PCPI: Prediction of circRNA and Protein Interaction Using Machine Learning Method

Circular RNA (circRNA) is an RNA molecule different from linear RNA with covalently closed loop structure. CircRNAs can act as sponging miRNAs and can interact with RNA binding protein. Previous studies have revealed that circRNAs play important role in the development of different diseases. The biological functions of circRNAs can be investigated with the help of circRNA-protein interaction. Due to scarce circRNA data, long circRNA sequences and the sparsely distributed binding sites on circRNAs, much fewer endeavors are found in studying the circRNA-protein interaction compared to interaction between linear RNA and protein. With the increase in experimental data on circRNA, machine learning methods are widely used in recent times for predicting the circRNA-protein interaction. The existing methods either use RNA sequence or protein sequence for predicting the binding sites. In this paper, we present a new method PCPI (Predicting CircRNA and Protein Interaction) to predict the interaction between circRNA and protein using support vector machine (SVM) classifier. We have used both the RNA and protein sequences to predict their interaction. The circRNA sequences were converted in pseudo peptide sequences based on codon translation. The pseudo peptide and the protein sequences were classified based on dipole moments and the volume of the side chains. The 3-mers of the classified sequences were used as features for training the model. Several machine learning model were used for classification. Comparing the performances, we selected SVM classifier for predicting circRNA-protein interaction. Our method achieved 93% prediction accuracy.