AN OVERVIEW OF DRUG BINDING TO HUMAN SERUM ALBUMIN: PROTEIN FOLDING AND UNFOLDING
Bibliographic record
Abstract
Human Serum Albumin (HSA) is a principal extracellular protein with a high concentration in blood plasma and a carrier of many drugs to different molecular targets. Drug binding to HSA can alter the protein biophysical and biochemical properties of protein. The structural analysis of human serum albumin complexes, with naturally occurring flavonoids quercetin(antioxidant), kaempferol (antioxidant), delphinidin (antioxidant), AZT (3'-azido-3'-deoxythymidine) (anti-AIDS), aspirin (anti inflammatory), taxol (anticancer), cisplatin (anticancer), atrazine (herbicide), 2,4-D (herbicide), polyamines (biogenic), chlorophyll (antimutagenic), chlorophyllin (antitumor), poly(ethylene glycol) (polymer), vandyl cation and vanadate anion in aqueous solution are reported. Using capillary electrophoresis, FTIR (Fourier transform infrared), UV-Visible and CD (Circular dichroism) spectroscopic methods, the drug binding mode, the binding constant and the effects of drug complexation on protein secondary structure are determined. The concentrations of HSA used were 0.6 to 0.3 mM, while different drug concentrations were 1~$mu$M to 1 mM. Structural analysis showed drugs are mostly located along the polypeptide chains, with both specific and non-specific interactions. The stability of drug-HSA complexes were in the order: K_VO^2+=1.2times 10^8M^-1 > K_AZT=1.9 times 10^6M^-1>K_del=4.7times 10^5M^-1>K_PEG=4.1times 10^5M^-1>K_kae=2.6times 10^5M^-1>K_que=1.4times 10^5M^-1>K_atrazine=3.5times 10^4M^-1>K_chlorophyll=2.9times 10^4M^-1>K_2,4-D=2.5times 10^4M^-1>K_spermine=1.7times 10^4M^-1>K_taxol=1.43times 10^4M^-1>K_aspirin=1.04times 10^4M^-1>K_chlorophyllin=7.0times 10^3M^-1>K_VO3^-=6.0times 10^3M^-1>K_spermidine=5.4times 10^3M^{-1}>K_putrescine=3.9times 10^3M^1>K_cisplatin=1.2times 10^2M^-1. At low drug concentration (1 muM), protein conformation was not altered (infrared and CD results), while, at high drug content(1 mM), a major reduction of alpha-helix from 60-55% (free HSA) to 49-40% and an increase of beta-structure from 22-15% (free HSA) to 33-23% in the drug-protein complexes occurred. These observations indicated that low drug content induced protein stabilization (folding), whereas, at high drug concentration, a partial protein destabilization (unfolding)occurred in these drug-HSA complexes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".