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 proteinprotein
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.
Recommendation: The DeepInteract server interface along with the train/test datasets, source codes
and supplementary files are freely available on: http://bioserver.iiita.ac.in/deepinteract.