Analyzing Effect of Multi-modality in Predicting Protein-Protein Interactions.

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Abstract

Nowadays, multiple sources of information about proteins are available such as protein sequences, 3D structures, Gene Ontology, etc. Most of the works on protein-protein interaction (PPI) identification had utilized this information about protein, mainly sequence-based, but individually. The new advances in deep learning techniques allow us to leverage multiple sources/modalities of proteins. Some recent works have shown that multi-modal PPI models perform better than uni-modal approaches. This paper investigates whether the performance of the multi-modal PPI models is always consistent or depends on other factors such as dataset distribution, algorithms used to learn features, etc. We have used three modalities for this study: Protein sequence, 3D structure, and GO. Various techniques, including deep learning algorithms, are employed to extract features from multiple sources of proteins. These feature vectors from different modalities are then integrated in several combinations (bi-modal and tri-modal) to predict PPI. To conduct this study, we have used Human and S. cerevisiae PPI datasets. The obtained results demonstrate the potential of a multi-modal approach and deep learning techniques in predicting protein interactions. However, the predictive capability of a model for PPI depends on feature extraction methods as well. Also, increasing the modality does not always ensure performance improvement.