DeepMaT: Prediction of Target Peptide Classification and Cleavage Site by Combining Mamba2 and Multiple Attention Mechanisms
Why this work is in the frame
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Bibliographic record
Abstract
Signal peptides and transit peptides are essential for directing mature proteins to their proper cellular locations, particularly through cleavage following transport. Although various prediction tools achieve strong performance in identifying and classifying targeting peptides, their accuracy in determining cleavage sites remains limited. We introduce DeepMaT, a deep learning model that integrates Mamba2 and a multihead self-attention mechanism, leveraging the global modeling capabilities of Mamba2 and the localized focus of self-attention. Experimental results show that DeepMaT significantly outperforms state-of-the-art models in cleavage site prediction, achieving an accuracy of 0.867 for thylakoid transit peptides and also performing well on other peptides. Moreover, DeepMaT can accurately learn the amino acid distribution of real samples. Ablation experiments show that the combination of Mamba and attention mechanisms can improve model efficiency, further proving the effectiveness of the combination. It also enables prediction of targeting peptides with unspecified cleavage sites, offering a valuable tool for protein database annotation. DeepMaT is freely available on GitHub at https://github.com/qianmao2001/DeepMaT.
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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 it