Tuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization
Why this work is in the frame
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Bibliographic record
Abstract
ProteinMPNN is widely used in protein design workflows due to its ability to identify amino acid sequences that fold into specific 3D protein structures. In our work, we adjust ProteinMPNN to design proteins for a given 3D protein structure with reduced immune-visibility to cytotoxic T lymphocytes that recognize proteins via the MHC-I pathway. To achieve this, we developed a novel framework that integrates direct preference optimization (DPO)-a tuning method originally designed for large language models-with MHC-I peptide presentation predictions. This approach fosters the generation of designs with fewer MHC-I epitopes while preserving the protein's original structure. Our results demonstrate that DPO effectively reduces MHC-I visibility without compromising the structural integrity of the proteins.
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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