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Drug Design and Pipeline Development for Proteinopathies
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Autores
Resumo(s)
Neurodegenerative diseases like Alzheimer’s and Parkinson’s present significant challenges in modern
medicine due to the progressive degeneration of neuronal cells, leading to severe cognitive and motor
impairments. A key feature of these disorders is protein aggregation, which disrupts cellular functions
and causes neuronal death. In Parkinson’s disease (PD), the aggregation of alpha-synuclein (α-syn), and
in Alzheimer’s, amyloid beta (Aβ) and tau, are critical molecular events.
Understanding the structural and dynamic changes driving protein aggregation is crucial for developing therapeutic interventions. These diseases share aberrant protein conformations that promote seed
aggregation, with misfolded proteins forming toxic aggregates that contribute to neuronal dysfunction
and cell death.
In this study, we utilized molecular simulations and structural descriptors to investigate α-syn behavior and identify potential drugs. We combined molecular dynamics (MD) simulations with the ProtT5
model to explore the ability of cyclic peptides to bind to specific regions of α-syn and their impact on the
protein’s conformational space.
Our approach involved two key steps: first, using MD simulations to examine α-syn conformational
changes in the presence of cyclic peptides, and second, employing ProtT5 to generate embeddings for
efficiently estimating the radius of gyration (Rg) values of α-syn with new peptides. The random forest
regression model predicted Rg values, reducing the need for costly molecular simulations.
Additionally, we developed a Python script for peptide selection in future investigations. Although
this tool shows promise in the peptide selection process, it requires further refinement due to the small
dataset size, which currently limits the robustness of predictions.
This study improves our understanding of protein-cyclic peptide interactions, potentially leading to
new treatment options that target α-syn aggregation pathways. Our results help to advance the greater
objective of identifying effective therapies for neurodegenerative diseases, using fast and cheaper in silico
methods.
Descrição
Tese de mestrado, Bioinformática e Biologia Computacional, 2024, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Alterações Conformacionais de Proteínas α-syn Simulações de Dinâmica Molecular Algoritmos de aprendizagem automática Inteligência Artificial (IA) Teses de mestrado - 2024