In-Silico Comparative Study on Millets Peptide Inhibiting Fat Mass and Obesity-Associated Protein
Bibliographic record
Abstract
Over and undernutrition are generally perceived as lifestyle or diet related disorders.Apart from these external contributors, certain genes and proteins have been studied to plays a vital role in maintaining the metabolic state of an individual.One such gene is the fat mass and obesity-associated (FTO) protein-an m6A RNA demethylase, responsible for regulating energy homeostasis.This protein has been found to be strongly associated with obesity and related metabolic disorders.Targeting FTO with small-molecule inhibitors has shown promise as a therapeutic approach to manage obesity.The study employs a comprehensive computational strategy to identify bioactive peptides acting as potential natural inhibitors of the FTO protein derived from three millet species-finger millet (Eleusine coracana), pearl millet (Pennisetum glaucum), and foxtail millet (Setaria italica).Bioactive peptides were curated from published literature focusing on millet seed proteins.Their physicochemical properties were assessed using PepCalc to evaluate stability and solubility.Subsequently, three-dimensional structures of the peptides were predicted using the I-TASSER server to generate high-confidence models for docking.Molecular docking analyses were conducted using ClusPro to examine peptide-FTO binding affinities and interaction poses.The crystal structure of human FTO (retrieved from the Protein Data Bank) served as the docking target.Top-performing peptide-FTO complexes were further taken for molecular dynamics (MD) simulations to evaluate the dynamic behavior and stability of the interactions.Key parameters such as RMSD, RMSF, and hydrogen bond profiles were analyzed over the course of the simulations.Our results show that several millet-derived peptides bind strongly and stably to the FTO protein, with favorable docking scores and sustained hydrogen bonding at its active site.These findings highlight the potential of millet peptides as natural FTO inhibitors for developing functional foods or nutraceuticals to combat obesity.
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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 | 0.001 |
| 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".