A comprehensive multispectroscopic and molecular docking studies on the interaction of bioactive coumarins with bovine serum albumin
Bibliographic record
Abstract
The investigation focused on the interaction between bovine serum albumin (BSA) and the biologically active coumarin derivatives 4-(5-amino-[1,3,4]thiadiazol-2-ylsulfanylmethyl)-7-methoxy-chrome-2-one (1) and 4-(5-amino-[1,3,4]thiadiazol-2-ylsulfanyl methyl)-7-methyl-chrome-2-one (2). Molecular docking approaches, synchronous fluorescence spectroscopy, UV–Vis spectroscopy, circular dichroism (CD) spectra and fluorescence spectroscopy were among the multispectroscopic methods used to study the interaction between BSA and coumarin derivatives. The examined coumarin compounds’ interaction with BSA yielded a static quenching mechanism for fluorescence. Values for the binding constant (Kb) and quenching constant (Kq) for BSA–coumarin derivatives have been calculated using the Stern–Volmer equation. A change in the tryptophan residue of BSA was seen in its surroundings using synchronous fluorescence quenching investigations. The potential of the compounds under investigation to bind BSA was examined, and it was found that each compound had around one binding site. According to the free energy estimate, there is a spontaneous and very favorable binding interaction between BSA and test compounds. Using the Forster energy transfer theory, the binding average distance between BSA and the chemicals under investigation was found. In conjunction with the findings of CD spectral and fluorescence investigations, it shown that compound 2 has a higher affinity for BSA than compound 1. Molecular docking studies and spectroscopic experimental data are found to be in good agreement. The binding pocket for the development of the ligand–protein complex through hydrophobic and hydrogen bonding interactions was identified by the molecular docking investigation. Furthermore, the results of the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction and drug-likeness analysis demonstrated the medicinal chemistry characteristics and drug-likeness of these compounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".