Predicting the specific substrate for transmembrane transport proteins using BERT language model
Bibliographic record
Abstract
Transmembrane transport proteins play a vital role in cells' metabolism by the selective passage of substrates through the cell membrane. Metabolic network reconstruction requires transport reactions that describe the specific substrate transported as well as the metabolic reactions of enzyme catalysis. In this paper, we apply BERT (Bidirectional Encoder Representations from Transformers) language model for protein sequences to predict one of 12 specific substrates. Our UniProt-ICAT-100 dataset is automatically constructed from UniProt using the ChEBI and GO ontologies to identify 4,112 proteins transporting 12 inorganic anion or cation substrates. We classified this dataset using three different models including Logistic Regression with an MCC of 0.81 and accuracy of 97.5%; Feed-forward Neural Networks classifier with an MCC of 0.88 and accuracy of 98.5%. Our third model utilizes a Fine-tuned BERT language model to predict the specific substrate with an MCC of 0.95 and accuracy of 99.3% on an independent test set.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".