Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, 565-0871, Japan.
Faculty of Data Science, Shiga University, Hikone, 522-8522, Japan.
Chembiochem. 2022 Sep 16;23(18):e202200303. doi: 10.1002/cbic.202200303. Epub 2022 Aug 11.
Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody-specific epitopes. We further applied AbAdapt-AF in an anti-receptor binding domain (RBD) antibody complex benchmark and found AbAdapt-AF outperformed three alternative docking methods. Also, AbAdapt-AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti-RBD antibody complex benchmark. We anticipate that AbAdapt-AF will facilitate prediction of antigen-antibody interactions in a wide range of applications.
抗体以高亲和力和特异性识别其同源抗原，但预测与特定抗体相对应的抗原（表位）的结合位点仍然是一个具有挑战性的问题。为了解决这个问题，我们开发了 AbAdapt，这是一个集成抗体和抗原结构建模与刚性对接的管道，以便为表位预测得出抗体-抗原的特异性特征。在这项研究中，我们系统地评估了将最先进的蛋白质建模方法 AlphaFold 与 AbAdapt 管道集成的影响。通过纳入更准确的抗体模型，我们观察到对接、变构位预测和抗体特异性表位预测的改进。我们进一步将 AbAdapt-AF 应用于抗受体结合域 (RBD) 抗体复合物基准测试中，发现 AbAdapt-AF 优于三种替代对接方法。此外，AbAdapt-AF 在抗 RBD 抗体复合物基准测试中表现出比其他测试的表位预测工具更高的表位预测准确性。我们预计 AbAdapt-AF 将促进广泛应用中抗原-抗体相互作用的预测。
