Descrizione del progetto
Un nuovo quadro per effettuare previsioni efficienti sul co-assemblaggio dei peptidi
Recentemente l’interesse verso l’autoassemblaggio supramolecolare, e in particolare il co-assemblaggio dei peptidi multicomponente, è andato crescendo. Questa tecnologia dimostra grandi potenzialità nelle industrie basate sui materiali in quanto migliora l’utilità funzionale dei materiali a base di peptidi. Ciononostante, la comprensione della complessità dei materiali a base di peptidi e la previsione delle strutture di co-assemblaggio hanno notevolmente ostacolato il progresso di questa tecnologia. Il progetto SupraModel, finanziato dall’UE, si propone di sviluppare un quadro efficiente e rapido per la previsione delle strutture di co-assemblaggio dei peptidi, fornendo al contempo indizi sul processo del co-assemblaggio stesso. Il progetto stabilirà nuove regole di progettazione per migliorare l’efficienza dei progetti futuri.
Obiettivo
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.
Campo scientifico
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
80333 Muenchen
Germania