Molecular Docking of Fisetin as a Multi-target drug in the treatment of Multiple Sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS), is an autoimmune disorder of the CNS a long-lasting disorder that can attack the brain, spinal cord, and eyes. The severity of this disorder varies from person to person. Demyelination and lesion formation is the major pathological changes of MS, due to which there is no significant generation of the action potential along the axon of the nerves. In turn, leads to delayed propagation and perception of the chemical signals required for varieties of function in the body. Several theories have been emerging to apprehend the origin of MS such as genes, smoking, viral infection caused by Epstein-Barr virus, or the human herpes virus may trigger the disorder or cause relapses, Vitamin D deficiency etc. Statistical reports show Canada is the country having the highest rate with 1 in 400 people suffering from MS. In the present study, the drug targets of MS were analyzed by understanding its interaction with a plant flavonoid Fisetin having neuroprotective properties. Molecular docking of plant flavonoid fisetin with the enzyme targets of MS was performed using auto-dock 4.2. The minimum binding energy obtained from docking explains the efficiency of the ligand binding with the therapeutic target proteins. Three proteins were selected based on their action and function they play in the progression of Multiple sclerosis namely Caspase 1 (PDB Id: 1lBC), Calpain-1(PDB Id: 2ARY), and Cathepsin B (PDB Id: 1GMY). The docking of fisetin with Caspase 1 (PDB Id: 1lBC), Calpain-1(PDB Id: 2ARY), and Cathepsin B (PDB Id: 1GMY) displayed the minimum binding energy score as, Cathepsin B = -10.01 kcal/mol, Calpain-1= -9.95 kcal/mol, Caspase 1= -8.18 kcal/mol respectively and also the number of hydrogen bonds: 20, 18, 23 respectively. The target proteins Cathepsin B and Calpian 1, showed the strongest interaction with Fisetin with the least minimum binding energy. Molecular properties & drug-likeness, biological activity, and toxicity of Fistein were analyzed using the Way2drug bio tool. Lipinski's benchmark rule of five (RO5)5 defines desirable drug candidate physicochemical property which was successfully shown by the ligand fistein : log P:1.35, HBD: 4, HBA:6 The following are the obtained results showing biological activity and toxicity of ligand fistein: • Biological Activity: 0.966- Membrane intergrity; 0.959- Aryl-alcohol dehydrogenase (NADP+) inhibitor; 0.950- kinase inhibitor • Toxicity: Vascular toxic (0.755 pa), ulcer, aphthous (0.713 pa). where pa [pharmacologically active] is greater than 0.7.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".