Electrochemical flow injection analysis of the interaction between pyrroloquinoline quinone (PQQ) and α-synuclein peptides related to Parkinson's disease
Bibliographic record
Abstract
α-Synuclein (α-syn) is a hallmark protein of Parkinson's disease (PD). The aggregation process of α-syn has been heavily associated with the pathogenesis of PD. With the exponentially growing number of potential therapeutic compounds that can inhibit the aggregation of α-syn, there is now a significant demand for a high-throughput analysis system. Herein, a novel flow injection analysis system with an electrochemical biosensor as the detector was developed to study the interaction of a well-described antioxidant and amyloid inhibitor, pyrroloquinoline quinone (PQQ) with α-synuclein peptides. Screen-printed gold electrodes (SPEs) were modified using heptapeptides from α-syn wild-type (WT) and mutants such as lysine knock-out (ETEE) and E46K. Affinity binding events between these peptides and PQQ were analyzed by electrochemical impedance spectroscopy (EIS) and further confirmed by high-performance liquid chromatography (HPLC), liquid chromatography/mass spectrometry (LC/MS), and nuclear magnetic resonance (NMR) spectroscopy. HPLC and LC/MS results revealed that PQQ formed a stable complex with α-syn. NMR results confirmed that the α-syn-PQQ complex was formed via a Schiff base formation-like process. In addition, results showed that lysine residues influenced the binding event, in which the presence of an extra lysine stabilized the α-syn-PQQ complex, and the absence of a lysine significantly decreased the interaction of α-syn with PQQ. Therefore, we concluded that EIS is a promising technique for the evaluation of the interaction between PQQ-based amyloid inhibitors and α-syn. The electrochemical flow injection analysis assembly provided a rapid and low-cost drug discovery platform for the evaluation of small molecule-protein interactions.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".