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Showing 2 results for Protein-Protein Interaction Network
F. Zare Mirak-Abad, Z. Ghorbanali,
Volume 10, Issue 2 (7-2019)
Abstract
A biological network represents the interaction between a set of macromolecules to drive a particular biological process. In a biological environment, abnormalities happen not only in one molecule but also through a biological network. One of the most effective methods to detect anomaly is the comparison between healthy and diseased networks. In this regard, biological network alignment is one of the most efficient ways to find the difference between healthy and diseased cells. This problem, protein-protein interaction network alignment, has been raised in two main types: Local network alignment and Global network alignment. According to the NP-completeness of this problem, different non-deterministic approaches have been proposed to tackle the Global network alignment problem. Recently, NetAl has been introduced as a common algorithm to align two networks. Although this algorithm can align two networks at the appropriate time, it does not consider biological features. In this study, we present a new framework called PRAF to improve the results of network alignment algorithms such as NetAl by considering some biological features like gene ontology (GO).
Farshid Shirafkan, Sajjad Gharaghani,
Volume 13, Issue 2 (1-2023)
Abstract
Moonlight proteins are a subset of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Analyzing the interactive networks of proteins in the cell makes it possible to understand how complex processes cause disease. With the help of systems biology, larger and more complex systems can be studied, and the molecular basis of several diseases can be considered. The proteins of the human organism that are moonlight are mostly involved in cancer, anemia, and neurodegeneration. In this work, we created a subnet according to the human PPI network, in which the nodes, the proteins that cause the three selected diseases, and the edges, are the connection of these proteins with each other. We measured the power of the indirect effects of non-disease mediators between the three disease groups and identified key disease-binding intermediate proteins. The results show the relationship between mediator role and centrality and between mediator role and functional properties of these proteins. We have shown that a protein that plays a key indirect mediator between two diseases is not necessarily a hub in the PPI network. Therefore, as hub proteins are considered, intermediate proteins should be considered. We have observed that the mediators between anemia and neurodegeneration diseases are functionally important in the cell. The mediator proteins suggested herein should be experimentally tested as hypothetical disease-related proteins.
