Description du projet
Combien de partenaires TANGO une protéine peut-elle avoir?
L’algorithme de prédiction de l’agrégation TANGO est capable d’analyser des séquences de protéines afin d’identifier les acides aminés susceptibles de former des feuillets bêta intermoléculaires les uns avec les autres. Ces tronçons d’acides aminés, appelés régions favorables à l’agrégation (aggregation-prone regions, APR), constituent des moteurs avérés de l’agrégation des protéines. Financé par le Conseil européen de la recherche, le projet MANGO élargira le concept de l’agrégation induite par des APR identiques (agrégation homotypique) aux interactions bêta entre des APR similaires mais non identiques (coagrégation ou ensemencement croisé). Les protéines étant essentielles à toutes les fonctions cellulaires, leur agrégation joue un rôle dans de nombreuses pathologies, comme la maladie d’Alzheimer. MANGO concevra de nouveaux algorithmes bio-informatiques pour prédire avec précision la coagrégation et l’ensemencement croisé afin d’acquérir de nouvelles connaissances sur la physiopathologie des maladies liées à l’agrégation des protéines.
Objectif
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.
Champ scientifique
- 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
Programme(s)
Régime de financement
ERC-COG - Consolidator GrantInstitution d’accueil
9052 ZWIJNAARDE - GENT
Belgique