Interactions of Poly(amidoamine) Dendrimers with Human Serum Albumin: Binding Constants and Mechanisms
Bibliographic record
Abstract
The interactions of nanomaterials with plasma proteins have a significant impact on their in vivo transport and fate in biological fluids. This article discusses the binding of human serum albumin (HSA) to poly(amidoamine) [PAMAM] dendrimers. We use protein-coated silica particles to measure the HSA binding constants (K(b)) of a homologous series of 19 PAMAM dendrimers in aqueous solutions at physiological pH (7.4) as a function of dendrimer generation, terminal group, and core chemistry. To gain insight into the mechanisms of HSA binding to PAMAM dendrimers, we combined (1)H NMR, saturation transfer difference (STD) NMR, and NMR diffusion ordered spectroscopy (DOSY) of dendrimer-HSA complexes with atomistic molecular dynamics (MD) simulations of dendrimer conformation in aqueous solutions. The binding measurements show that the HSA binding constants (K(b)) of PAMAM dendrimers depend on dendrimer size and terminal group chemistry. The NMR (1)H and DOSY experiments indicate that the interactions between HSA and PAMAM dendrimers are relatively weak. The (1)H NMR STD experiments and MD simulations suggest that the inner shell protons of the dendrimers groups interact more strongly with HSA proteins. These interactions, which are consistently observed for different dendrimer generations (G0-NH(2)vs G4-NH(2)) and terminal groups (G4-NH(2)vs G4-OH with amidoethanol groups), suggest that PAMAM dendrimers adopt backfolded configurations as they form weak complexes with HSA proteins in aqueous solutions at physiological pH (7.4).
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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".