Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.
Bioinformatics. 2022 Jan 27;38(4):1141-1143. doi: 10.1093/bioinformatics/btab762.
Understanding antibody-antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of binding landscapes, guiding experimental design. Despite this, little effort has been devoted to accurately predicting the binding affinity between antibodies and antigens and to develop tailored docking scoring functions for this type of interaction. Here, we developed CSM-AB, a machine learning method capable of predicting antibody-antigen binding affinity by modelling interaction interfaces as graph-based signatures.
CSM-AB outperformed alternative methods achieving a Pearson's correlation of up to 0.64 on blind tests. We also show CSM-AB can accurately rank near-native poses, working effectively as a docking scoring function. We believe CSM-AB will be an invaluable tool to assist in the development of new immunotherapies.
CSM-AB is freely available as a user-friendly web interface and API at http://biosig.unimelb.edu.au/csm_ab/datasets.
Supplementary data are available at Bioinformatics online.
理解抗体-抗原相互作用是提高它们的结合亲和力和特异性的关键。虽然实验方法是开发新疗法的基础，但计算方法可以快速评估结合景观，指导实验设计。尽管如此，人们几乎没有努力准确预测抗体和抗原之间的结合亲和力，并为这种类型的相互作用开发定制的对接评分函数。在这里，我们开发了 CSM-AB，这是一种通过将相互作用界面建模为基于图的特征来预测抗体-抗原结合亲和力的机器学习方法。
CSM-AB 优于替代方法，在盲测中达到了高达 0.64 的 Pearson 相关系数。我们还表明，CSM-AB 可以准确地对近天然构象进行排序，有效地作为对接评分函数。我们相信 CSM-AB 将成为协助开发新免疫疗法的宝贵工具。
CSM-AB 可作为用户友好的网络界面和 API 在 http://biosig.unimelb.edu.au/csm_ab/datasets 上免费获得。
补充数据可在 Bioinformatics 在线获得。
