DeepInteract: Deep Neural Network Based Protein-Protein Interaction Prediction Tool

Background: Proteins form specific molecular complexes and the specificity of its interaction is highly essential for discovering and analyzing cellular mechanisms. Aim: The development of large-scale high-throughput experiments using in silico approach has resulted in the production of accurate data which has accelerated the uncovering of novel protein-protein interactions (PPIs). Method: In this work we present an integrative domain-based method, 'DeepInteract' for predicting PPIs using Deep Neural Network (DNN). The interacting set of PPIs was extracted from the Database of Interacting Proteins (DIP) and Kansas University Proteomics Service (KUPS). Results: When validating the performance on an independent dataset of 34100 PPIs of Saccharomyces cerevisiae the proposed classifier achieved promising prediction result with accuracy, precision, sensitivity and specificity of 92.67%, 98.31%, 86.85% and 98.51%, respectively. Similar classifiers were implemented on protein complexes for Escherichia coli, Drosophila melanogaster, Homo sapiens and Caenorhabditis elegans, with prediction accuracy achieved of 97.01%, 90.85%, 94.47% and 88.91% respectively. Conclusion: The performance of this proposed method is found to be better than the existing domain-based machine learning PPI prediction approaches.