Cracking the code of amyloid polymorphism: Integrating cryoEM and machine learning to unravel impact of small aggregation modulators on amyloid fibril polymorphism

Descripción del proyecto

El código del polimorfismo amiloide en los trastornos neurodegenerativos

Los trastornos neurodegenerativos, como las enfermedades de Alzheimer y Parkinson, se caracterizan por el plegamiento incorrecto de proteínas y la agregación de amiloide y suponen un importante problema sanitario. Sin embargo, aún no se comprende del todo el papel del polimorfismo estructural de las proteínas amiloides. En el proyecto AMI-CryoML, financiado con fondos europeos, se investigarán los moduladores de la agregación amiloide y sus efectos sobre la cinética de agregación y el polimorfismo estructural resultante. El objetivo es recopilar y analizar datos sobre moduladores conocidos, prestando especial atención a sus propiedades químicas y mecanismos de acción. El equipo del proyecto estudiará cómo estos moduladores influyen en diferentes fases de la autoagregación amiloide utilizando ensayos ThT. Además, evaluará su eficacia frente a diversos amiloides, incluidos priones, CsgA y α-sinucleína. Se utilizará la criomicroscopía electrónica para estudiar los polimorfismos estructurales, se creará la base de datos AmyloGraph y se empleará aprendizaje automático para desarrollar modelos predictivos.

Objetivo

Neurodegenerative diseases such as Alzheimer's (AD), Parkinson’s (PD), and prion-related disorders pose major global health challenges due to their progressive nature and lack of effective treatments. Central to these conditions is the misfolding and aggregation of proteins into amyloid structures, which cause severe cellular damage. Despite advances in understanding amyloid aggregation, the role of structural polymorphism in these processes remains underexplored. This proposal aims to address this gap by investigating amyloid aggregation modulators and their effects on aggregation kinetics and polymorphism.

The project has five main objectives:
1. Data Collection on agregation modulators: Systematically gather and curate data on known modulators that impact amyloid aggregation, focusing on their chemical properties and mechanisms.
2. Aggregation kinetics studies: Study the impact of selected modulators on the kinetics of amyloid self-aggregation using ThT assays. This includes analyzing how modulators influence different phases of aggregation and testing their efficacy on a range of amyloids, including prions, CsgA, and α-synuclein.
3. Study structural polymorphisms via CryoEM: Utilize Cryo-Electron Microscopy (CryoEM) to resolve and map the structural polymorphs of amyloids under various conditions. Providing insights into impact of modulators on structural polymorphism and aggregation kinetics.
4. AmyloGraph database enhancement: Develop a new module for the AmyloGraph database to store and analyze data on modulators, aggregation kinetics, and structural polymorphisms.
5. Development of ML models: Leverage machine learning to develop predictive and generative models to forecast amyloid aggregation kinetics and modulator effects, incorporating structural polymorphisms, and generate new modulators targeting specific polymorphic forms.