Cutting Daniel, Dreyer Frédéric A, Errington David, Schneider Constantin, Deane Charlotte M
Exscientia, Oxford Science Park, Oxford, UK.
Department of Statistics, University of Oxford, Oxford, UK.
J Comput Biol. 2025 Apr;32(4):351-361. doi: 10.1089/cmb.2024.0768. Epub 2024 Dec 27.
We introduce , 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 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.
我们引入了一种基于通用蛋白质主链扩散框架的抗体可变域扩散模型,该模型已扩展为可处理多条链。通过评估我们的模型生成的结构的可设计性和新颖性,我们发现该模型能产生具有高度可设计性且可能包含新型结合区域的抗体。采样结构的主链二面角与参考抗体分布显示出良好的一致性。我们通过实验验证了这些设计的抗体,发现它们均能高产表达。最后,我们在一系列抗体设计任务上,如互补决定区的设计或轻链与现有重链的配对,将我们的模型与一种先进的生成性主链扩散模型进行了比较,结果表明我们的模型具有更好的特性和可设计性。