用轴向向量让AI学会设计反手肽，实现从同手性到异手性的跨类泛化。
Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
- 在等变向量中注入轴向特征，实现手性泛化
- 在虚拟和真实实验中均优于现有工具
- 首个湿实验验证的从头设计反手肽AI方法
D-型肽结合剂针对L-型蛋白具有重要的治疗潜力。尽管基于机器学习的目标条件肽设计发展迅速，但生成D-型肽结合剂仍鲜有探索。本文表明，通过向E(3)等变（极性）向量特征中引入轴向特征，可实现从同手性（L–L）训练数据到异手性（D–L）设计任务的跨手性泛化。将该方法嵌入潜在扩散模型后，所设计的D-型肽结合剂不仅在虚拟基准测试中表现优于现有工具，还在湿实验中得到验证。据我们所知，本方法是首个经湿实验验证的D-型肽结合剂从头生成式AI，为蛋白质设计中的手性问题提供了新思路。代码见https://github.com/YZY010418/PepMirror。
原文摘要 · Abstract (English)
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
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