PLMABFW:使用蛋白质语言模型预测抗体-抗原相互作用的深度学习框架
Yongbing Chen1,2,3, Qianyi Jia1, Xinyue Jia1
1School of Information Science and Technology, and Center of AI for Science, Northeast Normal University, Changchun, China.
Journal of bioinformatics and computational biology
|December 17, 2025
概括
一个新的深度学习框架PLMABFW准确地预测了针对SARS-CoV-2变种的中和抗体. 它克服了同源抗原的高序列相似性所带来的挑战,改善了抗体的发现.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫信息学是指免疫信息学.
- 机器学习在药物发现中的作用
背景情况:
- SARS-CoV-2 的出现需要先进的计算方法来识别中和抗体.
- 现有的基于序列的预测工具在区分同源抗原方面存在局限性,这阻碍了对抗体中和有效性的准确预测.
- 在SARS-CoV-2菌株和抗体框架区域 (FWR) 之间的高序列相似性使抗原-抗体相互作用预测复杂化.
研究的目的:
- 开发一种新的深度学习框架,PLMABFW,用于准确预测中和抗体,特别是解决同源抗原歧视的挑战.
- 通过结合先进的编码技术和网络架构设计,提高抗原-抗体相互作用的预测能力.
- 改进能够中和各种SARS-CoV-2变体的抗体的鉴定.
主要方法:
- 开发了PLMABFW,这是一个深度学习框架,使用预训练的蛋白质语言模型ESM-2进行抗原编码和AntiBERTy进行抗体编码.
- 实施编码技术和网络架构设计,以区分同源抗原.
- 嵌入的抗原特征及其转移版本以丰富抗原信息捕获.
- 利用SARS-CoV-2中和数据集进行模型验证,并采用部分掩盖策略来学习CDR-H3-抗原相互作用.
主要成果:
- 与现有的 AbAgIntPre,DeepAAI,HDOCK 和 LSTM-PHV.等工具相比,PLMABFW 在预测对同源抗原的中和抗体方面表现优异.
- 该框架有效地捕获了复杂的抗原-抗体相互作用,特别突出了抗体CDR-H3区域的作用.
- 使用精选的SARS-CoV-2中和数据集验证了模型的有效性.
结论:
- PLMABFW在中和抗体的计算预测方面取得了重大进展,特别是在挑战同源抗原方面.
- 该框架区分同源抗原和捕获复杂的结合相互作用的能力为更有效的抗体发现和设计铺平了道路.
- 模型代码的开放可用性促进了更广泛的应用和定制,以满足抗体工程和传染病研究中的各种研究需求.
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