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AbImmPred:一种使用基于AntiBERTy的序列特征的治疗抗体的免疫性预测方法

Hong Wang¹, Xiaohu Hao¹, Yuzhuo He¹

¹Production and R&D Center I of Life Science Services, GenScript Biotech Corporation, Nanjing, China.

|February 23, 2024

概括

早期预测抗体免疫性对于治疗开发至关重要. 本研究介绍了一种使用AntiBERTy功能和机器学习的计算方法,达到0.7273准确度,优于现有方法.

科学领域:

生物技术是生物技术.
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

免疫性在治疗性抗体开发中构成重大挑战,往往导致不良的免疫反应.
目前用于评估免疫原性的湿实验室方法在早期开发阶段可能耗时且昂贵.

研究的目的:

开发一种高效的计算方法,通过氨基酸序列来预测抗体免疫性.
为了促进抗体治疗药物的早期查,并减轻发展风险.

主要方法:

使用AntiBERTy预训练模型从抗体变量区域提取序列特征.
应用主要组件分析 (PCA) 用于将维度缩小到两个主要组件.
雇佣了AutoGluon来训练和优化机器学习模型,通过5倍交叉验证选择加权组合模型.

主要成果:

开发的计算方法在一个独立的测试数据集上达到0.7273的准确性.
与现有的预测方法相比,拟议的方法在准确度上有9.09%的改进.
该模型在199个商业治疗抗体的数据集上进行了训练和验证.

结论:

拟议的计算方法有效预测抗体免疫性,为早期药物开发提供了宝贵的工具.
这种方法可以加快查过程,减少与开发治疗抗体相关的风险.
这个预测工具的Web服务器可以通过GenScript的官方网站访问.

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