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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

针对抗体工程的抗原结合亲缘关系的精确in silico预测

Tuğçe Uluçay¹, Merve Arslan², Hatice Döşeme³

¹Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Balçova, Izmir, Turkey; Izmir Institute of Technology, Gülbahçe Campus, Urla, Izmir, Turkey.

Advances in protein chemistry and structural biology

|September 19, 2025

概括

抗体工程的计算工具在预测结合亲和力和突变效应方面显示出较低的准确性. 改进需要解决高亲缘关系数据的不足以及训练集中的有利突变.

科学领域:

生物化学生物化学
计算生物学计算生物学
免疫学免疫学免疫学

背景情况:

抗体在诊断和治疗中非常重要,因为它们的特异性和亲和力.
测量抗体-抗原亲和力对于发育至关重要,但可能耗时且昂贵.
在 silico 方法提供了高通量,成本效益的替代品,用于亲和度评估和理解绑定驱动器.

研究的目的:

为了评估六个用于抗体工程的高通量in silico服务器的性能.
评估这些工具在预测抗体-抗原结合常数和单点突变影响方面的准确性.
确定当前用于抗体开发的in silico方法的局限性.

主要方法:

测试了六个不同的高吞吐量服务器.
在两个关键的抗体工程场景中评估了性能: de novo亲和力预测和预测来自单点突变的结合自由能量变化.
分析了突变生物物理特征对预测准确性的影响.

主要成果:

在 silico 工具在预测高亲缘关系结合和有利突变方面表现出明显低的准确性.
模型训练集往往低于高亲和抗体-抗原复合体和有益突变.
发现突变的生物物理性质,如体积和疏水性的变化,会增加预测错误.

结论:

当前的in silico工具需要重新参数化,以在可接受的边际内预测突变影响 (1kcal/mol).
准确的 de novo 预测抗体亲和力可能需要对这些服务器中使用的底层物理模型进行根本性修订.
解决训练集中的数据限制对于推进计算抗体设计至关重要.

关键词:

亲和关系预测预测.抗体是对抗体的一种.抗体-抗原相互作用具体性具体性具体性

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