Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910271152324608 |
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| author | Yang, Ziyi Tian, Zitong Jia, Yinjun Zhang, Tianyi Zheng, Jiqing Wang, Hao Su, Yubu He, Juncai Liu, Lei Lan, Yanyan |
| author_facet | Yang, Ziyi Tian, Zitong Jia, Yinjun Zhang, Tianyi Zheng, Jiqing Wang, Hao Su, Yubu He, Juncai Liu, Lei Lan, Yanyan |
| contents | D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating D-peptide binders remains largely unexplored. In this work, we show that by injecting axial features to $E(3)$-equivariant (polar) vector features, it is feasible to achieve cross-chirality generalization from homo-chiral (L--L) training data to hetero-chiral (D--L) design tasks. By implementing this method within a latent diffusion model, we achieved D-peptide binder design that not only outperforms existing tools in \textit{in silico} benchmarks, but also demonstrates efficacy in wet-lab validation. To our knowledge, our approach represents the first wet-lab validated generative AI for the \textit{de novo} design of D-peptide binders, offering new perspectives on handling chirality in protein design. Codes are available at https://github.com/YZY010418/PepMirror |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20176 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design Yang, Ziyi Tian, Zitong Jia, Yinjun Zhang, Tianyi Zheng, Jiqing Wang, Hao Su, Yubu He, Juncai Liu, Lei Lan, Yanyan Biomolecules Machine Learning D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating D-peptide binders remains largely unexplored. In this work, we show that by injecting axial features to $E(3)$-equivariant (polar) vector features, it is feasible to achieve cross-chirality generalization from homo-chiral (L--L) training data to hetero-chiral (D--L) design tasks. By implementing this method within a latent diffusion model, we achieved D-peptide binder design that not only outperforms existing tools in \textit{in silico} benchmarks, but also demonstrates efficacy in wet-lab validation. To our knowledge, our approach represents the first wet-lab validated generative AI for the \textit{de novo} design of D-peptide binders, offering new perspectives on handling chirality in protein design. Codes are available at https://github.com/YZY010418/PepMirror |
| title | Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2602.20176 |