A Combinational Strategy upon RNA Sequencing and Peptidomics Unravels a Set of Novel Toxin Peptides in Scorpion Mesobuthus martensii
Bibliographic record
Abstract
Scorpion venom is deemed to contain many toxic peptides as an important source of natural compounds. Out of the two hundred proteins identified in Mesobuthus martensii (M. martensii), only a few peptide toxins have been found so far. Herein, a combinational approach based upon RNA sequencing and Liquid chromatography-mass spectrometry/mass spectrometry (LC MS/MS) was employed to explore the venom peptides in M. martensii. A total of 153 proteins were identified from the scorpion venom, 26 previously known and 127 newly identified. Of the novel toxins, 97 proteins exhibited sequence similarities to known toxins, and 30 were never reported. Combining peptidomic and transcriptomic analyses, the peptide sequence of BmKKx1 was reannotated and four disulfide bridges were confirmed within it. In light of the comparison of conservation and variety of toxin amino acid sequences, highly conserved and variable regions were perceived in 24 toxins that were parts of two sodium channel and two potassium channel toxins families. Taking all of this evidences together, the peptidomic analysis on M. martensii indeed identified numerous novel scorpion peptides, expanded our knowledge towards the venom diversity, and afforded a set of pharmaceutical candidates.
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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".