Publications

2024

1. Cyclization Scaffolding for Improved Vaccine Immunogen Stability: Application to Tau Protein in Alzheimer’s Disease

   Shawn C. C. Hsueh, Mark Nijland, Adekunle Aina, and Steven S. Plotkin

   Journal of Chemical Information and Modeling, 2024

   Abs DOI

   Effective scaffolding of immunogens is crucial for generating conformationally selective antibodies through active immunization, particularly in the treatment of protein misfolding diseases such as Alzheimer’s and Parkinson’s disease. Previous computational work has revealed that a disorder-prone region of the tau protein, when in a stacked form, is predicted to structurally resemble a small, soluble protofibril, having conformational properties similar to those of experimental in vitro tau oligomers. Such an oligomeric structural mimic has the potential to serve as a vaccine immunogen design for Alzheimer’s disease. In this study, we developed a cyclization scaffolding method in Rosetta, in which multiple cyclic peptides are stacked into a protofibril. Cyclization results in significant stabilization of protofibril-like structures by constraining the conformational space. Applying this method to the disorder-prone region of the tau fibril, we evaluated the metastability of the cyclized tau immunogen using molecular dynamics simulations, and we identified sequences of two cyclic constructs having high metastability in the protofibril. We then assessed their thermodynamic stability by computing the free energy required to separate a distal chain from the rest of the stacked structure. Our computational results, based on molecular dynamics simulations and free energy calculations, demonstrate that two cyclized constructs, cyclo-(VKSEKLDFKDRVQSKIFyN) and cyclo-(VKSEKLDFKDRVQSKIYvG) (lowercase letters indicate d-form amino acids), possess significantly increased thermodynamic stability in the protofibril over an uncyclized linear construct VKSEKLDFKDRVQSKI. The cyclization scaffolding approach proposed here holds promise as a means to effectively design immunogens for protein misfolding diseases, particularly those involving liposome-conjugated peptide constructs.

2023

1. De Novo Design of a β-Helix Tau Protein Scaffold: An Oligomer-Selective Vaccine Immunogen Candidate for Alzheimer’s Disease

   Adekunle Aina, Shawn C. C. Hsueh, Ebrima Gibbs, Xubiao Peng, Neil R. Cashman, and Steven S. Plotkin

   ACS Chemical Neuroscience, 2023

   Abs DOI PDF

   Tau pathology is associated with many neurodegenetive disorders, including Alzheimer’s disease, where the spatiotemporal 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% 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 HREMD simulations. The synthesized β-helix scaffold showed direct binding in SPR experiments to several antibodies that were raised to the structured epitope. 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.

2. PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles

   Adekunle Aina, Shawn C. C. Hsueh, and Steven S. Plotkin

   Journal of Chemical Information and Modeling, 2023

   Abs DOI PDF

   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 https://github.com/PlotkinLab/Prothon.

2022

1. Optimizing Epitope Conformational Ensembles Using α-Synuclein Cyclic Peptide “Glycindel” Scaffolds: A Customized Immunogen Method for Generating Oligomer-Selective Antibodies for Parkinson’s Disease

   Shawn C. C. Hsueh, Adekunle Aina, Andrei Yu. Roman, Neil R. Cashman, Xubiao Peng, and Steven S. Plotkin

   ACS Chemical Neuroscience, 2022

   Abs DOI

   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.

2. Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS

   Shawn C.C. Hsueh, Adekunle Aina, and Steven S. Plotkin

   The Journal of Physical Chemistry B, 2022

   PMID: 36410027

   Abs DOI

   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 https://github.com/PlotkinLab/Reservoir-REMD.

3. Rational Generation of Monoclonal Antibodies Selective for Pathogenic Forms of Alpha-Synuclein

   Ebrima Gibbs, Beibei Zhao, Andrei Roman, Steven S. Plotkin, Xubiao Peng, Shawn C. C. Hsueh, Adekunle Aina, Jing Wang, Clay Shyu, Calvin K. Yip, Sung-Eun Nam, Johanne M. Kaplan, and Neil R. Cashman

   Biomedicines, 2022

   Abs DOI

   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.

2021

1. Structural fluctuations and mechanical stabilities of the metamorphic protein RfaH

   Bahman Seifi, Adekunle Aina, and Stefan Wallin

   Proteins: Structure, Function, and Bioinformatics, 2021

   Abs DOI

   Abstract RfaH is a compact two-domain bacterial transcription factor that functions both as a regulator of transcription and an enhancer of translation. Underpinning the dual functional roles of RfaH is a partial but dramatic fold switch, which completely transforms the  50-amino acid C-terminal domain (CTD) from an all-α state to an all-β state. The fold switch of the CTD occurs when RfaH binds to RNA polymerase (RNAP), however, the details of how this structural transformation is triggered is not well understood. Here we use all-atom Monte Carlo simulations to characterize structural fluctuations and mechanical stability properties of the full-length RfaH and the CTD as an isolated fragment. In agreement with experiments, we find that interdomain contacts are crucial for maintaining a stable, all-α CTD in free RfaH. To probe mechanical properties, we use pulling simulations to measure the work required to inflict local deformations at different positions along the chain. The resulting mechanical stability profile reveals that free RfaH can be divided into a “rigid” part and a “soft” part, with a boundary that nearly coincides with the boundary between the two domains. We discuss the potential role of this feature for how fold switching may be triggered by interaction with RNAP.

2017

1. Multisequence algorithm for coarse-grained biomolecular simulations: Exploring the sequence-structure relationship of proteins

   Adekunle Aina, and Stefan Wallin

   The Journal of Chemical Physics, 2017

   Abs DOI

   We consider a generalized-ensemble algorithm for coarse-grained simulations of biomolecules which allows the thermodynamic behavior of two or more sequences to be determined in a single multisequence run. By carrying out a random walk in sequence space, the method also enhances conformational sampling. Escape from local energy minima is accelerated by visiting sequences for which the minima are more shallow or absent. We test the method on an intermediate-resolution coarse-grained model for protein folding with 3 amino acid types and explore the potential for a large-scale coverage of sequence space by applying the method to sets of more than 1000 sequences. The resulting thermodynamic data are used to analyze the structures and stability properties of sequences covering the space between folds with different secondary structures.