Surface-enhanced Raman spectroscopy detection of protein-ligand binding using D-glucose and glucose binding protein on nanostructured plasmonic substrates
Bibliographic record
Abstract
Conjugated nano-biological architectures interfacing solid nano-structured surfaces with biological polymers have gained significant attention due to their potential biosensing and biocatalytic applications. However, efficient characterization of such integrated systems remains a challenge. We describe surface enhanced Raman spectroscopy (SERS) detection of complex of D-glucose with glucose binding protein (GBP) immobilized on substrates. Substrates comprised of dense Ag nanostructure arrays on Ni-coated fused silica wafers were fabricated employing ultrahigh resolution electron beam lithography. Glucose-bound and glucose-free histidine-tagged GBP was immobilized on the substrates and probed using SERS while the samples were kept in solution, and the observed Raman spectra were recorded. Three substrate designs were tested for SERS detection of the protein-ligand binding. SERS spectra of immobilized glucose-free and glucose-bound GBP exhibited pronounced differences in their Raman signatures, demonstrating the potential of SERS as a sensitive method for the detection of protein-ligand molecular recognition on a solid surface. However, morphology of the nano-patterned plasmonic structures was found to influence the SERS signatures significantly. In order to interpret the findings, simulations of electric field around the nano-structured substrates were performed. An interplay of two factors, the availability of space between Ag features where the GBP could bind to Ni, and the effectiveness of the electromagnetic enhancement of the Raman scattering in “hot spots” between these features, was concluded to determine the observed trends.
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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 | 0.000 | 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 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".