Interaction of AZT with Human Serum Albumin Studied by Capillary Electrophoresis, FTIR and CD Spectroscopic Methods
Bibliographic record
Abstract
The thymidine analog 3'-azido-3'-deoxythymidine (AZT) is still one of the effective drugs against human immunodeficiency (HIV) infection. AZT has been used as inhibitor of HIV-1 reverse transcriptase, the virus encoded enzyme which catalyzes transcription of viral RNA to DNA. The drug interaction with protein has been included in its mechanism of action. Human serum albumin (HSA) is a carrier of many drugs in vivo and thus AZT-HSA complexation can serve as a model for drug-protein interaction. This study was designed to examine the interaction of AZT with human serum albumin at physiological conditions using constant protein concentration (0.2% or 2%) and different drug contents (5 to 1000 microM). Capillary electrophoresis, FTIR and CD spectroscopic methods were used to determine the drug binding mode, the drug binding constant and the effects of drug-HSA complexation on the protein and AZT conformations in aqueous solution. Capillary electrophoresis and spectroscopic results showed two major bindings for the AZT-HSA complexes with K(1)=1.9 x 10(6) M(-1)and K(2)= 2.1 x 10(4) M(-1). Minor alterations of the protein secondary structure from that of the alpha-helix to beta-sheet were observed upon drug complexation, whereas the drug sugar pucker remained in the C2'-endo/anti conformation upon protein interaction.
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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 | 0.001 |
| 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".