Accurate structure prediction of immune proteins using parameter-efficient transfer learning
Bibliographic record
Abstract
Abstract Accurate prediction of immune protein structures is crucial for understanding the immune system and advancing immunotherapy development. While deep learning methods have significantly advanced protein structure prediction by extracting evolutionary constraints from homologous sequences of a target protein, they struggle with immune proteins due to the limited number of known structures and the lack of homologous sequences in hypervariable regions. To address this challenge, we propose ImmuneFold, a transfer learning approach that fine-tunes ESMFold specifically for immune proteins. We leverage low-rank adaption (LoRA), a parameter-efficient fine-tuning technique that requires considerably less memory and substantially fewer parameters. Evaluations on various immune proteins, including T-cell receptors, antibodies, and nanobodies, demonstrate that ImmuneFold outperforms existing methods in prediction accuracy. Furthermore, we apply ImmuneFold to develop a zero-shot protocol for TCR-epitope binding prediction. Unlike previous supervised methods suffering from severe overfitting due to limited experimental binding data, our approach first predicts TCR-epitope structure using ImmuneFold and then directly estimates the binding affinity by calculating Rosseta energy. Evaluations on experimental binding datasets suggest that our method is robust and accurate in predicting TCR-epitope binding. In summary, ImmuneFold demonstrates accurate predictions of immune protein structures and TCR-epitope binding, highlighting its potential to advance the development of immunotherapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".