在Spike-ACE2深度突变相互作用集上基于结构的结合亲和度预测器的准确性进行基准测试.
Burcu Ozden1,2, Eda Şamiloğlu1,2, Atakan Özsan1
1Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey.
Proteins
|November 22, 2023
概括
用于预测SARS-CoV-2 Spike-ACE2结合亲和力的六种计算工具进行了基准测试. 没有一个准确预测的实验数据,强调了对宿主-病原体系统改进的结合亲和力预测器的需求.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 病毒学 病毒学
背景情况:
- 了解SARS-CoV-2 Spike-ACE2相互作用对于抗击疫情至关重要.
- 深度突变扫描研究已经产生了大量关于影响Spike和ACE2结合的突变的数据.
研究的目的:
- 为了比较常用的基于结构的结合亲和度预测器的性能.
- 通过使用来自Spike-ACE2接口突变的深度突变扫描研究的实验数据来评估预测因素.
主要方法:
- 选择并测试了六种基于结构的结合亲和力预测器 (FoldX,EvoEF1,MutaBind2,SSIPe,HADDOCK,UEP).
- 预测者与来自深度突变扫描的实验性结合数据进行了基准测试.
- 使用相关系数和二进制分类准确度来评估性能.
主要成果:
- 没有任何测试的预测因子与实验结合亲和力数据显示出有意义的相关性.
- 折叠X实现了最好的相关性 (R = -0.51) 和二进制分类准确性 (64%).
- 简单的能量评分功能超过了基于进化的方法,人工智能方法的表现与基于现场的强力技术相美.
结论:
- 目前基于结构的结合亲和度预测器需要显著改进,以预测宿主-病原体系统中变异诱导的结合变化.
- 该研究提供了有价值的基准测试数据和可视化的突变模型,以促进未来的预测器开发.
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