Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Protein function prediction is essential for elucidating biological processes and accelerating drug discovery. However, the vast number of unannotated protein sequences and the limited availability of experimentally validated functional data remain major challenges. Although deep learning models based on protein sequences or protein-protein interaction networks have shown promise, their performance is still restricted, particularly for proteins without interaction data. Furthermore, many existing approaches treat sequence and structural information separately, potentially resulting in suboptimal feature representations. To address these limitations, we propose an improved graph-based framework that integrates two key innovations: (i) ESM-2, a state-of-the-art protein language model, to generate semantically rich sequence embeddings; and (ii) a hybrid pooling mechanism within graph convolutional blocks to better capture both global and local structural features from AlphaFold2-predicted structures. Experiments on the human proteome demonstrate that our model consistently outperforms existing methods in predicting molecular function, cellular component, and biological process annotations. These findings highlight the advantages of combining advanced sequence representations with enhanced structural learning for accurate and generalizable protein function prediction.
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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 it