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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".