Characterizing Aptamer-Based Alpha-Synuclein Fibril Inhibitors as a Potential Therapeutic Agent of Parkinson's Disease
Bibliographic record
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder pathologically characterized as loss of dopaminergic neurons and development of proteinaceous inclusions known as Lewy bodies (LBs).Predominant focus has been placed on the role of the protein α-synuclein, given its high concentration within LBs and its increased tendency to form oligomeric and fibril species.Among the many treatment strategies that have been developed to circumvent PD neurodegeneration, this thesis investigates aptamers that were generated specifically for the detection of monomeric α-synuclein and their fibril inhibition potential.Aptamers are single stranded oligonucleotide sequences designed for the recognition of target structures with high affinity and selectivity.Binding affinity and conformation analysis of five aptamer sequences, ASYN(1-5), for monomeric α-synuclein were evaluated through a variety of methods.Electrochemical impedance spectroscopy, circular dichroism (CD), and microscale thermophoresis suggested ASYN(1-5) display high affinity for α-synuclein, with ASYN2 being more selective.ASYN2 discriminated against similarly structured proteins, such as β-and γ-synuclein, preferentially binding with monomeric α-synuclein.ASYN2 truncations, exploiting potential binding domains within the aptamer, yielded six minimer sequences labelled A2m(1-6).Melting temperature and CD analysis suggested a centralized G-quadruplex binding motif was conserved within minimers A2m(3-5).Fibril inhibition assays determined whether ASYN(1-5) or A2m(1-6) were capable of hindering the aggregation of α-synuclein into larger aggregate species.Both ASYN2 and A2m3 displayed inhibition potential at 1:1 molar ratios of aptamer to protein.Further investigation into ASYN2 and A2m3 could reveal its application as a PD therapeutic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".