Disruption of Protein Complexes from Weighted Complex Networks

Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is pre...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2020-01, Vol.17 (1), p.102-109
Main Authors: Habibi, Mahnaz, Khosravi, Pegah
Format: Article
Language:English
Subjects:
Algorithms
betweenness
Biological processes
Biological properties
complex disruption
Complex networks
Computational Biology
Databases, Protein
Datasets
Disruption
Drugs
essential protein
Fungal Proteins - chemistry
Fungal Proteins - genetics
Fungal Proteins - metabolism
Humans
Lethality
Methods
Organisms
Protein engineering
Protein Interaction Mapping - methods
Protein Interaction Maps - genetics
Proteins
Proteins - chemistry
Proteins - genetics
Proteins - metabolism
Software
Weighed complex network
Yeasts
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Summary:Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is predicted as a high-degree vertex of protein interaction network. However, interaction data are usually incomplete and an essential protein cannot have high-connection due to data deficiency. Then, it is critical to design informative networks from other biological data sources. In this paper, we defined a minimal set of proteins to disrupt the maximum number of protein complexes. We constructed a weighted graph using a set of given complexes. We proposed a more appropriate method based on betweenness values to diagnose a minimal set of proteins whose removal would generate the disruption of protein complexes. The effectiveness of the proposed method was benchmarked using given dataset of complexes. The results of our method were compared to the results of other methods in terms of the number of disrupted complexes. Also, results indicated significant superiority of the minimal set of proteins in the massive disruption of complexes. Finally, we investigated the performance of our method for yeast and human datasets and analyzed biological properties of the selected proteins. Our algorithm and some example are freely available from http://bs.ipm.ac.ir/softwares/DPC/DPC.zip .
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2018.2859952