图形-pMHC:图形神经网络方法用于MHCII类呈现和抗体免疫性
William John Thrift1, Jason Perera1, Sivan Cohen1
1Genentech, 1 DNA Way, South San Francisco, California 94080, USA.
Briefings in bioinformatics
|March 30, 2024
概括
我们开发了图形-pMHC,一种新型图形神经网络,以准确预测-MHCII类呈现. 这种方法提高了预测的准确性,并有助于评估抗体药物免疫性风险.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- -MHC二级 (pMHCII) 呈现对于适应性免疫至关重要,但可以引起抗药反应.
- 质谱学和机器学习的进步改善了pMHCII演示建模.
- 准确的pMHCII预测对于了解免疫反应和药物疗效至关重要.
研究的目的:
- 开发一种新的图形神经网络方法,图形-pMHC,用于预测pMHCII呈现.
- 为了提高pMHCII演示建模的准确性.
- 创建一种评估抗体药物免疫性风险的方法.
主要方法:
- 开发了graph-pMHC,一个使用AlphaFold2-multimer用于邻近矩阵的图形神经网络.
- 采用图形计数来解决-MHC结合槽对齐问题.
- 从临床试验数据创建了一个抗体药物免疫性数据集.
主要成果:
- 图形pMHC显著优于现有方法,包括NetMHCIIpan-4.0,绝对平均精度提高了+20.17%.
- 与单独OASis过相比,开发的方法在预测抗体药物免疫性方面增加了2.57%的ROC-AUC.
结论:
- 图表-pMHC为预测pMHCII呈现提供了一种优越的方法.
- 该模型有效地帮助评估抗体药物的免疫性风险.
- 这项工作推动了计算免疫学和药物开发策略的发展.
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