Combining sequence and network information to enhance protein-protein interaction prediction

• Published in: BMC bioinformatics 2020-12, Vol.21 (Suppl 16), p.537-537, Article 537
• Main Authors: Liu, Leilei, Zhu, Xianglei, Ma, Yi, Piao, Haiyin, Yang, Yaodong, Hao, Xiaotian, Fu, Yue, Wang, Li, Peng, Jiajie
• Format: Article
• Language: English
• Subjects: Accuracy; Amino Acid Sequence; Amino acids; Cellular structure; Competitiveness; Databases, Protein; Datasets; Experiments; Gene expression; Graph convolutional networks; Humans; Networks; Neural networks; Predictions; Protein interaction; Protein Interaction Mapping - methods; Proteins; Proteins - chemistry; Proteins - metabolism; Protein–protein interactions; Representations; Research methodology; Research methods; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - metabolism; Structure-function relationships
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-03896-6

Protein-protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology. Recently, graph convolutional networks (GCNs) have been proposed to capture the graph structure information and generate representations for nodes in the graph. In our paper, we use GCNs to learn the position information of proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent. Combining amino acid sequence information and position information makes a stronger representation for protein, which improves the accuracy of PPIs prediction. In previous research methods, most of them only used protein amino acid sequence as input information to make predictions, without considering the structural information of PPIs networks graph. We first time combine amino acid sequence information and position information to make representations for proteins. The experimental results indicate that our method has strong competitiveness compared with several sequence-based methods.