Unravelling Nonspecific Adsorption of Complex Protein Mixture on Surfaces with SPR and MS
Bibliographic record
Abstract
Characterization of protein adsorption to surfaces has implications from biosensing to protective biocoatings. While research studies have principally focused on determining the magnitude of protein adsorption to surfaces, the proteins involved in the process remains only broadly identified and has not been investigated on several surfaces. To further elucidate the nonspecific adsorption process of serum to surfaces, surface plasmon resonance (SPR) and matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) were used in combination to obtain quantitative and qualitative information about the process of protein adsorption to surfaces. To validate the technique, crude serum was nonspecifically adsorbed on four self-assembled monolayer (SAM) on gold: 16-mercaptohexadecanoic acid (16-MHA), 11-mercaptoundecane(ethylene glycol)3-COOH (PEG), 3-MPA-LHDLHD-OH, and 3-MPA-HHHDD-OH. Direct MS analysis of the nonspecifically adsorbed proteins suggested the presence of a variety of protein (BSA, IgG, and apolipoprotein A-1). Performing a trypsin digestion of the nonspecifically adsorbed proteins confirmed the presence of BSA and apolipoprotein A-1 and further revealed the complexity of the process by detecting the presence of complement C3, SHC-transforming protein 1, and kininogen 2. The level of nonspecific adsorption on different surfaces measured by SPR sensing directly correlated with the intensity of the serum protein and indirectly with the tryptic peptides measured by MS. Detailed analysis of the BSA peptides digested on 16-MHA and for BSA digested in solution was used to investigate the orientation of BSA on this surface. The combination of SPR and MS allows the quantitative and qualitative understanding of protein adsorption processes to surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".