Discovering Lethal Proteins in Protein Interaction Networks

Discovering Lethal Proteins in Protein Interaction Networks

Kar Leong Tew (Institute for Infocomm Research, Singapore) and Xiao-Li Li (Institute for Infocomm Research, A* STAR, Singapore)
Copyright: © 2009 |Pages: 20
DOI: 10.4018/978-1-60566-398-2.ch011
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Abstract

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.
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Introduction

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.

Complete Chapter List

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Dedication
Editorial Advisory Board
Table of Contents
Acknowledgment
Xiao-Li Li, See-Kiong Ng
Chapter 1
Christian Schönbach
Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose... Sample PDF
Molecular Biology of Protein-Protein Interactions for Computer Scientists
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Chapter 2
Koji Tsuda
In this tutorial chapter, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and... Sample PDF
Data Mining for Biologists
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Chapter 3
Tatsuya Akutsu, Morihiro Hayashida
Many methods have been proposed for inference of protein-protein interactions from protein sequence data. This chapter focuses on methods based on... Sample PDF
Domain-Based Prediction and Analysis of Protein-Protein Interactions
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Chapter 4
Martin S.R. Paradesi, Doina Caragea, William H. Hsu
This chapter presents applications of machine learning to predicting protein-protein interactions (PPI) in Saccharomyces cerevisiae. Several... Sample PDF
Incorporating Graph Features for Predicting Protein-Protein Interactions
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Chapter 5
David La, Daisuke Kihara
This chapter gives a comprehensive introduction of the sequence/structural features that are characteristic of protein- protein interaction sites... Sample PDF
Discovering Protein-Protein Interaction Sites from Sequence and Structure
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Chapter 6
Paolo Marcatili, Anna Tramontano
This chapter provides an overview of the current computational methods for PPI network cleansing. The authors first present the issue of identifying... Sample PDF
Network Cleansing: Reliable Interaction Networks
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Chapter 7
Hugo Willy
Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction... Sample PDF
Discovering Interaction Motifs from Protein Interaction Networks
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Chapter 8
Raymond Wan, Hiroshi Mamitsuka
This chapter examines some of the available techniques for analyzing a protein interaction network (PIN) when depicted as an undirected graph.... Sample PDF
Discovering Network Motifs in Protein Interaction Networks
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Chapter 9
Clara Pizzuti, Simona Ester Rombo
In this chapter a survey on the main graph-based clustering techniques proposed in the literature to mine proteinprotein interaction networks (PINs)... Sample PDF
Discovering Protein Complexes in Protein Interaction Networks
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Chapter 10
Takashi Makino, Aoife McLysaght
This chapter introduces evolutionary analyses of protein interaction networks and of proteins as components of the networks. The authors show... Sample PDF
Evolutionary Analyses of Protein Interaction Networks
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Chapter 11
Kar Leong Tew, Xiao-Li Li
This chapter introduces state-of-the-art computational methods which discover lethal proteins from Protein Interaction Networks (PINs). Lethal... Sample PDF
Discovering Lethal Proteins in Protein Interaction Networks
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Chapter 12
Hon Nian Chua, Limsoon Wong
Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable... Sample PDF
Predicting Protein Functions from Protein Interaction Networks
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Chapter 13
Pablo Minguez, Joaquin Dopazo
Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic... Sample PDF
Protein Interactions for Functional Genomics
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Chapter 14
Xiaoyue Zhao, Lilia M. Iakoucheva, Michael Q. Zhang
Genetic factors play a major role in the etiology of many human diseases. Genome-wide experimental methods produce an increasing number of genes... Sample PDF
Prioritizing Disease Genes and Understanding Disease Pathways
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Chapter 15
Smita Mohanty, Shashi Bhushan Pandit, Narayanaswamy Srinivasan
Integration of organism-wide protein interactome data with information on expression of genes, cellular localization of proteins and their functions... Sample PDF
Dynamics of Protein-Protein Interaction Network in Plasmodium Falciparum
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Chapter 16
Sirisha Gollapudi, Alex Marshall, Daniel Zadik, Charlie Hodgman
Software for the visualization and analysis of protein-protein interaction (PPI) networks can enable general exploration, as well as providing... Sample PDF
Graphical Analysis and Visualization Tools for Protein Interaction Networks
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Chapter 17
Valeria Fionda, Luigi Palopoli
The aim of this chapter is that of analyzing and comparing network querying techniques as applied to protein interaction networks. In the last few... Sample PDF
Network Querying Techniques for PPI Network Comparison
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Chapter 18
Tero Aittokallio
This chapter provides an overview of the computational approaches developed for exploring the modular organization of protein interaction networks.... Sample PDF
Module Finding Approaches for Protein Interaction Networks
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About the Contributors