Identification of Protein–Protein Interaction (PPI) Sites on the Influenza A (H1N1) Viral Genome Using Gradient Boosting and Artificial Neural Network (ANN) Models

Identification of protein-protein interaction (PPI) sites is crucial for understanding molecular recognition. Experimental identification of PPI is expensive, time-consuming, and laborious. A large number of computational methods addressed this problem. However, no computations specifically addressed PPI site prediction for the frequently mutating influenza A virus (IAV) genome that invades human hosts. For the first time, we report the prediction of PPI sites on the IAV genome (protein sequences). The method was benchmarked across various machine-learning models, optimizing class imbalance and unlabeled data types. The best-performing models were (i) the gradient boosting model, augmented with minority class oversampling and positive unlabeled (PU) learning and (ii) the protein-specific bidirectional encoder representations from transformers (Prot-BERT) combined with an artificial neural network (ANN) (termed the Prot-BERT-ANN model), adjusted with class weight correction and threshold tuning. The models were trained on two types of interaction site data sets: one obtained from diverse protein families (Train-1) (17995 amino acid sites) with known interaction sites from protein structures and the other from the IAV consensus protein sequences (3322 amino acid sites) with experimentally annotated PPI sites on the conserved regions of the proteins (Train-2). External validation was performed on two test data sets: (i) from six IAV proteins, M1, NS1, NEP, NP, PB1, and PB2, reported to interact with host factors, with experimentally annotated PPI sites on the nonconserved region of the proteins (Test-1), and (ii) the SARS-CoV-2 spike protein sequence (195 amino acid sites) (Test-2). Blind prediction was performed on three IAV protein sequencesNA, HA, and M2curated from the Human Viral Interaction Database (HVIDB). The prediction aimed to decipher the effect of amino acid substitutions on the protein-protein interaction sites of the viral genome. The gradient boosting method with oversampling and PU learning, trained on the Train-2 data set, consistently performed better on both external validation data sets. The recall values obtained from the predictions on the Test-1 data set were compared with the published D-SCRIPT (a neural language-based model) results. The gradient boosting model showed a higher average recall value (0.53 ± 0.04) for six IAV proteins compared to the D-SCRIPT results (0.18 ± 0.19). The gradient boosting prediction for the experimentally reported PPI sites on the SARS-CoV-2 spike protein (Test-2 data set) was 55% accurate, despite Test-2 being independent of Train-2. The results indicated the generalizability and interpretability of the gradient boosting model for IAV PPI site predictions. The effects of amino acid substitutions on PPI sites were demonstrated on five Matrix 1 (M1) protein sequences. This approach could be used to identify the PPI sites on newly emerging viral strains (e.g., influenza virus, SARS-CoV-2, etc.) with potential applications for drug design, improvement of drug binding, or drug repurposing, subject to further validation.