Descripción del proyecto
¿Cuántos socios TANGO puede tener una proteína?
El algoritmo de predicción de agregación TANGO puede examinar secuencias de proteínas en busca de tramos de aminoácidos propensos a formar láminas beta intermoleculares entre sí. Estos tramos de aminoácidos, denominados «regiones propensas a la agregación» (RPA), son factores comprobados de la agregación de proteínas. El equipo del proyecto MANGO, financiado por el Consejo Europeo de Investigación, ampliará el concepto de agregación impulsada por RPA idénticas (agregación homotípica) a las interacciones beta entre RPA similares, pero no idénticas (coagregación o siembra cruzada). Dado que las proteínas son fundamentales para todas las funciones celulares, su agregación contribuye a muchas enfermedades, como el Alzheimer. En MANGO se desarrollarán novedosos algoritmos bioinformáticos para pronosticar con precisión la coagregación y la siembra cruzada con el fin de obtener nuevos conocimientos sobre la fisiopatología de las enfermedades relacionadas con la agregación de proteínas.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
9052 ZWIJNAARDE - GENT
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