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.

The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at .

Misfolded toxic forms of alpha-synuclein (α-Syn) have been implicated in the pathogenesis of synucleinopathies, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). The α-Syn oligomers and soluble fibrils have been shown to mediate neurotoxicity and cell-to-cell propagation of pathology. To generate antibodies capable of selectively targeting pathogenic forms of α-Syn, computational modeling was used to predict conformational epitopes likely to become exposed on oligomers and small soluble fibrils, but not on monomers or fully formed insoluble fibrils. Cyclic peptide scaffolds reproducing these conformational epitopes exhibited neurotoxicity and seeding activity, indicating their biological relevance. Immunization with the conformational epitopes gave rise to monoclonal antibodies (mAbs) with the desired binding profile showing selectivity for toxic α-Syn oligomers and soluble fibrils, with little or no reactivity with monomers, physiologic tetramers, or Lewy bodies. Recognition of naturally occurring soluble α-Syn aggregates in brain extracts from DLB and MSA patients was confirmed by surface plasmon resonance (SPR). In addition, the mAbs inhibited the seeding activity of sonicated pre-formed fibrils (PFFs) in a thioflavin-T fluorescence-based aggregation assay. In neuronal cultures, the mAbs protected primary rat neurons from toxic α-Syn oligomers, reduced the uptake of PFFs, and inhibited the induction of pathogenic phosphorylated aggregates of endogenous α-Syn. Protective antibodies selective for pathogenic species of α-Syn, as opposed to pan α-Syn reactivity, are expected to provide enhanced safety and therapeutic potency by preserving normal α-Syn function and minimizing the diversion of active antibody from the target by the more abundant non-toxic forms of α-Syn in the circulation and central nervous system.

The comparison of protein conformational ensembles is of central importance in structural biology. However, there are few computational methods for ensemble comparison, and those that are readily available, such as ENCORE, utilize methods that are sufficiently computationally expensive to be prohibitive for large ensembles. Here, a new method is presented for efficient representation and comparison of protein conformational ensembles. The method is based on the representation of a protein ensemble as a vector of probability distribution functions (pdfs), with each pdf representing the distribution of a local structural property such as the number of contacts between Cβ atoms. Dissimilarity between two conformational ensembles is quantified by the Jensen–Shannon distance between the corresponding set of probability distribution functions. The method is validated for conformational ensembles generated by molecular dynamics simulations of ubiquitin, as well as experimentally derived conformational ensembles of a 130 amino acid truncated form of human tau protein. In the ubiquitin ensemble data set, the method was up to 88 times faster than the existing ENCORE software, while simultaneously utilizing 48 times fewer computing cores. We make the method available as a Python package, called PROTHON, and provide a GitHub page with the Python source code at .

Background Previous studies of Alzheimer’s disease (AD) pathology point to cytotoxic tau as a cause of neuronal cell death, which is induced or exacerbated by soluble misfolded Aβ oligomers. Soluble misfolded species of both tau and Aβ are both observed to propagate cell‐to‐cell. A method for identifying antibodies to tau and Aβ that are conformationally‐selective to propagative misfolded oligomeric forms, and which also have low affinity to isolated monomers or, particularly for Aβ, low affinity to fibrils, is thus a highly desired goal that holds significant promise for AD therapy. Method We have developed a novel computational platform to identify epitopes that may be selectively exposed on oligomers. Epitope prediction ideally uses an experimentally determined fibril structure as input, but the method alters this structure using molecular dynamics, to more accurately model the regions that may be exposed on soluble oligomers. Both primary sequence and structural conformation are taken into account: The epitope should be conformationally‐distinct from those conformations presented in the functional healthy protein. Epitope scaffolding is then employed to optimize the presentation of the epitope in animal immunizations, so that the resulting antibodies are predicted to be selective to misfolded oligomeric forms. Result Oligomer‐specific epitope predictions for tau and for Aβ have been used to raise preclinical antibodies that have selectivity for pathogenic tau and Aβ species. In vitro SPR measurements confirmed selective binding to synthetic oligomers and soluble pre‐formed fibrils (PFFs), vs. native healthy protein. As well, the antibodies showed little immunoreactivity toplaque or vascularAβ deposits via immunohistochemistry, while SEC fractionation of AD brain homogenate shows selective binding to toxic dimers, tetramers and dodecamers, in contrast to aducanumab and bapineuzumab. Tau antibodies recognized tau from AD brain extract, and inhibited seeding activity in a FRET assay. Aβ antibodies alleviated the cognitive deficits caused by oligomers in mouse NOR studies. Conclusion Lead antibodies to tau and Aβ developed using rationally designed conformational epitopes are likely to achieve greater therapeutic potency by selectively targeting soluble toxic oligomers, and reducing the risk of target distraction and ARIA.