Bio-physical and computational studies on serum albumin / target protein binding of a potential anti-cancer agent
Bibliographic record
Abstract
The successful evolution of an effective drug depends on its pharmacokinetics, efficiency and safety and these in turn depend on the drug-target/drug-carrier protein binding. This work, deals with the interaction of a pyridine derivative, 2-hydroxy-5-(4-methoxyphenyl)-6-phenylpyridine 3-carbonitrile (HDN) with serum albumins at physiological conditions utilizing the steady state and time-resolved fluorescence techniques by probing the emission behavior of Trp in BSA and HSA. In-silico studies revealed a combined static and dynamic quenching mechanism for the interactions. The binding studies suggests a spontaneous binding between HDN and the albumins with a moderate binding affinity (Kb ∼ 10–5 M-1) with a single class of binding site. The FRET mediated emission from HDN indicates preferential binding of HDN in subdomain IIA of the albumins with Trp residue in close proximity. Circular dichroism results indicate HDN induced conformational changes for BSA and HSA, but the α-helical secondary structure was well preserved even up to a concentration of 10 µM HDN. Moderate binding affinity of HDN with BSA and HSA and the unaltered secondary structure of proteins on binding propose the potential application of HDN as an efficient drug. The application of docking method on the affinity of HDN towards the proposed target/receptor is discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".