Optimizing Epitope Conformational Ensembles Using α‑Synuclein Cyclic Peptide “Glycindel” Scaffolds: A Customized Immunogen Method for Generating Oligomer-Selective Antibodies for Parkinson’s Disease
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https://figshare.com/articles/dataset/Optimizing_Epitope_Conformational_Ensembles_Using_Synuclein_Cyclic_Peptide_Glycindel_Scaffolds_A_Customized_Immunogen_Method_for_Generating_Oligomer-Selective_Antibodies_for_Parkinson_s_Disease/20323855
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资源简介:
Effectively presenting
epitopes on immunogens, in order to raise
conformationally selective antibodies through active immunization,
is a central problem in treating protein misfolding diseases, particularly
neurodegenerative diseases such as Alzheimer’s disease or Parkinson’s
disease. We seek to selectively target conformations enriched in toxic,
oligomeric propagating species while sparing the healthy forms of
the protein that are often more abundant. To this end, we computationally
modeled scaffolded epitopes in cyclic peptides by inserting/deleting
a variable number of flanking glycines (“glycindels”)
to best mimic a misfolding-specific conformation of an epitope of
α-synuclein enriched in the oligomer ensemble, as characterized
by a region most readily disordered and solvent-exposed in a stressed,
partially denatured protofibril. We screen and rank the cyclic peptide
scaffolds of α-synuclein in silico based on
their ensemble overlap properties with the fibril, oligomer-model
and isolated monomer ensembles. We present experimental data of seeded
aggregation that support nucleation rates consistent with computationally
predicted cyclic peptide conformational similarity. We also introduce
a method for screening against structured off-pathway targets in the
human proteome by selecting scaffolds with minimal conformational
similarity between their epitope and the same solvent-exposed primary
sequence in structured human proteins. Different cyclic peptide scaffolds
with variable numbers of glycines are predicted computationally to
have markedly different conformational ensembles. Ensemble comparison
and overlap were quantified by the Jensen–Shannon divergence
and a new measure introduced here, the embedding depth, which determines
the extent to which a given ensemble is subsumed by another ensemble
and which may be a more useful measure in developing immunogens that
confer conformational selectivity to an antibody.
创建时间:
2022-08-03



