DDAffinity:使用蛋白质3D结构预测多点突变的结合亲和力的变化
Guanglei Yu1,2,3, Qichang Zhao1,2, Xuehua Bi1,2,3
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics (Oxford, England)
|June 28, 2024
概括
一个新的神经网络DDAffinity通过分析3D结构并考虑本地和全球影响,准确地预测多个突变如何影响蛋白质结合亲和力.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 机器学习 机器学习
背景情况:
- 突变通过改变蛋白质的稳定性,相互作用,结构,功能和表达来推动进化.
- 现有的计算方法由于复杂的协同作用的表现而难以预测多点突变的影响.
研究的目的:
- 开发一种新的计算方法,DDAffinity,用于预测由多点突变引起的蛋白质结合亲和力的变化.
- 解决现有方法的局限性,系统地考虑地方和全球的协同作用.
主要方法:
- 开发了DDAffinity,一种通过蛋白质3D结构的神经网络传递空间和序列信息.
- 采用k-最近邻居残留图来提取蛋白质口袋特征.
- 采用了两步添加高斯噪声策略来捕捉全球拓特征.
主要成果:
- 与最先进的方法相比,DDAffinity显著提高了对多点突变的预测准确性.
- 废弃性研究证实了DDAffinity的成分的有效性.
- 在盲测试,SARS-CoV-2变种分析和抗体优化中表现出有效性.
结论:
- DDAffinity提供了一种强大而准确的方法来预测多个突变对蛋白质结合亲和力的影响.
- 该方法对蛋白质工程和理解病毒进化中的应用具有前景.
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