De Novo Antibody Design with SE(3) Diffusion.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-12-27 DOI:10.1089/cmb.2024.0768
Daniel Cutting, Frédéric A Dreyer, David Errington, Constantin Schneider, Charlotte M Deane
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Abstract

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.

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基于SE(3)扩散的从头抗体设计。
我们介绍了一种基于一般蛋白质骨架扩散框架的抗体可变结构域扩散模型IgDiff,并将其扩展到多链。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IgDiff产生了高度可设计的抗体,可以包含新的结合区域。样品结构的骨架二面角与参考抗体分布吻合良好。我们通过实验验证了这些设计的抗体,发现它们都具有高表达率。最后,我们将我们的模型与最先进的生成骨架扩散模型在一系列抗体设计任务上进行了比较,例如互补性决定区域的设计或轻链与现有重链的配对,并显示出改进的性能和可设计性。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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