Projektbeschreibung
Wie viele TANGO-Partner kann ein Protein haben?
Der TANGO-Algorithmus zur Aggregationsvorhersage kann Proteinsequenzen nach Aminosäureabschnitten scannen, die dazu neigen, miteinander zwischenmolekulare β-Faltblätter zu formen. Diese Aminosäureabschnitte, auch als zu Aggregation neigende Regionen bekannt, beschleunigen nachweislich die Proteinaggregation. Das über den Europäischen Forschungsrat finanzierte Projekt MANGO wird das Konzept der Aggregation, die durch identische zu Aggregation neigende Regionen angetrieben wird (homotypische Aggregation), auf β-Interaktionen zwischen ähnlichen aber nicht gleichen zu Aggregation neigenden Regionen (Ko-Aggregation oder Cross-Seeding) ausweiten. Proteine spielen bei allen Zellfunktionen eine wichtige Rolle, weshalb die Proteinaggregation zu vielen Krankheiten beiträgt, darunter der Alzheimer-Krankheit. Über MANGO werden neue Bioinformatikalgorithmen aufgestellt, um die Ko-Aggregation und das Cross-Seeding vorherzusagen und so neue Einblicke in die Pathophysiologie von Krankheiten, die mit Proteinaggregation in Verbindung gebracht werden, zu erhalten.
Ziel
Amyloid-like protein aggregation is a process of protein assembly via the formation of intermolecular β-structures by short aggregation prone sequence regions. This occurs as an unwanted side-reaction of impaired protein folding in disease, but also for the construction of natural nanomaterials. Aggregates appear to be strongly enriched in particular proteins, suggesting that the assembly process itself is specific, but the cross-seeding of the aggregation of one protein by aggregates of another protein has also been reported.
The key question that I aim to address in this proposal is how the beta-interactions of the amino acids in the aggregate spine determine the trade-off between the specificity of aggregation versus cross-seeding. To this end, I will determine the energy difference between homotypic versus heterotypic interactions and how differences in sequence translate into energy gaps. Moreover, I will analyse the sequence variations of aggregation prone stretches in natural proteomes to understand the danger of widespread co-aggregation.
To achieve these outcomes, I will study the interactions and cross-seeding of aggregating proteins and model peptides in vitro and in cells. I will extract the sequence and structural determinants of co-aggregation, and employ these to construct novel bioinformatics algorithm that can accurately predict co-aggregation and cross-seeding. I will use these to analyse co-aggregation cascades in natural proteomes looking for mechanisms that protect them from wide-spread cross-seeding.
This work will have a significant impact on the understanding of the downstream effects of protein aggregates and may reveal co-aggregation networks in human diseases such as the major neurodegenerative diseases or cancer, potentially opening up new research lines on the mechanisms underlying these pathologies and thus identify targets for novel therapies.
Wissenschaftliches Gebiet
- medical and health sciencesbasic medicineneurologydementiaalzheimer
- natural sciencescomputer and information sciencescomputational science
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsprotein folding
- medical and health sciencesbasic medicinepathology
- natural sciencesmathematicsapplied mathematicsstatistics and probability
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-COG - Consolidator GrantGastgebende Einrichtung
9052 ZWIJNAARDE - GENT
Belgien