Endowing protein language models with structural knowledge
Bibliographic record
Abstract
MOTIVATION: Protein language models (PLMs) have transformed protein research by learning rich representations from sequence data alone, yet they largely ignore the wealth of structural information now available through advances in structure prediction. Current methods that incorporate structural data often require substantial computational resources and complex architectures, limiting their practical adoption. We present a novel joint sequence and structure embedding method that achieves computational and parameter efficiency while maintaining high performance. Our approach introduces a lightweight integration framework that combines pretrained sequence transformers' self-attention with specialized structural adapters, enabling seamless incorporation of structural knowledge into existing PLMs through these enhanced self-attention mechanisms. RESULTS: The method demonstrates remarkable efficiency, requiring only modest pretraining on 542K protein structures, three orders of magnitude less than the data used to train PLMs, using standard masked language modeling objectives. Despite this lightweight approach, our joint embeddings consistently outperform sequence-only models like ESM-2 while achieving comparable results to more complex structure-based methods that use significantly more parameters and computational resources. This work establishes a new paradigm for protein representation learning that balances performance with practical constraints. By providing computationally efficient joint sequence-structure embeddings, we offer the scientific community an accessible tool that captures both sequential and structural protein information without the computational overhead typically associated with structure-aware models. AVAILABILITY AND IMPLEMENTATION: code and links to checkpoints are available at https://github.com/BorgwardtLab/PST.
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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 | 0.000 | 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 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".