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机器学习框架可以提取由交叉反应性抗体识别的B细胞表位的物理化学特征

Simranjit Grewal¹, Uwa Iyamu², Daniel Ferrer Vinals²

¹Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada.

NPJ systems biology and applications

|October 2, 2025

概括

开发了一个机器学习框架来分析基阵列数据,揭示了在Plasmodium Duffy结合类蛋白中共享的表位. 这种方法有助于为孕妇设计广泛保护性疟疾疫苗.

科学领域:

免疫学和寄生虫学
计算生物学和生物信息学

背景情况:

怀孕期间的Plasmodium falciparum感染涉及毒性因子VAR2CSA,介导受感染的红细胞粘附于胎盘.
在孕妇中开发抗疟疾疫苗需要针对VAR2CSA的抗体,但其多态性需要识别广泛免疫的保存表位.
对达菲结合蛋白II区域 (DBPII) 的交叉反应性小鼠抗体 (3D10) 意外地识别了多种VAR2CSA等位基因,表明达菲结合类 (DBL) 蛋白内有共同的表位.

研究的目的:

开发一个计算框架来分析复杂的基数组数据以识别保存的表位.
通过交叉反应性抗体 (3D10) 识别的不同Plasmodium Duffy结合类 (DBL) 蛋白质中共享的表位体的研究.
探索机器学习在表位发现中的潜力,以开发针对多态寄生虫抗原的疫苗.

主要方法:

四个DBL蛋白质 (包括VAR2CSA等位基和DBPII) 的选基阵列与交叉反应抗体3D10.
开发并应用了一种机器学习框架,利用决策树从数组数据中提取与3D10绑定相关的特征.
使用独立数据集 (动物Plasmodium DBL蛋白) 和结构映射的验证模型预测,包括测试突变和重组抗原.

主要成果:

机器学习框架成功分析了复杂的基阵列数据,识别了与3D10抗体结合相关的特征.
该方法预测了线性和构造性表观,构造性表观使用重组蛋白质 (例如,PcDBP) 进行验证.
通过交叉反应性抗体识别的表位体的物理化学特性从基阵列数据中成功提取出来.

结论:

机器学习为挖掘序列数据提供了一个强大的工具,以识别像VAR2CSA这样的多态寄生虫蛋白中保存的表位.
该研究确定了达菲结合性蛋白质家族中的共享表位,这对于开发孕妇的超菌株疟疾疫苗至关重要.
这种计算方法可以发现形状表位,提高我们对抗体识别和疫苗设计策略的理解.

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