Identifying essential proteins from protein–protein interaction networks based on influence maximization

Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment...

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Bibliographic Details
Published in:BMC bioinformatics 2022-08, Vol.23 (Suppl 8), p.1-339, Article 339
Main Authors: Xu, Weixia, Dong, Yunfeng, Guan, Jihong, Zhou, Shuigeng
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Candidates
Cell survival
Drug development
Essential proteins
Experiments
Gene expression
Identification
Identification and classification
Influence discount
Influence maximization
Maximization
Medical treatment
Methods
Optimization
Protein-protein interactions
Proteins
Protein–protein interaction network
Social networks
Survival
Viral marketing
Yeast
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Summary:Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein-protein interaction (PPI) data, computationally identifying essential proteins from protein-protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed. In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism. We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-04874-w