Innate Immune Discrimination Failure In α-Synuclein Pathology: A Mimicry Framework

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Parkinson's Disease is a highly prevalent neurodegenerative disease associated with the accumulation of misfolded alpha-synuclein, which forms aggregates that disrupt neuronal function. These aggregates represent a well-defined pathological feature and remain central to efforts to understand and address neurodegeneration.

Despite this, current therapeutic approaches are largely symptomatic, with limited capacity to directly and selectively engage aggregation. While strategies to reduce aggregate burden are actively explored, comparatively little attention has been directed toward how these structures are initially recognized at a molecular level.

In the brain, the innate immune system controls the identification and clearance of abnormal molecular structures, relying on the ability to distinguish between normal and harmful protein states. However, monomeric and aggregated forms of alpha-synuclein can share structural features, constraining the precision of this distinction.

In other contexts, molecular mimicry demonstrates how structural similarity governs recognition. For example, in Sydenham Chorea and Guillain-Barré Syndrome, shared features between distinct molecules can lead to convergent recognition and misdirected responses. As a design principle, such similarity may be leveraged to direct recognition with greater specificity.

This project investigates whether structural overlap between alpha-synuclein states constrains recognition and explores a molecular mimicry-based framework to enhance selective identification of toxic species.

Objectives:

Investigate structural differences between monomeric and aggregated alpha-synuclein

Analyze molecular surface properties to assess structural overlap between states

Evaluate how overlap may influence recognition and discrimination

Identify and evaluate candidate molecular mimics for selective targeting of oligomeric forms

Hydrophobic Topology in α-Synuclein Aggregation Computational Analysis Summary:

I developed a multi-step computational pipeline to analyze how hydrophobic surface patterns (a key component in innate immune recognition), changes across α-synuclein structures and to identify motifs that may distinguish these states.

Materials:

Protein structures from the Protein Data Bank (PDB)

Python programming environment

Data analysis tools (e.g. pandas, matplotlib, seaborn)

Custom-built analysis programs

Dataset of ~5000 random short protein sequences

Step 1: Data Collection

I collected protein structures representing monomers, fibrils, and computationally generated oligomer-like dimers and trimers to model early aggregation. Multiple samples were used for consistency, and to account for the intristically disordered nature of the protein.

Step 2: Hydrophobic Surface Analysis

Hydrophobic (water-repelling) amino acids were identified, and their surface exposure was measured to compare overall hydrophobicity across structures. Solvent-accessible surface area was used to quantify exposure, allowing direct comparison between conformational states.

Step 3: Patch Detection

Nearby hydrophobic residues were grouped into surface "patches". I measured the number of patches, the size of the largest patch, and how fragmented they were to capture differences in surface organization.

Step 4: Robustness Testing

The analysis was repeated using multiple distance thresholds (EPS values) to ensure results were not dependent on a single parameter.

Step 5: Motif Screening

I tested ~5000 random short sequences and compared them to top motifs (e.g., YYVY, LAYY) and weaker controls (e.g., KQTV) to identify selective patterns. Motifs were ranked by performance, with top candidates showing strong enrichment for oligomer-associated features relative to controls.

Results

Monomeric and oligomeric α-synuclein exhibited overlapping distributions in global hydrophobic exposure. Both hydrophobic fraction and solvent-accessible surface area (SASA) showed substantial convergence between conformational states, with no clear separation observed.

This pattern was consistent across additional structural metrics. Large hydrophobic patches were identified in both monomeric and oligomeric conformations. While absolute patch size and count varied, the presence of these features was not restricted to a single state. Variation in clustering thresholds altered numerical values but did not eliminate the observed overlap.

Localized structural analysis revealed that most hydrophobic motifs were shared between conformations. However, a subset of motifs demonstrated increased prevalence or clustering in oligomeric structures. These motifs appeared more frequently within regions associated with aggregation interfaces.

Ranking of candidate mimic motifs showed that certain features displayed higher selectivity for oligomeric conformations relative to others. These candidates were consistently identified across parameter variations, indicating stability in motif selection. One motif in particular, YYVY, emerged as the most promising, ranking in the 99th percentile of all selected candidates. Structural mapping confirmed that these motifs aligned with exposed surface regions in the analyzed models.

Overall, the results show that global structural features overlap between monomeric and oligomeric α-synuclein, while localized motif patterns exhibit measurable differences in distribution and selectivity.

Discussion & Implications

This work reveals a fundamental limitation in how protein states may be distinguished. Hydrophobic surface exposure overlaps substantially across α-synuclein structural states, yet their spatial organization differs in a systematic and meaningful way. Interestingly, monomeric and oligomer-like structures exhibit comparable levels of hydrophobic exposure, but oligomer-like assemblies display fragmented surface patterns, while fibrils consolidate these regions into dominant hydrophobic domains.

This distinction suggests that recognition cannot rely on hydrophobic content alone. Instead, it is the organization of these features that encodes structural identity. If recognition systems depend on surface patterns, then overlapping exposure combined with altered topology creates inherent ambiguity. This may represent a structural basis for the failure to selectively identify toxic protein forms.

Motif screening provides independent support for this framework. Aromatic-rich sequences such as YYVY and LAYY consistently demonstrated high selectivity, while lower-ranked controls such as KQTV did not. The model therefore captures a pattern that is both detectable and discriminative.

These findings remain stable across multiple structural samples and distance thresholds, reinforcing that the observed trends are not artifacts of a single dataset or parameter choice.

Taken together, these results support a topology-based model of protein recognition, in which spatial organization, rather than composition alone, defines the ability to distinguish structural states.

Future directions (in vitro):

Assess conformer-selective targeting of α-synuclein under intrinsically disordered protein (IDP) behavior in dynamic experimental conditions

Evaluate preferential binding to oligomeric vs monomeric forms across conformational ensembles

Examine uptake, intracellular trafficking, and degradation in microglia/macrophage-like cells using lysosomal/autophagic markers

Perform time-course analysis to assess clearance under impaired degradation conditions

Measure cytokine release and cell viability to distinguish protective vs inflammatory responses

Consider downstream BBB compatibility and extension to other proteinopathies (e.g., Alzheimer's, Huntington's, ALS, prion diseases)

Thank you to Elizabeth (Liza) Kovalevich, for our thoughtful discussions as this project took shape.

Thank you to my science teacher, Mme Larocque, for acting as my reference for this project. I am also grateful to the tech teachers at Merivale High School, who provided access to resources and assistance.

I am especially grateful to the researchers who responded to my questions with diverse insights on this subject, helping me refine the direction of this project.

I would like to acknowledge the organizers of the Ottawa Regional Science Fair and the Canada-Wide Science Fair for providing the opportunity to present my research.

Additionally, I appreciate the use of open scientific databases and computational tools that made this analysis possible.

Finally, I would like to thank my friends for their encouragement, support, and patience in listening to the numerous iterations of this project.

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