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一个基准来评估基于结构的在线工具的抗体-抗原结合亲和力

Jiayi Xu¹, Jianting Gong², Xiaochen Bo²

¹College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Biophysical chemistry

|May 20, 2024

概括

基准计算工具用于预测由于突变导致的SARS-CoV-2尖端蛋白结合亲和力变化至关重要. 我们的研究评估了使用各种数据集的七种工具,揭示了抗原-抗体相互作用和治疗抗体设计的性能变化.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
结构生物学结构生物学

背景情况:

从误解突变中预测结合亲和力变化有助于理解抗原-抗体相互作用.
许多基于结构的计算工具存在,但为特定研究选择合适的软件,特别是关于SARS-CoV-2尖端蛋白和抗体,仍然具有挑战性.
将这些工具与突变多样性数据集进行基准测试对于SARS-CoV-2研究至关重要.

研究的目的:

为了对七种基于结构的在线计算工具的性能进行基准测试,以预测SARS-CoV-2尖端蛋白-抗体复合体中的结合亲和力变化.
为了提供一个全面的评估,使用一个精心策划的数据集的1216个变体从22个复杂.
引导选择适合用于抗原-抗体相互作用研究和治疗抗体设计的工具.

主要方法:

在22个SARS-CoV-2尖端蛋白和单克隆抗体复合体中,收集和策划了1216种变异的结合亲和力变化的数据集.
应用了七种不同的结合亲和力预测工具来评估它们的性能.
使用Pearson相关性对持续的亲和力变化和分类任务的准确性进行性能评估 (预测亲和力增加/减少).

主要成果:

预测和测量的结合亲和力变化之间的皮尔森相关性范围从-0.158到0.657.
预测亲和力变化的分类准确度从0.444到0.834.4不等.
工具在单个突变上表现更好,特别是在表位位点,但在极度下降的亲缘关系方面表现不佳. 工具性能对用于复杂结构的实验技术不敏感.

结论:

开发的数据集和基准测试结果为SARS-CoV-2抗原-抗体相互作用的当前基于结构的预测工具的功能提供了宝贵的见解.
这些发现将有助于研究人员选择适当的计算工具来分析突变对结合亲和力的影响.
该评估支持增强针对SARS-CoV-2的治疗单克隆抗体的计算设计策略.

关键词:

亲密关系的变化改变了.抗原-抗体结合亲和力亲和力突变突变是一种突变.绩效评价绩效评价绩效评价绩效评价绩效评价这就是SARS-CoV-2病毒.

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