An exploration on the topologies of SARS-CoV-2/human protein-protein interaction network.
A A NavishR UthayakumarPublished in: Journal of biomolecular structure & dynamics (2022)
Network biology is an important finding that uncovers the significant elements in viral infection control. Since viruses use the proteins on their surfaces to attach and enter into the host cell, the establishment of virus-host protein interactions is a potent regulator of the global organization of the viral life cycle after virus entry into host cells. In this instance, a topological study on the SARS - CoV - 2 / Human Protein-Protein Interaction Network ( P P I N ) evacuates much information about the protein-protein interactions. By making some interruptions to the interaction between proteins and hosts, we can quickly reduce the spread of the disease and get an insight into the target protein for drug development. This paper mainly focused on the graphical and structural complexity of the SARS - CoV - 2 / Human P P I N . For this purpose, the various primary (distance, radius, diameter, etc…) and advanced levels of graph measures (density, modularity, clustering coefficient, etc…) as well as a few fractal (box dimension, multifractal analysis) and entropy measures have been used. In addition, several graph descriptions and distribution graphs of P P I N offered to gain a thorough understanding of the SARS - CoV - 2 / Human P P I N . Conclusively, based on our work, we have discovered that P P I N is moderately complex and identified that hiring Nsp8 as a target node will positively affect the P P I N and has pointed out that mathematically found target proteins are matched with already suggested target proteins in the previous survey.Communicated by Ramaswamy H. Sarma.
Keyphrases
- sars cov
- protein protein
- endothelial cells
- small molecule
- respiratory syndrome coronavirus
- induced pluripotent stem cells
- pluripotent stem cells
- healthcare
- transcription factor
- escherichia coli
- machine learning
- lymph node
- single cell
- magnetic resonance
- cell proliferation
- life cycle
- computed tomography
- rna seq
- health information
- cross sectional
- cystic fibrosis
- biofilm formation
- cell cycle arrest
- convolutional neural network