Abstract

Protein-protein interactions are the foundations of cellular life activities. At present, the already known protein-protein interactions only account for a small part of the total. With the development of experimental and computing technology, more and more PPI data are mined, PPI networks are more and more dense. It is possible to predict protein-protein interaction from the perspective of network structure. Although there are many high-throughput experimental methods to detect protein-protein interactions, the cost of experiments is high, time-consuming, and there is a certain error rate meanwhile. Network-based approaches can provide candidates of protein pairs for high-throughput experiments and improve the accuracy rate. This paper presents a new link prediction approach “Sim” for PPI networks from the perspectives of proteins' complementary interfaces and gene duplication. By integrating our approach “Sim” with the state-of-art network-based approach “L3,” the prediction accuracy and robustness are improved.

Highlights

  • Protein is the executor of all biological physiological functions, and most of the cell functions are accomplished by interactions of proteins

  • We find several proteins that are recorded as the products of gene duplication events from (PhylomeDB), and generate several organisms’ PPI networks containing them from (STRING)

  • In order to verify the performance of our method on real PPI networks, we select PPI networks of different organisms from several independent data sets: (HINT), (BIOGRID), (STRING), (PrePPI), and (Pajek)

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Summary

Introduction

Protein is the executor of all biological physiological functions, and most of the cell functions are accomplished by interactions of proteins. There are many computational methods based on genome information, genetic evolution (Tsoka and Ouzounis, 2000; Chen et al, 2006; Lin et al, 2013) and protein structure (Planas-Iglesias et al, 2013; Zhao et al, 2017). These methods explain the principle of protein-protein interactions from different aspects. Based on the primary sequences of proteins, they use machine

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