ArticleProtein engineering, design & selection : PEDS2025
Tuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization.
Article in Protein engineering, design & selection : PEDS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Review
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- Steering generative models for protein design: Aligning and conditioning strategies.Current opinion in structural biology · 2026Review
- Reliable repurposing of the antibody interactome inside the cell.Nature communications · 2026Article
- A novel decoding strategy for ProteinMPNN to design with less visibility to cytotoxic T-lymphocytes.Computational and structural biotechnology journal · 2025Article
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Authors and funding
4 authors.
Funding
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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Registered trials
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