Deep Convolutional Neural Networks for Predicting Hydroxyproline in Proteins

Background: Protein hydroxyproline is one type of post translational modification (PTM). Because protein sequence contains many uncharacterized residues of P, the question that needs to be answered is: Which ones can be hydroxylated, and which ones cannot? The solution will not only give a deeper understanding of the hydroxylation mechanism but can also lead to drug development. The evergrowing demand for better handling of protein sequences in the post-genomic age presents new prediction challenges. Objective: To address these challenges, developing computational methods to identify these sites quickly and accurately is our objective. Method: We propose a new approach for predicting hydroxyproline using the deep learning model known as the convolutional neural network (CNN), and employed a pseudo amino acid composition (PseAAC) to identify these proteins and used the position-specific scoring matrix (PSSM) to represent samples as input to the CNN model. Results and Conclusion: In our experiment, K-fold cross-validation testing on benchmark datasets further demonstrated the potential for CNN identification of protein hydroxyproline as well as other PTM type proteins.