用AI设计全新酶，实现天然不存在的化学反应。
General Multimodal Protein Design Enables DNA-Encoding of Chemistry
- 通过多模态模型联合设计蛋白序列与三维结构，无需预设催化残基。
- 设计出新型血红素酶，催化碳烯转移等新反应，活性超过工程酶。
- 适合合成生物学、酶设计与药物开发领域研究者参考。
进化是酶多样性的重要驱动力，但其所探索的化学反应仅占DNA可编码范围的一小部分。深度生成模型可设计结合配体的新蛋白，但均需预先指定催化残基。本文提出DISCO（DIffusion for Sequence-structure CO-design），一种多模态模型，可围绕任意生物分子协同设计蛋白序列与3D结构，并引入推理时扩展方法，在两个模态间优化目标。仅以反应中间体为条件，DISCO设计出具有新颖活性位点构型的多样化血红素酶，催化自然界中不存在的碳烯转移反应，包括烯烃环丙烷化、螺环丙烷化、B-H及C(sp³)-H插入反应，其催化活性超过现有工程酶。对选定设计进行随机突变后，经定向进化进一步提升了酶活性。DISCO为可进化酶提供了可扩展的设计路径，拓展了基因可编码转化的潜力。代码已开源：https://github.com/DISCO-design/DISCO。
原文摘要 · Abstract (English)
Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp$^3$)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。