Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations
Bibliographic record
Abstract
Abstract Protein language models are trained to predict amino acid sequences from vast protein databases, while learning to represent proteins as feature vectors. These vector representations have enabled impressive applications, from predicting mutation effects to protein folding. One of the reasons offered for the success of these models is that conserved sequence motifs tend to be important for protein fitness. Yet, the relationship between sequence conservation and fitness can be confounded by the evolutionary and environmental context. Should we therefore look to other data sources that may contain more direct functional information? In this work, we conduct a comprehensive study examining the effects of training protein models to predict nineteen types of text annotations from UniProt. Our results show that finetuning protein models on a subset of these annotations enhances the models’ predictive capabilities on a variety of function prediction tasks. Notably, our model outperforms the search algorithm BLAST, which none of the pre-trained protein models accomplished in our evaluation. Our results suggest that a much wider array of data modalities, such as text annotations, may be tapped to improve protein language models. We host our model checkpoints on https://huggingface.co/h4duan .
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".