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BRAVE:使用蛋白质的大型语言模型预测HIV-1抗体耐药性的高精度方法

Mohammed El Anbari¹, Tatsiana Bylund¹, Sijy O'Dell¹

¹Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.

bioRxiv : the preprint server for biology

|September 19, 2025

概括

一个新的机器学习工具BRAVE准确地预测了人类免疫缺陷病毒-1 (HIV-1) 对广泛中和抗体 (bNAbs) 的耐药性. BRAVE的性能优于现有方法,有助于对HIV-1治疗和预防策略的临床决策.

科学领域:

免疫学免疫学免疫学
病毒学病毒学
计算生物学计算生物学

背景情况:

广泛中和抗体 (bNAbs) 对于HIV-1预防和治疗至关重要.
快速的HIV-1突变导致中和耐药性,限制了bNAb的疗效.
需要预测性计算方法来预测HIV-1对bNAbs的耐药性.

研究的目的:

开发和验证一种新的机器学习方法,BRAVE (通过进化规模建模2进行Bnab抵抗分析),用于预测HIV-1对33个bNAbs的抗性.
评估BRAVE的性能与现有的最先进的阻力预测工具相比.

主要方法:

使用Random Forests分类器与蛋白质语言模型集成,以捕获必要的蛋白质特征.
在对抗33个已知的bNAbs的HIV-1分离物上训练并评估了BRAVE.
进行严格的统计比较,将BRAVE与领先的预测工具进行比较.

主要成果:

BRAVE在关键指标上表现出卓越的表现,包括精度,曲线下的面积,对数损失和F1得分.
统计分析证实,与当前最先进的工具相比,BRAVE的准确性明显更高 (p<0.001).
该模型有效地捕捉了与bNAb耐药性相关的蛋白质特征.

结论:

BRAVE代表了预测HIV-1中和耐药性的重大进展.
该工具可以为有关bNAb选择和使用的临床决策提供信息.
布雷夫 (BRAVE) 促进了对HIV-1感染个体病毒逃逸的基于序列的监测.

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