Protein Inverse Folding From Structure Feedback

• URL: http://arxiv.org/abs/2506.03028v1
• Date: Tue, 03 Jun 2025 16:02:12 GMT
• Title: Protein Inverse Folding From Structure Feedback
• Authors: Junde Xu, Zijun Gao, Xinyi Zhou, Jie Hu, Xingyi Cheng, Le Song, Guangyong Chen, Pheng-Ann Heng, Jiezhong Qiu,
• Abstract summary: We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
• Score: 78.27854221882572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The inverse folding problem, aiming to design amino acid sequences that fold into desired three-dimensional structures, is pivotal for various biotechnological applications. Here, we introduce a novel approach leveraging Direct Preference Optimization (DPO) to fine-tune an inverse folding model using feedback from a protein folding model. Given a target protein structure, we begin by sampling candidate sequences from the inverse-folding model, then predict the three-dimensional structure of each sequence with the folding model to generate pairwise structural-preference labels. These labels are used to fine-tune the inverse-folding model under the DPO objective. Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning not only improves sequence recovery of baseline models but also leads to a significant improvement in average TM-Score from 0.77 to 0.81, indicating enhanced structure similarity. Furthermore, iterative application of our DPO-based method on challenging protein structures yields substantial gains, with an average TM-Score increase of 79.5\% with regard to the baseline model. This work establishes a promising direction for enhancing protein sequence design ability from structure feedback by effectively utilizing preference optimization.

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