Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization

• URL: http://arxiv.org/abs/2410.19471v1
• Date: Fri, 25 Oct 2024 11:04:02 GMT
• Title: Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization
• Authors: Ryan Park, Darren J. Hsu, C. Brian Roland, Maria Korshunova, Chen Tessler, Shie Mannor, Olivia Viessmann, Bruno Trentini,
• Abstract summary: Inverse folding models predict amino acid sequences that fold into desired reference structures. ProteinMPNN, a message-passing encoder-decoder model, are trained to reliably produce new sequences from a reference structure. But when applied to peptides, these models are prone to generating repetitive sequences that do not fold into the reference structure.
• Score: 33.131551374836775
• License:
• Abstract: Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures. Models like ProteinMPNN, a message-passing encoder-decoder model, are trained to reliably produce new sequences from a reference structure. However, when applied to peptides, these models are prone to generating repetitive sequences that do not fold into the reference structure. To address this, we fine-tune ProteinMPNN to produce diverse and structurally consistent peptide sequences via Direct Preference Optimization (DPO). We derive two enhancements to DPO: online diversity regularization and domain-specific priors. Additionally, we develop a new understanding on improving diversity in decoder models. When conditioned on OpenFold generated structures, our fine-tuned models achieve state-of-the-art structural similarity scores, improving base ProteinMPNN by at least 8%. Compared to standard DPO, our regularized method achieves up to 20% higher sequence diversity with no loss in structural similarity score.

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