DeepPNI用图神经网络和语言模型预测蛋白-核酸突变能量变化。
DeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics
- 融合图结构与蛋白质语言模型，建模突变对结合能影响。
- 在1951个突变上达0.76的皮尔逊相关系数，跨数据集表现稳定。
- 适合生物医学研究者用于疾病相关突变功能预测。
蛋白质与核酸的相互作用对维持细胞功能至关重要，涉及DNA修复、基因表达调控和翻译等过程。蛋白质-核酸复合物中的氨基酸突变常导致严重疾病。实验方法在预测突变效应方面存在局限。本研究构建了包含1951个突变（涵盖蛋白-DNA与蛋白-RNA复合物）的大规模数据集，整合结构与序列特征，提出基于深度学习的回归模型DeepPNI，用于估算突变引起的结合自由能变化。结构特征通过边感知的RGCN编码，序列特征由蛋白质语言模型ESM-2提取。五折交叉验证下平均皮尔逊相关系数（PCC）达0.76，且在蛋白-DNA、蛋白-RNA单独数据集及不同实验温度划分的数据集中表现一致，体现良好泛化性。模型在基于复杂物的五折交叉验证中表现稳健，并在外部数据集验证中优于现有工具。
原文摘要 · Abstract (English)
The interaction between proteins and nucleic acids is crucial for processes that sustain cellular function, including DNA maintenance and the regulation of gene expression and translation. Amino acid mutations in protein-nucleic acid complexes often lead to vital diseases. Experimental techniques have their own specific limitations in predicting mutational effects in protein-nucleic acid complexes. In this study, we compiled a large dataset of 1951 mutations including both protein-DNA and protein-RNA complexes and integrated structural and sequential features to build a deep learning-based regression model named DeepPNI. This model estimates mutation-induced binding free energy changes in protein-nucleic acid complexes. The structural features are encoded via edge-aware RGCN and the sequential features are extracted using protein language model ESM-2. We have achieved a high average Pearson correlation coefficient (PCC) of 0.76 in the large dataset via five-fold cross-validation. Consistent performance across individual dataset of protein-DNA, protein-RNA complexes, and different experimental temperature split dataset make the model generalizable. Our model showed good performance in complex-based five-fold cross-validation, which proved its robustness. In addition, DeepPNI outperformed in external dataset validation, and comparison with existing tools
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