Prediction of Human Papillomavirus-Host Oncoprotein Interactions Using Deep Learning - Santa, Sheila; Kwofie, Samuel Kojo; Agyenkwa-Mawuli, Kwasi; Quaye, Osbourne; Brown, Charles A; Tagoe, Emmanuel A (Bioinformatics and biology insights, 2024)
Background: Human papillomavirus (HPV) causes disease through complex interactions between viral and host proteins, with the PI3K signaling pathway playing a key role. Proteins like AKT, IQGAP1, and MMP16 are involved in HPV-related cancer development. Traditional methods for studying protein-protein interactions (PPIs) are labor-intensive and time-consuming. Computational models are becoming more popular as they are less labor-intensive and often more efficient. This study aimed to develop a deep learning model to predict interactions between HPV and host proteins. Method: To achieve this, available HPV and host protein interaction data was retrieved from the protocol of Eckhardt et al and used to train a Recurrent Neural Network algorithm. Training of the model was performed on the SPYDER (scientific python development environment) platform using python libraries; Scikit-learn, Pandas, NumPy, and TensorFlow. The data was split into training, validation, and testing sets in the ratio 7:1:2, respectively. After the training and validation, the model was then used to predict the possible interactions between HPV 31 and 18 E6 and E7, and host oncoproteins AKT, IQGAP1 and MMP16. Results: The model showed good performance, with an MCC score of 0.7937 and all other metrics above 88%. The model predicted an interaction between E6 and E7 of both HPV types with AKT, while only HPV31 E7 was shown to interact with IQGAP1 and MMP16 with confidence scores of 0.9638 and 0.5793, respectively. Conclusion: The current model strongly predicted HPVs E6 and E7 interactions with PI3K pathway, and the viral proteins may be involved in AKT activation, driving HPV-associated cancers. This model supports the robust prediction of interactomes for experimental validation.