DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.

Protein-nucleic acid interactions (PNIs) are central to fundamental biological processes, and mutations can disrupt these interactions by altering local structural features and binding free energy. Here, we present DeepPNI, a deep learning regression model that integrates sequence- and structure-based features to estimate mutation-induced changes in binding free energy in protein-nucleic acid complexes. The model was developed using a comprehensive dataset of 1754 mutations spanning protein-DNA and protein-RNA complexes, representing one of the largest curated datasets for PNI binding free energy prediction. Structural features were encoded using an edge-aware relational graph convolutional network, while sequence features were represented using the Evolutionary Scale Modeling 2 protein language model. Despite the increased dataset size and heterogeneity, DeepPNI achieved an overall Pearson correlation coefficient of 0.76 in five-fold cross-validation. Consistent performance was observed across protein-DNA and protein-RNA subsets, datasets grouped by experimental temperature, and external blind test datasets, suggesting robustness against dataset heterogeneity. DeepPNI is freely available as a web server at https://research.iitbhilai.ac.in/molinfo/deeppni.