The researchers at the Stowers Institute for Medical Research revealed new insight into the organization of complicated and dynamic protein networks. To serve the purpose they have applied a mathematical method to large proteomics data sets. Their respective research was published in the Nature Publishing Group’s Scientific Reports. The study focuses on the biological networks of two proteins,
- INO80 in the yeast cerevisiae
- Sin3 in a human cell line
The advanced mathematical approach used for the study is known as topological data analysis (TDA). It is a general method for analyzing highly multi-dimensional data sets. The researchers also compared TDA with other methods used for analyzing protein interaction data. They found that TDA allows assessing a larger number of proteins and their connections. Moreover, it provides with an expanded view of the network.
Proteins are the basic structural and functional components of cells and tissues. They work with partners in networks and carry out many biological functions. The proteins interact with different partners at different times and in different cellular environments. Thus, they give rise to dynamic networks that are a challenge for the scientists who study them.
According to the researchers, a change in one part of the network impacts not just that component but surrounding ones as well.
What did the researchers do?
For the INO80 network study, the genes encoding components of the INO80 chromatin remodeling complex were deleted from the genome. Afterward, the researchers isolated the protein complexes. On the other hand, in the Sin3 network study, they reanalyzed prior data which demonstrated the disruption of the human Sin3 network with a histone deacetylase inhibitor. Note this inhibitor is associated with the anticancer activity.
Both of the studies reported the identification of the topological network modules (TNMs) made up of proteins with shared properties, found in particular locations in networks. The findings of the research give an insight into the networks as they identify modules comprising of proteins from particular categories. These may include,
- Proteins within a complex
- Proteins with shared biological functions
- Proteins disrupted across networks
Identification of TNMs can be beneficial for the study of diseases like cancer, where protein interaction networks are altered due to chemotherapies or the disease itself. According to the research experts, understanding the proteins, their neighborhoods, and their travels, provide an insight into a wide range of biological functions, including drug resistance and the effect of cancer mutations.
TDA is a speedy and proficient way of interpreting complicated data sets. There isn’t much data out there on the disrupted or agitated protein interaction networks. Most of the research studies focus on static networks. In fact, a perturbed system is a source of learning how it works as a dynamic network.
An advanced mathematical approach called topological data analysis (TDA) allows a better understanding of proteins in their dynamic world. Moreover, the research reveals that how does disrupting the parts of the protein networks affects their interactions and networking. Additionally, the research provided an expanded view of cascading interactions across the larger network and identified new areas of biological networks to explore.