Identifying Essential Proteins by Purifying Protein Interaction Networks
Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. In this paper, we propose a new method to identify essential proteins based on the purified PIN by using gene expression profiles and subcellular location information. The basic idea behind the proposed purifying method is that two proteins can physically interact with each other only if they appear together at the same subcellular location and are active together at least at a time point in the cell cycle. The original static PIN is marked as S-PIN and the final PIN purified by our method is marked as TS-PIN. To evaluate whether the constructed TS-PIN is more suitable to being used in the identification of essential proteins, six network-based essential protein discovery methods (DC, EC, SC, BC, CC, and IC) are applied on it to identify essential proteins. It is the same way with S-PIN and NF-APIN. NF-APIN is a dynamic PIN constructed by using gene expression data and S-PIN. The experimental results on the protein interaction network of S.cerevisiae shows that all the six network-based methods achieve better results when being applied on TS-PIN than that being applied on S-PIN and NF-APIN.
KeywordsPositive Predictive Value Negative Predictive Value Degree Centrality Betweenness Centrality Protein Interaction Network
- 11.Joy, M.P., Brock, A., Ingber, D.E., et al.: High-betweenness proteins in the yeast protein interaction network. BioMed. Res. Int. 2005(2), 96–103 (2005)Google Scholar
- 31.Li, M., Lu, Y., Niu, Z., et al.: United complex centrality for identification of essential proteins from PPI networks (2015). doi: 10.1109/TCBB.2015.2394487
- 35.Binder, J.X., Pletscher-Frankild, S., Tsafou, K., et al.: COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database, 2014: bau012 (2014)Google Scholar
- 40.Saccharom yces Genome Deletion Project. http://www.sequence.stanford.edu/group/yeast_deletion_project