TransABseq:一种基于蛋白质序列的两阶段方法,用于预测基因突变后的抗原-抗体结合 afinity 变化
Cui-Feng Li1, Zihao Yan2, Fang Ge3
1School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China.
Journal of chemical information and modeling
|May 12, 2025
概括
TransABseq 预测突变如何影响抗原-抗体相互作用,使用一种新的两阶段计算框架. 该工具增强了对抗体功能在疾病检测和治疗开发中的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 抗原-抗体相互作用对于宿主防御,诊断和治疗至关重要.
- 抗体的特异性和亲和力是它们临床应用的关键.
- 预测突变对这些相互作用的影响对于药物开发至关重要.
研究的目的:
- 开发一种新的计算框架TransABseq,用于预测误解突变对抗原-抗体相互作用的影响.
- 为了提高预测突变对抗体结合的影响的准确性和稳定性.
主要方法:
- TransABseq采用两级架构,结合了蛋白质语言模型嵌入,变压器编码器和多尺度卷积模块.
- 深度特征融合策略整合了生物化学特性,全球序列依赖性和层次特征.
- 在XGBOOST模型执行基于融合特征的定量预测.
主要成果:
- 在交叉验证中,TransABseq表现出强的表现,平均PCC值为0.607-0.843,RMSE值为1.166-1.337 kcal/mol.
- 该模型在盲测试数据上显示出优异的预测准确性,PCC为0.721和RMSE为0.925kcal/mol.
- 深度功能融合策略有效地揭示了蛋白质的生化特性.
结论:
- TransABseq提供了一个强大而准确的计算工具,用于预测抗原-抗体相互作用的突变效应.
- 该框架的创新架构和功能融合增强了预测能力.
- 公共可用的代码和数据有助于进一步研究抗体工程和药物发现.
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