Identification of Neuroactive Peptide from Venomous Species using Structural Analysis: A Possible Neuronal Therapeutic Candidate
Bibliographic record
Abstract
Neuroactive peptides derived from venomous species have proven to be used as a lead compound for treating neurological diseases. In the present study, the primary structure of the peptide toxins of snakes, scorpions, spiders, cone snails, honey bees, and sea anemones was recovered from different toxin databases. The 3-D structures of the peptide toxins were analyzed with respect to secondary structural elements such as cysteine patterns and disulfide connectivity’s using PYMOL. Their interaction with ion channels/receptors was studied because of its pharmacological importance. The toxins retrieved were found to have – C–Xn–C–Xn–CC–Xn–C–Xn–C-- cysteine pattern for n≥1 that was the same --C---C---CC---C---C— cysteine pattern of ω-conotoxin and hanatoxin, but with a varying intervening non-cysteine residue between cysteines. Hence, these provide insight for structure-based drug design using these peptide toxin scaffolds. Given the optimal molecular weight and specificity of peptides compared to conventional small molecule drugs, peptides are considered future next-generation drug candidates.
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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".