基于结构的抗体-抗原结合亲和力在线工具评估基准

IF 3.3 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Jiayi Xu , Jianting Gong , Xiaochen Bo , Yigang Tong , Zilin Ren , Ming Ni
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引用次数: 0

摘要

预测错义突变引起的结合亲和力变化可以阐明抗原与抗体之间的相互作用。目前已经提出了一些基于结构的在线计算工具。然而,为特定研究选择合适的软件具有挑战性,尤其是有关 SARS-CoV-2 棘突蛋白与抗体的研究。因此,对突变多样的 SARS-CoV-2 数据集进行基准测试至关重要。在这里,我们从 22 个 SARS-CoV-2 S 蛋白和 22 种单克隆抗体的复合物中收集了 1216 个关于抗原结合亲和力变化的变异数据集,并将其用于评估 7 种结合亲和力预测工具的性能。测试工具在预测和测量的结合亲和力变化之间的皮尔逊相关性介于-0.158和0.657之间,而在预测亲和力增减的分类任务中的准确性介于0.444和0.834之间。这些工具在预测单个突变(尤其是表位位点上的突变)时表现相对较好,而在预测亲和力极度下降时表现较差。测试工具对用于获取复合物结构的实验技术相对不敏感。总之,我们构建了一个数据集列表,并评估了一系列基于结构的在线预测工具,这些工具将解释抗原-抗体相互作用的相关过程,并提高治疗性单克隆抗体的计算设计能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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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来源期刊
Biophysical chemistry
Biophysical chemistry 生物-生化与分子生物学
CiteScore
6.10
自引率
10.50%
发文量
121
审稿时长
20 days
期刊介绍: Biophysical Chemistry publishes original work and reviews in the areas of chemistry and physics directly impacting biological phenomena. Quantitative analysis of the properties of biological macromolecules, biologically active molecules, macromolecular assemblies and cell components in terms of kinetics, thermodynamics, spatio-temporal organization, NMR and X-ray structural biology, as well as single-molecule detection represent a major focus of the journal. Theoretical and computational treatments of biomacromolecular systems, macromolecular interactions, regulatory control and systems biology are also of interest to the journal.
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