Retrieved Sequence Augmentation for Protein Representation Learning
Bibliographic record
Abstract
Abstract The advancement of protein representation learning has been significantly influenced by the remarkable progress in language models. Accordingly, protein language models perform inference from individual sequences, thereby limiting their capacity to incorporate evolutionary knowledge present in sequence variations. Existing solutions, which rely on Multiple Sequence Alignments (MSA), suffer from substantial computational overhead and suboptimal generalization performance for de novo proteins. In light of these problems, we introduce a novel paradigm called Retrieved Sequence Augmentation (RSA) that enhances protein representation learning without necessitating additional alignment or preprocessing. RSA associates query protein sequences with a collection of structurally or functionally similar sequences in the database and integrates them for subsequent predictions. We demonstrate that protein language models benefit from retrieval enhancement in both structural and property prediction tasks, achieving a 5% improvement over MSA Transformer on average while being 373 times faster. Furthermore, our model exhibits superior transferability to new protein domains and outperforms MSA Transformer in de novo protein prediction. This study fills a much-encountered gap in protein prediction and brings us a step closer to demystifying the domain knowledge needed to understand protein sequences. Code is available at https://github.com/HKUNLP/RSA .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".