众中取一：以Genie 2在结构宇宙尺度上设计与支架蛋白质
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2
AI总结：
Genie 2通过架构创新和数据增强扩展蛋白质扩散模型，支持多基序支架，在可设计性、多样性和新颖性上达到最先进水平，树立结构设计新标准。
摘要：
蛋白质扩散模型已成为蛋白质设计的一种有前景的方法。其中开创性模型Genie，在正向和反向过程中非对称地表示蛋白质结构，前者使用简单的高斯噪声，后者使用表达力强的SE(3)-等变注意力。在本工作中，我们引入Genie 2，通过架构创新和大量数据增强，将Genie扩展到捕获更大更多样的蛋白质结构空间。Genie 2通过新颖的多基序框架增加了基序支架能力，该框架设计共现基序，且基序间位置和方向未指定。这使得设计涉及多个相互作用伙伴并执行多种功能的复杂蛋白质成为可能。在无条件生成和条件生成上，Genie 2均达到最先进性能，在可设计性、多样性和新颖性等关键设计指标上优于所有已知方法。Genie 2还解决了比其他方法更多的基序支架问题，并提供更多独特多样的解决方案。综上，这些进展为基于结构的蛋白质设计树立了新标准。Genie 2的推理和训练代码以及模型权重可在https://github.com/aqlaboratory/genie2免费获取。
英文摘要：
Protein diffusion models have emerged as a promising approach for protein design. One such pioneering model is Genie, a method that asymmetrically represents protein structures during the forward and backward processes, using simple Gaussian noising for the former and expressive SE(3)-equivariant attention for the latter. In this work we introduce Genie 2, extending Genie to capture a larger and more diverse protein structure space through architectural innovations and massive data augmentation. Genie 2 adds motif scaffolding capabilities via a novel multi-motif framework that designs co-occurring motifs with unspecified inter-motif positions and orientations. This makes possible complex protein designs that engage multiple interaction partners and perform multiple functions. On both unconditional and conditional generation, Genie 2 achieves state-of-the-art performance, outperforming all known methods on key design metrics including designability, diversity, and novelty. Genie 2 also solves more motif scaffolding problems than other methods and does so with more unique and varied solutions. Taken together, these advances set a new standard for structure-based protein design. Genie 2 inference and training code, as well as model weights, are freely available at: https://github.com/aqlaboratory/genie2.
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。
