The effects of poly(ethylene glycol) on the solution structure of human serum albumin
Bibliographic record
Abstract
Protein physical and chemical properties can be altered by polymer interaction. The presence of several high affinity binding sites on human serum albumin (HSA) makes it a possible target for many organic and polymer molecules. This study was designed to examine the interaction of HSA with poly(ethylene glycol) (PEG) in aqueous solution at physiological conditions. Fourier transform infrared, ultraviolet-visible, and CD spectroscopic methods were used to determine the polymer binding mode, the binding constant, and the effects of polymer complexation on protein secondary structure. The spectroscopic results showed that PEG is located along the polypeptide chains through H-bonding interactions with an overall affinity constant of K = 4.12 x 10(5) M(-1). The protein secondary structure showed no alterations at low PEG concentration (0.1 mM), whereas at high polymer content (1 mM), a reduction of alpha-helix from 59 (free HSA) to 53% and an increase of beta-turn from 11 (free HSA) to 22% occurred in the PEG-HSA complexes (infrared data). The CDSSTR program (CD data) also showed no major alterations of the protein secondary structure at low PEG concentrations (0.1 and 0.5 mM), while at high polymer content (1 mM), a major reduction of alpha-helix from 69 (free HSA) to 58% and an increase of beta-turn from 7 (free HSA) to 18% was observed.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".