This chapter introduces state-of-the-art computational methods which discover lethal proteins from Protein Interaction Networks (PINs). Lethal proteins are an interesting subject in understanding the minimal condition for cellular development and survival. A dysfunctional research subject or absence of a lethal protein would result in fatality of the cell. Biological experiments have been conducted to systematically detect such proteins. However, such processes are time consuming and requires huge amount of effort to conduct. The researchers have developed a series of computational methods which take advantage of the network properties of individual proteins to detect lethal proteins in PINs. In this chapter, each computational method is studied in depth with an analysis on its pros and cons. Finally, a discussion on the possible further research directions will conclude the chapter.
TopIntroduction
Proteins play a key role in the operation and survival of a cell. They rarely function alone, but perform biological tasks through interactions with other biological entities such as DNA, RNA and other proteins. Interactions between proteins within an organism collectively form a Protein Interaction Network (PIN). The most common representation of a PIN is a graph where proteins are depicted by nodes and an edge between two nodes indicates a detected interaction between the two proteins. A lethal (or essential) protein is one that renders the cell unviable upon its removal. Lethal proteins play an intricate role in the development and survival of the cell and their characteristics and detection is an interesting research topic in proteomics.
A protein’s lethality has been used in various biological and medical researches in recent years. In the study of protein evolution and conservation, it was predicted that proteins differing in their importance (lethality) are subjected to different evolution rate (Kimura et al. 1974; Wilson et al. 1977). Their prediction has been put to test with the availability of large gene-knockout data by various research groups (Hurst et al. 1999; Liao et al. 2006; Pal et al. 2003; Yang et al. 2003; Zhang et al. 2005). Due to its biological significance, even with a substantial number of genes with unknown lethality profiles (Jeong et al. 2003), lethal proteins are used as the focus of their work. The results of their investigations do agree that lethal proteins evolve at a different rate from normal proteins. However, there is a dispute over whether does lethal proteins evolve faster (to better adapt to changing environment) or slower (to avoid drastic changes).
Cross species studies have also utilized information pertaining to protein lethality. A study on the PIN of Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster indicates an association between protein evolution, centrality, and protein lethality (Hahn et al. 2005). Conservation of lethal proteins has also been detected between Saccharomyces cerevisiae and Saccharomyces mikatae (Seringhaus et al. 2006). Such work demonstrates the usefulness of lethal proteins in aiding biological research while investigating new species.
Associations between lethal proteins, disease, and human gene morbidity have also been established by various research groups. Steinmetz et al. (2002) used the Saccharomyces cerevisiae deletion mutants to identify 256 new human mitochondrial proteins with a fivefold greater selection than gene expression analysis. Kondrashov et al. (2004) first stated the close relationship between morbidity and protein lethality and further found that morbid genes are more similar to lethal proteins of Drosophila melanogaster. Furney et al. (2006) made a finer division of disease genes into dominant or recessive mutations and lethal proteins were found to have a higher correlation with dominant genes.