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在基于新抗原的疫苗设计中,用于以自由能量扰动为辅助的机器学习策略,用于仿原体查

Qinglu Zhong^1,2, Kevin C Chan^1,2, Lei Fu^1,2

¹College of Life Sciences, College of Physics, Institute of Quantitative Biology, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.

Briefings in bioinformatics

|July 10, 2025

概括

这项研究引入了一种新的FEP辅助ML (FEPaML) 策略,用于增强癌症免疫治疗. FEPaML精确选新抗原仿原体,改善疫苗设计和免疫反应.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
生物化学生物化学

背景情况:

基于新抗原的免疫治疗是一种有前途的癌症治疗策略.
开发有效的新抗原疫苗需要精确选增强的仿原体.
目前的机器学习 (ML) 模型很难从原始新抗原中区分高亲和度的仿真物,而基于物理的方法是计算密集的.

研究的目的:

开发一种混合计算策略,将机器学习的优势与基于物理的方法相结合,用于准确的模拟物选.
为了提高确定基于新抗原的疫苗增强型仿原体的精度和效率.

主要方法:

提出了一个FEP辅助的ML (FEPaML) 策略,利用贝叶斯优化.
用基于物理的自由能量扰动 (FEP) 评估代精细的ML预测.
应用FEPaML来选代表性新抗原的仿原体.

主要成果:

FEPaML取得了优异的预测精度,超过0.9.
该策略在有限数量的FEP采样中显示出高准确性.
在区分高亲缘关系的仿真物质方面,FEPaML显著优于现有的ML模型.

结论:

该FEPaML战略提供了一种精确而强大的查新抗原仿原体的方法.
这种方法有助于开发更有效的基于新抗原的癌症疫苗.
FEPaML代表了免疫治疗研究计算方法的重大进步.

关键词:

贝叶斯的优化是贝叶斯的优化.自由能量扰动的自由能量扰动.机器学习是机器学习.模拟物种的模拟物种基于新抗原的疫苗

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