Exscientia, Oxford OX4 4GE, United Kingdom.
Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom.
Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae576.
In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ABodyBuilder2. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties.
The software package is available at https://github.com/Exscientia/ABodyBuilder3 with model weights and data at https://zenodo.org/records/11354577.
本文介绍了基于 ABodyBuilder2 的改进可扩展抗体结构预测模型 ABodyBuilder3。我们通过利用语言模型嵌入,在 CDR 环建模方面取得了新的最先进的准确性,并展示了如何通过精心的松弛策略进一步改进预测结构。最后,我们在模型输出中加入了预测的局部距离差异测试,以更准确地估计不确定性。
软件包可在 https://github.com/Exscientia/ABodyBuilder3 上获得,模型权重和数据可在 https://zenodo.org/records/11354577 上获得。