Multimodal deep representation learning for protein interaction identification and protein family classification

Abstract

Background: Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge.

Results: In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods.

Conclusion: To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

Keywords: Knowledge graph representation learning; Multimodal deep neural network; Protein-protein interaction network.

Fig. 1

Multi-modal Deep Representation Learning Framework

Fig. 2

Metapath Generation

Fig. 3

Protein Family Classification Deep Neural Network

Fig. 4

SAE Loss for S.cerevisiae Species

Fig. 5

5-CV AUC-ROC score for eight species

Fig. 6

a Comparison of ACC score between our method and traditional machine learning techniques over eight species b Comparison of Recall score between our method and traditional machine learning techniques over eight species

Fig. 7

Comparison of the AUC-ROC score between our method and traditional machine learning methods over eight species

Fig. 8

5-CV AUC-ROC score for HPRD dataset

Fig. 9

Training and Validation Accuracy

Fig. 10

Protein Multi-Family Classification Micro-F1 Score

Fig. 11

Protein Multi-Family Classification Macro-F1 Score