Computational Modeling and Design of Oligomer Selective Vaccine Candidates for Parkinson’s and Alzheimer’s Disease

Event Date:
2022-12-02T13:30:00
2022-12-02T15:30:00
Event Location:
Hennings 318
Speaker:
Adekunle Aina, PhD student
Related Upcoming Events:
Grad Theses
Intended Audience:
Public
Event Information:

Protein aggregation-related diseases, in particular neurodegenerative diseases, are characterized by the aberrant perturbation of the underlying protein conformational ensemble. Effectively presenting epitopes using vaccines, to raise conformationally selective antibodies, is a central problem in treating neurodegenerative diseases. Parkinson’s and Alzheimer’s disease, which pathogenesis has been attributed to aberrant aggregates of α-synuclein and tau protein, respectively, are the two most common neurodegenerative diseases. Designing conformationally selective vaccines in silico often requires: 1.) an effective epitope scaffolding strategy that selectively targets pathologic aggregates known as oligomers while sparing the more abundant healthy monomers, with both the oligomeric and the monomeric forms having essentially identical amino acid sequences, and 2.) efficient methods and software for quantitative comparison of large conformational ensembles, which are currently not readily available. 

 

In this thesis, we apply various computational techniques including those based on information theory and principles of physics to address these two challenges. We computationally modeled and designed cyclic peptide and β-helix protein vaccines to best mimic toxic oligomeric conformational ensembles of α-synuclein and tau protein computationally-predicted epitopes, respectively. In both Parkinson’s and Alzheimer’s disease, our designed vaccines are predicted to be conformationally selective for toxic oligomers. Additionally, we developed a new generalized method for efficient representation and comparison of protein conformational ensembles. The method is up to 88 times faster while utilizing 48 times fewer computing cores than the readily available Encore software on a molecular dynamics-generated ensemble dataset. 

 

The methods developed and results presented in this thesis will not only accelerate the process of in silico conformation-specific vaccine design for protein aggregation-related diseases but have potential applications to antibody drug discovery and development in pharmaceutical biotechnology.

Add to Calendar 2022-12-02T13:30:00 2022-12-02T15:30:00 Computational Modeling and Design of Oligomer Selective Vaccine Candidates for Parkinson’s and Alzheimer’s Disease Event Information: Protein aggregation-related diseases, in particular neurodegenerative diseases, are characterized by the aberrant perturbation of the underlying protein conformational ensemble. Effectively presenting epitopes using vaccines, to raise conformationally selective antibodies, is a central problem in treating neurodegenerative diseases. Parkinson’s and Alzheimer’s disease, which pathogenesis has been attributed to aberrant aggregates of α-synuclein and tau protein, respectively, are the two most common neurodegenerative diseases. Designing conformationally selective vaccines in silico often requires: 1.) an effective epitope scaffolding strategy that selectively targets pathologic aggregates known as oligomers while sparing the more abundant healthy monomers, with both the oligomeric and the monomeric forms having essentially identical amino acid sequences, and 2.) efficient methods and software for quantitative comparison of large conformational ensembles, which are currently not readily available.    In this thesis, we apply various computational techniques including those based on information theory and principles of physics to address these two challenges. We computationally modeled and designed cyclic peptide and β-helix protein vaccines to best mimic toxic oligomeric conformational ensembles of α-synuclein and tau protein computationally-predicted epitopes, respectively. In both Parkinson’s and Alzheimer’s disease, our designed vaccines are predicted to be conformationally selective for toxic oligomers. Additionally, we developed a new generalized method for efficient representation and comparison of protein conformational ensembles. The method is up to 88 times faster while utilizing 48 times fewer computing cores than the readily available Encore software on a molecular dynamics-generated ensemble dataset.    The methods developed and results presented in this thesis will not only accelerate the process of in silico conformation-specific vaccine design for protein aggregation-related diseases but have potential applications to antibody drug discovery and development in pharmaceutical biotechnology. Event Location: Hennings 318