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 modelled 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 en- sembles. We present experimental data of seeded aggregation that supports 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 was 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.

中文翻译：

---

使用 α-突触核蛋白环肽“甘氨酸”支架优化表位构象集合：一种用于产生帕金森病寡聚体选择性抗体的定制免疫原方法

有效地呈现免疫原上的表位，以便通过主动免疫产生构象选择性抗体，是治疗蛋白质错误折叠疾病，特别是阿尔茨海默病或帕金森病等神经退行性疾病的核心问题。我们寻求选择性地靶向富含有毒寡聚繁殖物种的构象，同时保留通常更丰富的蛋白质的健康形式。为此，我们通过插入/删除可变数量的侧翼甘氨酸（“甘氨酸”）来计算模拟环状肽中的支架表位，以最好地模拟寡聚体集合中富含的 α-突触核蛋白表位的错误折叠特异性构象，如其特点是在压力下最容易无序和溶剂暴露的区域，部分变性的原纤维。我们根据它们与原纤维、寡聚体模型和分离的单体集合的集合重叠特性，在计算机中筛选和排序 α-突触核蛋白的环肽支架。我们提出了种子聚集的实验数据，这些数据支持与计算预测的环肽构象相似性一致的成核率。我们还介绍了一种筛选人类蛋白质组中结构化的非通路靶标的方法，方法是选择在其表位与结构化人类蛋白质中相同的溶剂暴露一级序列之间具有最小构象相似性的支架。计算预测具有可变数量甘氨酸的不同环肽支架具有显着不同的构象集合。