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增强疫苗DL用于疫苗候选人预测,并增加了ESM产生的功能

Yichao Chen^1,2, Yuhan Zhang¹, Yongqun He¹

¹University of Michigan, Ann Arbor, MI 48109, USA.

bioRxiv : the preprint server for biology

|September 16, 2024

概括

新的蛋白质折叠特性显著提高了疫苗抗原预测的准确性. 这种机器学习方法通过更有效地识别保护性抗原来改善疫苗设计.

科学领域:

计算生物学计算生物学
疫苗学疫苗学疫苗学
机器学习机器学习

背景情况:

像Vaxign-DL这样的现有机器学习模型使用509个特征预测细菌保护性抗原.
深度学习技术对于推进疫苗设计至关重要.

研究的目的:

评估蛋白质折叠特征对疫苗抗原预测的影响.
为了提高疫苗-DL模型的准确性和性能.

主要方法:

利用ESM程序获得了1,280个蛋白质折叠特征.
集成ESM衍生功能与现有的Vaxign-DL功能进行抗原预测.
在10种细菌病原体上进行了离开一个病原体的验证 (LOPOV).

主要成果:

只有ESM衍生功能的预测性能与原来的Vaxign-DL相美.
结合ESM和传统特征,在七个指标 (特异性,敏感性,AUROC) 上显著改善了预测性能.
在LOPOV研究中,使用ESM特征预测疫苗抗原显著改善.

结论:

蛋白质折叠特征为疫苗抗原预测提供了附加值.
增强的Vaxign-DL模型与ESM功能显示了改进的预测能力.
这项研究开创了在疫苗抗原发现中使用蛋白质折叠特征的先驱.

关键词:

深度学习 (Deep Learning) 是一种深度学习.进化的规模建模 (ESM)蛋白质折叠蛋白质的折叠这是逆向疫苗学.疫苗抗原是一种疫苗抗原.疫苗-DL 疫苗-DL 是一个疫苗.疫苗-ML 疫苗-ML 疫苗-ML 疫苗

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