Understanding the Effect of Secondary Structure on Molecular Interactions of Poly-<scp>l</scp>-lysine with Different Substrates by SFA
Bibliographic record
Abstract
Nonspecific adsorption of proteins on biomaterial surfaces challenges the widespread application of engineered materials, and understanding the impact of secondary structure of proteins and peptides on their adsorption process is of both fundamental and practical importance in bioengineering. In this work, poly-L-lysine (PLL)-based α-helices and β-sheets were chosen as a model system to investigate the effect of secondary structure on peptide interactions with substrates of various surface chemistries. Circular dichroism (CD) was used to confirm the presence of both α-helix and β-sheet structured PLL in aqueous solutions and upon adsorption to quartz, where these secondary structures seemed to be preserved. Atomic force microscopy (AFM) imaging showed different surface patterns for adsorbed α-helix and β-sheet PLL. Interactions between PLL of different secondary structures and various substrates (i.e., PLL, Au, mica, and poly(ethylene glycol) (PEG)) were directly measured using a surface forces apparatus (SFA). It was found that β-sheet PLL films showed higher adsorbed layer thicknesses in general. Adhesion energies of β-sheet versus Au and β-sheet versus β-sheet were considerably higher than that of α-helix versus Au and α-helix versus α-helix systems, respectively. Au and β-sheet PLL interactions seemed to be more dependent on the salt concentration than that of α-helix, while the presence of a grafted PEG layer greatly diminished any attraction with either PLL structure. The molecular interaction mechanism of peptide in different secondary structures is discussed in terms of Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, Alexander-de Gennes (AdG) steric model and hydrogen bonding, which provides important insight into the fundamental understanding of the interaction mechanism between proteins and biomaterials.
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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 | 0.000 |
| 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".