TooT-SS: Transfer Learning using ProtBERT-BFD Language Model for Predicting Specific Substrates of Transport Proteins
Bibliographic record
Abstract
Transmembrane transport proteins are essential in cell life for the passage of substrates across cell membranes. Metabolic network reconstruction requires transport reactions that describe the specific substrate transported as well as the metabolic reactions of enzyme catalysis. We utilize a protein language model called ProtBERT (Protein Bidirectional Encoder Representations from Transformers) and transfer learning with a one-layer Feed-Forward Neural Network (FFNN) to predict 96 specific substrates. We automatically construct a dataset UniProt-SPEC-100 using the ChEBI and GO ontologies with 4,455 sequences from 96 specific substrates. This dataset is extremely imbalanced with a ratio of 1:408 between the smallest class and the largest. Our model TooT-SS predicts 83 classes out of 96 with an F1-score of 0.92 and Matthews Correlation Coefficient (MCC) of 0.91 on a hold-out test set. The results of 3-fold cross-validation experiments, particularly, on small classes show the potential of transfer learning from the ProtBERT language model for handling imbalanced datasets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".