SFM-Net:多路蛋白特征网络的选择性融合,用于预测突变时的结合亲和力变化
Chunting Liu1,2, Sudong Cai1, Tong Pan3
1Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
Journal of chemical information and modeling
|March 20, 2025
概括
预测蛋白质与蛋白质相互作用 (PPI) 的突变效应至关重要. SFM-Net是一种新的深度学习模型,有效地整合了序列,结构和进化数据,以准确预测由突变引起的结合亲和力变化.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 基因组学就是基因组学.
背景情况:
- 预测蛋白质与蛋白质相互作用 (PPI) 的突变效应对于理解蛋白质功能和疾病机制至关重要.
- 当前的方法往往难以充分利用复杂的结构信息,并整合多样化的数据源.
- 由于突变而导致的结合亲和力变化 (ΔΔG) 的准确预测是一个重大挑战.
研究的目的:
- 开发一种新的深度学习模型,SFM-Net,用于准确预测突变诱导的结合亲和力变化.
- 为了有效地利用序列,结构和进化信息来提高预测准确度.
- 为应对在突变效应预测中整合多源特征的挑战.
主要方法:
- 开发了SFM-Net,这是一个集成基于图形神经网络 (GNN) 的多路特征提取器的深度学习模型.
- 实施了情境感知选择性融合模块,共同利用序列,结构和进化数据.
- 采用基准测试实验和废弃研究来验证模型的性能.
主要成果:
- SFM-Net展示了来自多个来源的功能的有效和选择性集成.
- 该模型在预测由突变引起的结合亲和力变化 (ΔΔG) 中显示出更高的准确性.
- 废弃性研究证实了拟议方法的稳定性和有效性.
结论:
- SFM-Net提供了一种强大的方法来预测突变对PPI的影响.
- 该模型选择性地融合多种数据源的能力提高了预测准确度.
- 这项工作有助于更好地了解蛋白质结构和功能的突变效应.
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