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.

Cu,Zn superoxide dismutase (SOD1) is a 32 kDa homodimer that converts toxic oxygen radicals in neurons to less harmful species. The dimerization of SOD1 is essential to the stability of the protein. Monomerization increases the likelihood of SOD1 misfolding into conformations associated with aggregation, cellular toxicity, and neuronal death in familial amyotrophic lateral sclerosis (fALS). The ubiquity of disease-associated mutations throughout the primary sequence of SOD1 suggests an important role of physicochemical processes, including monomerization of SOD1, in the pathology of the disease. Herein, we use a first-principles statistical mechanics method to systematically calculate the free energy of dimer binding for SOD1 using molecular dynamics, which involves sequentially computing conformational, orientational, and separation distance contributions to the binding free energy. We consider the effects of two ALS-associated mutations in SOD1 protein on dimer stability, A4V and D101N, as well as the role of metal binding and disulfide bond formation. We find that the penalty for dimer formation arising from the conformational entropy of disordered loops in SOD1 is significantly larger than that for other protein–protein interactions previously considered. In the case of the disulfide-reduced protein, this leads to a bound complex whose formation is energetically disfavored. Somewhat surprisingly, the loop free energy penalty upon dimerization is still significant for the holoprotein, despite the increased structural order induced by the bound metal cations. This resulted in a surprisingly modest increase in dimer binding free energy of only about 1.5 kcal/mol upon metalation of the protein, suggesting that the most significant stabilizing effects of metalation are on folding stability rather than dimer binding stability. The mutant A4V has an unstable dimer due to weakened monomer-monomer interactions, which are manifested in the calculation by a separation free energy surface with a lower barrier. The mutant D101N has a stable dimer partially due to an unusually rigid β-barrel in the free monomer. D101N also exhibits anticooperativity in loop folding upon dimerization. These computational calculations are, to our knowledge, the most quantitatively accurate calculations of dimer binding stability in SOD1 to date.

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 .

Tau pathology is associated with many neurodegenerative disorders, including Alzheimer’s disease (AD), where the spatio–temporal pattern of tau neurofibrillary tangles strongly correlates with disease progression, which motivates therapeutics selective for misfolded tau. Here, we introduce a new avidity-enhanced, multi-epitope approach for protein-misfolding immunogen design, which is predicted to mimic the conformational state of an exposed epitope in toxic tau oligomers. A predicted oligomer-selective tau epitope 343KLDFK347 was scaffolded by designing a β-helix structure that incorporated multiple instances of the 16-residue tau fragment 339VKSEKLDFKDRVQSKI354. Large-scale conformational ensemble analyses involving Jensen–Shannon Divergence and the embedding depth showed that the multi-epitope scaffolding approach, employed in designing the β-helix scaffold, was predicted to better discriminate toxic tau oligomers than other “monovalent” strategies utilizing a single instance of an epitope for vaccine immunogen design. Using Rosetta, 10,000 sequences were designed and screened for the linker portions of the β-helix scaffold, along with a C-terminal stabilizing α-helix that interacts with the linkers, to optimize the folded structure and stability of the scaffold. Structures were ranked by energy, and the lowest 1% (82 unique sequences) were verified using AlphaFold. Several selection criteria involving AlphaFold are implemented to obtain a lead-designed sequence. The structure was further predicted to have free energetic stability by using Hamiltonian replica exchange molecular dynamics (MD) simulations. The synthesized β-helix scaffold showed direct binding in surface plasmon resonance (SPR) experiments to several antibodies that were raised to the structured epitope using a designed cyclic peptide. Moreover, the strength of binding of these antibodies to in vitro tau oligomers correlated with the strength of binding to the β-helix construct, suggesting that the construct presents an oligomer-like conformation and may thus constitute an effective oligomer-selective immunogen.

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.

Background Tauopathies are neurodegenerative disorders characterized by the abnormal aggregation of tau protein in the brain. Soluble toxic tau aggregates have the ability to transfer from cell‐to‐cell and to induce newly formed aggregates in recipient cells, thereby propagating tau pathology across the brain in a prion‐like manner. We used computational modeling to identify conformational “disease‐specific” epitopes predicted to become exposed on misfolded tau aggregates. Selectivity of antibodies for tau pathogenic species, as opposed to pan‐tau reactivity, is needed both in order to preserve normal tau function and to minimize the diversion of active antibody from the target through unproductive binding to more abundant non‐toxic forms of the protein. Method The ability of predicted disease‐associated conformational peptide epitopes to induce tau aggregation on their own was tested in a Thioflavin‐T fluorescence(ThT)‐based seeding assay. Mouse monoclonal antibodies raised against selected epitopes were screened by surface plasmon resonance (SPR) for binding to synthetic tau monomers, toxic oligomers, and soluble pre‐formed fibrils (PFFs). Recognition of naturally occurring tau species in Alzheimer’s disease (AD) brain was also confirmed by SPR. The ability of selected antibodies to inhibit induction of tau aggregation by AD brain seeds was assessed in a seeding assay using TauRD P301S FRET Biosensor Cells expressing tau constructs with different fluorescent labels producing a FRET signal when aggregated. Result Predicted conformational peptide epitopes capable of seeding activity in the ThT assay were used to immunize mice. SPR screening of hybridoma supernatants was used to identify antibody clones with the desired profile of preferential binding to a preparation of toxic tau oligomers vs non‐toxic tau monomers. A majority of these antibodies also bound soluble tau PFFs, a species believed to play a role in propagation of disease. Antibodies selected for further testing were found to recognize tau from AD brain extract by SPR and to inhibit seeding activity in a FRET assay. Conclusion Computational modeling for identification of predicted disease‐associated epitopes together with in vitro screening methods allowed for the generation of monoclonal antibodies with selectivity and activity against pathogenic, aggregated species of tau.