Monolayers of 3-Mercaptopropyl-amino Acid to Reduce the Nonspecific Adsorption of Serum Proteins on the Surface of Biosensors
Bibliographic record
Abstract
Monolayers prepared with polar or ionic amino acids with short side chains have a reduced nonspecific adsorption of serum proteins compared to that of hydrophobic amino acids and organic monolayers immobilized on the gold surface of surface plasmon resonance (SPR) biosensors. Proteins contained in biological samples adsorb on most surfaces, which in the case of biosensors causes a nonspecific response that hinders the quantification of biomarkers in these biological samples. To circumvent this problem, self-assembled monolayers (SAM) of N-3-mercaptopropyl-amino acids (3-MPA-amino acids) were prepared from 19 natural amino acids. These SAM were investigated to limit the nonspecific adsorption of proteins contained in biological fluids and to immobilize molecular receptors (i.e., antibodies) that are necessary in the construction of biosensors. SPR and Ge attenuated total reflection (GATR) FTIR spectroscopy were employed to characterize the formation of the amino acid SAMs. Monolayers of 3-MPA-amino acids densely packed on the surface of the SPR biosensors result in a surface concentration of approximately 10 (15) molecules/cm (2). SPR also quantifies the surface concentration of serum proteins nonspecifically adsorbed on 3-MPA-amino acids following the exposure of the biosensor to undiluted bovine serum. The concentration of nonspecifically bound proteins ranged from approximately 400 ng/cm (2) with polar and ionic amino acids to approximately 800 ng/cm (2) with amino acids of increased hydrophobicity. The nonspecific adsorption of serum proteins on the 3-MPA-amino acids increases in the following order: Asp < Asn < Ser < Met < Glu < Gln < Thr < Gly < His < Cys < Arg < Phe < Trp < Val < Pro < Ile < Leu < Ala < Tyr. The analysis of the adsorption and desorption curves for serum proteins on the SPR sensorgram has demonstrated the strong irreversibility of the protein adsorption on each surface. The effective hydrophilicity of the SAMs was measured from the contact angle with a saline buffer and has demonstrated that surfaces minimizing the contact angle with PBS performed better in serum. The antibody for beta-lactamase was immobilized on a 3-MPA-glycine SAM, and beta-lactamase was detected in the nanomolar range. The presence of beta-lactamase is an indicator of antibiotic resistance.
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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) | 0.001 | 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 | 0.001 |
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".