A benchmark for evaluation of structure-based online tools for antibody-antigen binding affinity

文献类型： 外文期刊

第一作者： Xu, Jiayi

作者： Xu, Jiayi;Tong, Yigang;Gong, Jianting;Bo, Xiaochen;Ni, Ming;Tong, Yigang;Ren, Zilin;Ren, Zilin

作者机构：

关键词： Antigen -antibody binding affinity; Affinity change; Mutation; SARS-CoV-2; Performance evaluation

期刊名称：BIOPHYSICAL CHEMISTRY （ 影响因子：3.8； 五年影响因子：2.6 ）

ISSN： 0301-4622

年卷期： 2024 年 311 卷

页码：

收录情况： SCI

摘要： The prediction of binding affinity changes caused by missense mutations can elucidate antigen -antibody interactions. A few accessible structure -based online computational tools have been proposed. However, selecting suitable software for particular research is challenging, especially research on the SARS-CoV-2 spike protein with antibodies. Therefore, benchmarking of the mutation -diverse SARS-CoV-2 datasets is critical. Here, we collected the datasets including 1216 variants about the changes in binding affinity of antigens from 22 complexes for SARS-CoV-2 S proteins and 22 monoclonal antibodies as well as applied them to evaluate the performance of seven binding affinity prediction tools. The tested tools' Pearson correlations between predicted and measured changes in binding affinity were between - 0.158 and 0.657, while accuracy in classification tasks on predicting increasing or decreasing affinity ranged from 0.444 to 0.834. These tools performed relatively better on predicting single mutations, especially at epitope sites, whereas poor performance on extremely decreasing affinity. The tested tools were relatively insensitive to the experimental techniques used to obtain structures of complexes. In summary, we constructed a list of datasets and evaluated a range of structure -based online prediction tools that will explicate relevant processes of antigen -antibody interactions and enhance the computational design of therapeutic monoclonal antibodies.

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