DeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics

TL;DR

DeepPNI is a deep learning model that predicts how mutations affect protein-nucleic acid binding energies using structural and sequence data, aiding understanding of mutation impacts on cellular functions.

Contribution

The paper introduces DeepPNI, a novel deep learning model combining structural and sequential features to accurately predict mutation-driven energetics in protein-nucleic acid complexes.

Findings

Achieved a Pearson correlation coefficient of 0.76 on large dataset

Demonstrated robustness across different datasets and experimental conditions

Outperformed existing tools in external validation

Abstract

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