基于SE(3)扩散的从头抗体设计
De novo antibody design with SE(3) diffusion
AI总结：
本研究提出了基于多链蛋白质主链扩散框架的抗体可变域扩散模型IgDiff，实验验证其生成的抗体可设计性高、产量高，在多项抗体设计任务上优于现有最先进模型。
摘要：
我们提出了IgDiff，这是一种抗体可变域扩散模型，它基于经扩展可处理多条链的通用蛋白质主链扩散框架。在评估我们的模型生成结构的可设计性和新颖性时，我们发现IgDiff能够生成高度可设计的抗体，这些抗体可包含全新的结合区域。采样结构的主链二面角与参考抗体分布表现出良好的一致性。我们通过实验验证了这些设计出的抗体，发现所有抗体均能以高产量表达。最后，我们将我们的模型与当前最先进的生成式主链扩散模型在一系列抗体设计任务上进行了比较，例如互补决定区设计或轻链与现有重链的配对，结果表明我们的模型在性能和可设计性上均有所提升。
英文摘要：
We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework which was extended to handle multiple chains. Assessing the designability and novelty of the structures generated with our model, we find that IgDiff produces highly designable antibodies that can contain novel binding regions. The backbone dihedral angles of sampled structures show good agreement with a reference antibody distribution. We verify these designed antibodies experimentally and find that all express with high yield. Finally, we compare our model with a state-of-the-art generative backbone diffusion model on a range of antibody design tasks, such as the design of the complementarity determining regions or the pairing of a light chain to an existing heavy chain, and show improved properties and designability.
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。
