跨域蛋白质结合物的潜在检索增强生成
Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
AI总结：
本文提出RADiAnce框架，通过对比潜在空间统一检索与生成，利用已知界面引导跨域蛋白质结合物设计，显著提升结合亲和力与几何恢复，为药物发现开辟新范式。
摘要：
设计靶向特定位点的蛋白质结合物，需要生成真实且功能性的相互作用模式，是药物发现中的一个基本挑战。当前基于结构的生成模型在生成具有足够合理性和可解释性的界面方面存在局限。在本文中，我们提出了用于对齐界面的检索增强扩散模型（RADiAnce），这是一种利用已知界面来指导新型结合物设计的新框架。通过在共享的对比潜在空间中将检索和生成统一起来，我们的模型能够高效地识别给定结合位点的相关界面，并通过条件潜在扩散生成器无缝集成这些界面，从而实现跨域界面转移。大量实验表明，RADiAnce 在多项指标上显著优于基线模型，包括结合亲和力以及几何和相互作用的恢复。额外的实验结果验证了跨域泛化能力，表明从不同域（如肽、抗体和蛋白质片段）检索界面能够增强其他域结合物的生成性能。我们的工作为蛋白质结合物设计建立了一种新范式，成功地将基于检索的知识与生成式人工智能相结合，为药物发现开辟了新的可能性。
英文摘要：
Designing protein binders targeting specific sites, which requires to generate realistic and functional interaction patterns, is a fundamental challenge in drug discovery. Current structure-based generative models are limited in generating nterfaces with sufficient rationality and interpretability. In this paper, we propose Retrieval-Augmented Diffusion for Aligned interface (RADiAnce), a new framework that leverages known interfaces to guide the design of novel binders. By unifying retrieval and generation in a shared contrastive latent space, our model efficiently identifies relevant interfaces for a given binding site and seamlessly integrates them through a conditional latent diffusion generator, enabling cross-domain interface transfer. Extensive exeriments show that RADiAnce significantly outperforms baseline models across multiple metrics, including binding affinity and recovery of geometries and interactions. Additional experimental results validate cross-domain generalization, demonstrating that retrieving interfaces from diverse domains, such as peptides, antibodies, and protein fragments, enhances the generation performance of binders for other domains. Our work establishes a new paradigm for protein binder design that successfully bridges retrieval-based knowledge and generative AI, opening new possibilities for drug discovery.
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。
