Cette technologie est très prometteuse pour les industries basées sur les matériaux, car elle améliore l’utilité fonctionnelle des matériaux à base de peptides. Toutefois, la compréhension de la complexité des matériaux à base de peptides et la prédiction des structures des co-assemblages ont constitué des obstacles majeurs à l’avancement de cette technologie. Le projet SupraModel, financé par l’UE, entend développer un cadre efficace et rapide permettant de prédire les structures de co-assemblage de peptides tout en fournissant des informations sur le processus de co-assemblage. Ce projet établira de nouvelles règles de conception afin d’améliorer l’efficacité des futurs projets.
Objectif
Supramolecular self-assembly is a fundamental process abundantly utilized by nature and emerging functional materials technologies ranging from drug delivery to soft semiconductor devices. Recently, an increased focus has been placed on the multicomponent peptide co-assembly as they often display unique emergent properties that can dramatically expand the functional utility of peptide-based materials. Still, the full potential is hindered by the combinatorial complexity of peptide-based materials and our inability to predict the co-assembled structures and, therefore, properties and functionality. Machine Learning models built on top of Molecular Dynamics simulations are ideally suited to decipher the co-assembly behavior. However, the existing molecular models either suffer from severe approximations disabling them to give accurate predictions or are computationally too expensive to transverse the material space. Addressing this trade-off, I aim to develop a computational framework for fast and accurate peptide co-assembly prediction using as a key strategy a multiscale construction of Graph Neural Network-based models that can predict the peptide co-assembly. This innovative approach will enable me to reach the following objectives: (1) obtain unprecedented molecular insight into the peptide co-assembly process inaccessible to experiments, (2) uncover novel candidate materials, and (3) provide rational design rules for multicomponent peptide-based supramolecular materials. In a broader context, increased insight into cooperative behavior will bring us closer to understanding and ultimately synthetically replicating the exceptional functionality of living systems, while the methodological advancements of data-driven molecular modeling will be of paramount importance in other areas of biomaterial engineering and beyond.
Champ scientifique
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
80333 Muenchen
Allemagne