Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction
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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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".