通过轴向向量实现异手性蛋白质-肽相互作用设计的跨手性泛化
Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
AI总结：
提出向E(3)等变（极）向量特征注入轴向特征的方法，结合潜在扩散模型实现从同手性训练数据到异手性设计任务的跨手性泛化，首次通过湿实验验证了生成式AI从头设计D-肽结合物的有效性。
摘要：
靶向L-蛋白的D-肽结合物具有广阔的治疗潜力。尽管基于机器学习的靶标条件肽设计取得了快速进展，但生成D-肽结合物仍基本未被探索。在这项工作中，我们表明通过向$E(3)$等变（极）向量特征注入轴向特征，可以实现从同手性（L--L）训练数据到异手性（D--L）设计任务的跨手性泛化。通过在潜在扩散模型中实现该方法，我们实现了D-肽结合物设计，不仅在 extit{in silico}基准测试中优于现有工具，而且在湿实验验证中显示出有效性。据我们所知，我们的方法代表了首个经过湿实验验证的用于 extit{de novo}设计D-肽结合物的生成式AI，为处理蛋白质设计中的手性提供了新视角。代码可在https://github.com/YZY010418/PepMirror获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
