DeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics
This addresses the problem of predicting mutational effects in protein-nucleic acid interactions for biomedical research, with incremental improvements over existing tools.
The study tackled predicting mutation-induced binding free energy changes in protein-nucleic acid complexes by building DeepPNI, a deep learning model integrating structural and sequential features, achieving a high average Pearson correlation coefficient of 0.76 on a dataset of 1951 mutations.
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