Predicting Protein Functions from Protein Interaction Networks

Predicting Protein Functions from Protein Interaction Networks

Hon Nian Chua (Institute for Infocomm Research, Singapore) and Limsoon Wong (National University of Singapore, Singapore)
Copyright: © 2009 |Pages: 20
DOI: 10.4018/978-1-60566-398-2.ch012
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Abstract

Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of assigning functional annotations to proteins in a high-throughput manner. In this chapter, the authors provide an introduction to the task of automated protein function prediction. They discuss about the motivation for automated protein function prediction, the challenges faced in this task, as well as some approaches that are currently available. In particular, they take a closer look at methods that use protein-protein interaction for protein function prediction, elaborating on their underlying techniques and assumptions, as well as their strengths and limitations.
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Introduction

Since the completion of the Human Genome Project (HGP) in 2003, genomic and proteomic research has gained much momentum. Based on statistics from the Genome OnLine Database (GOLD) (Liolios et al. 2008), the number of genomes sequenced grew exponentially since 1995, with nearly 700 genomes completely sequenced by 2007 (See Figure 1). With the maturation of genomic data generation, the focus in biological research has shifted towards the understanding of the complex functional and interactive processes between proteins and multi-component molecular machines that contribute to the majority of operations in cells, as well as the transcriptional regulatory mechanisms and pathways that control these cellular processes (Frazier et al. 2003). There is also a pressing need for the functional characterization of genes in clinical research to better understand diseases (Hu et al. 2007).

Figure 1.

Number of completely sequenced genomes from year 2005 to 2007. Credit: Image adapted from http://www.genomesonline.org/gold_statistics.htm.

In contrast to the unprecedented rate at which new genes are being discovered, the pace at which novel genes and their corresponding protein products are characterized pales in comparison. A recent survey on function prediction techniques showed that out of 345 genomes listed in the KEGG Genome collection (Kanehisa et al. 2004), 222 have some ambiguous functional annotations assigned to half or more of its genes (putative, probable, and unknown) (Hawkins et al. 2007). This is may be attributed to the lack of reliable high-throughput method to identify the functional nature of proteins. Unlike genomic sequences, function is an abstract and complex notion, and can only be ascertained through the observation of multiple aspects of a protein, such as its sequence, structure, interaction behavior and changes in phenotype upon its mutation or removal.

Besides the influx of genomic sequence data, the maturation of high-throughput techniques for various other genomic analyses such as gene expression profiling (Eisen et al. 1998; Hughes et al. 2000), immuno-precipitation, genetic interactions, two-hybrid (Gietz et al. 1997), tandem-affinity purification, mass spectrometry, and more recently, flow cytometry and Protein-Fragment Complementation Assay (Tarassov et al. 2008), also makes available a wealth of other biological data. Advancements in computational techniques such as secondary and tertiary structure prediction also make it possible to generate computationally predicted data in large scale (Rost et al. 2003). This multitude of heterogeneous information presents to researchers a global perspective of the mechanisms behind genes and their protein products, and offers hope to elucidate the functions of proteins which cannot be easily characterized by sequence alone. However, this escalating rate of growth in biological data also makes manual annotation of protein function an increasingly daunting task. This paves the way to the emergence and popularization of automated function prediction. While it is unlikely that automated function prediction can produce authoritative annotations, it can provide systematic identification of potential novel annotations, which may be used to guide the prioritization of resource allocation for experimental verification. This can potentially improve the throughput of conventional functional characterization.

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