Probing the Binding Sites of Antibiotic Drugs Doxorubicin and N-(trifluoroacetyl) Doxorubicin with Human and Bovine Serum Albumins
Bibliographic record
Abstract
We located the binding sites of doxorubicin (DOX) and N-(trifluoroacetyl) doxorubicin (FDOX) with bovine serum albumin (BSA) and human serum albumins (HSA) at physiological conditions, using constant protein concentration and various drug contents. FTIR, CD and fluorescence spectroscopic methods as well as molecular modeling were used to analyse drug binding sites, the binding constant and the effect of drug complexation on BSA and HSA stability and conformations. Structural analysis showed that doxorubicin and N-(trifluoroacetyl) doxorubicin bind strongly to BSA and HSA via hydrophilic and hydrophobic contacts with overall binding constants of K(DOX-BSA) = 7.8 (± 0.7) × 10(3) M(-1), K(FDOX-BSA) = 4.8 (± 0.5)× 10(3) M(-1) and K(DOX-HSA) = 1.1 (± 0.3)× 10(4) M(-1), K(FDOX-HSA) = 8.3 (± 0.6)× 10(3) M(-1). The number of bound drug molecules per protein is 1.5 (DOX-BSA), 1.3 (FDOX-BSA) 1.5 (DOX-HSA), 0.9 (FDOX-HSA) in these drug-protein complexes. Docking studies showed the participation of several amino acids in drug-protein complexation, which stabilized by H-bonding systems. The order of drug-protein binding is DOX-HSA > FDOX-HSA > DOX-BSA > FDOX>BSA. Drug complexation alters protein conformation by a major reduction of α-helix from 63% (free BSA) to 47-44% (drug-complex) and 57% (free HSA) to 51-40% (drug-complex) inducing a partial protein destabilization. Doxorubicin and its derivative can be transported by BSA and HSA in vitro.
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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".