A Bioorganometallic Approach for the Electrochemical Detection of Proteins: A Study on the Interaction of Ferrocene–Peptide Conjugates with Papain in Solution and on Au Surfaces
Bibliographic record
Abstract
In this paper, a new bioorganometallic approach for the detection of proteins using surface-bound ferrocene-peptide conjugates is presented. Specifically, a series of peptide conjugates of 1'-aminoferrocene-1-carboxylic acid (ferrocene amino acid, Fca) is synthesized: Boc-Fca-Gly-Gly-Tyr(Bzl)-Arg(NO2)-OMe (2), Thc-Fca-Gly-Gly-Tyr(Bzl)-Arg(NO2)-OMe (3), Thc-Fca-Gly-Gly-Tyr(Bzl)-Arg(NO2)-OH (4), Boc-Fca-Gly-Gly-Arg(Mtr)-Tyr-OMe (7), Thc-Fca-Gly-Gly-Arg(Mtr)-Tyr-OMe (8), Thc-Fca-Gly-Gly-Arg(Mtr)-Tyr-OH (9), Thc-Fca-Gly-Gly-Arg-Tyr-OH (10). The peptide is conjugated to the C-terminal side of Fca and compounds 4, 7-10 possess a thiostic acid linked to the N-terminal side of Fca in order to facilitate formation of thin films on gold substrates. Competitive inhibition towards papain was determined for Thc-Fca-Gly-Gly-Tyr(Bzl)-Arg(NO2)-OH (4), Thc-Fca-Gly-Gly-Arg(Mtr)-Tyr-OH (9) and Thc-Fca-Gly-Gly-Arg-Tyr-OH (10). The binding interaction between the peptide modified substrates and papain enzyme was studied using real-time surface plasmon resonance (SPR) imaging. Peptide 10 showed the strongest binding affinity to papain. Adsorption/desorption rate constants were ka = 1.75+/-0.05 x 10(5) M(-1) s(-1) and kd = 2.90 +/- 0.05 x 10(-2) s(-1). Interactions of papain with Fca-peptide 10 were investigated by cyclic voltammetry. The interaction results were also verified by measuring the electrochemical response of the peptide-papain interaction as function of increasing enzyme concentration. A linear relationship was observed for papain concentration of up to 80 nM. Shifting to higher potentials as well as decrease in the overall signal intensity was observed. The detection limit was 4 x 10(-9) M.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".