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Rational computational design of epitopes and antibodies for neurodegenerative disease and COVID-19

University of British Columbia

Protein computational design uses current knowledge in structural biology and statistical tools to predict amino acid sequences that exhibit targeted properties. In this study, two protein designs and one statistical tool are developed. The first designed protein family in this thesis is chimeric antibodies for Covid-19 and future coronavirus variants. The chimeric antibodies are composed of an IgG1 framework with "ACE2 units" grafted on complementarity-determining regions. ACE2 units were small protein fragments built around the spike-interacting regions of ACE2. Such a chimeric construct is designed to neutralize SARS-COV-2 by binding spike receptor binding domain and is expected to be tolerant to receptor binding domain (RBD) mutations, as long as ACE2 recognition is required for infection. The binding free energy of ACE2 units to the spike RBD was assessed by molecular dynamics simulation. Surprisingly, the computation result showed that some ACE2 units had similar or even stronger RBD binding than full length ACE2. Moreover, it adds validity to the simulations that the calculated binding free energy between the ACE2 and SARS-COV-2 RBD, -52.9 +- 5.0 kJ/mol, is within the range of the experimental results. The second designed protein in this thesis is an immunogen scaffold design for neurodegenerative disease using cyclic peptides. Effectively scaffolding epitopes on immunogens, in order to raise conformationally selective antibodies through active immunization, is a central problem in treating protein misfolding diseases. We seek to selectively target conformations enriched in toxic, oligomeric propagating species while sparing healthy forms of the protein which are often more abundant. To this end, we scaffolded cyclic peptides by varying the number of flanking glycines, to best mimic a misfolding-specific conformation of an epitope of alpha-synuclein enriched in the oligomer ensemble. The cyclic peptide scaffolds of alpha-synuclein are screened in silico based on their ensemble overlap properties with the fibril, oligomer-model, and isolated monomer ensembles. Lastly, a simulation tool, reservoir replica exchange molecular dynamics simulation (R-REMD), was implemented in GROMACS software. The enhanced sampling of the R-REMD was tested on several systems including a cyclic peptide scaffold whose structural ensemble can predict the selectivity of the raised antibody.

Text

2021-10-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/76380

Physics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/