Panchenko / Przytycka Protein-protein Interactions and Networks

Identification, Computer Analysis, and Prediction
1. Auflage 2010
ISBN: 978-1-84800-125-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 9, 212 Seiten

Reihe: Computational Biology

The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.

Autoren/Hrsg.

Panchenko, Anna

Przytycka, Teresa M.

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Contributors;10
4;1 Experimental Methods for Protein Interaction Identification and Characterization;13
4.1;1.1 Introduction;13
4.1.1;1.1.1 Complex Versus Binary Interactions;15
4.1.2;1.1.2 The Biological Relevance of Detected Protein-protein Interactions;16
4.1.3;1.1.3 Protein-protein Interactions are Incompletely Studied;16
4.2;1.2 Protein Complementation Techniques;16
4.2.1;1.2.1 The Yeast-Two-Hybrid System;17
4.2.2;1.2.2 Other Fragment Complementation Techniques;19
4.3;1.3 Affinity Purification Methods;21
4.3.1;1.3.1 GST-Pulldown;22
4.3.2;1.3.2 Co-Immunoprecipitation;22
4.4;1.4 Protein Complex Purification and Mass Spectrometry;25
4.4.1;1.4.1 Purification of Proteins Using Affinity Tags;25
4.4.2;1.4.2 Tandem Affinity Tagging;27
4.4.3;1.4.3 Genetics and Cloning of Affinity Tagged Proteins;28
4.4.4;1.4.4 Isolation of Protein Complexes;29
4.4.5;1.4.5 Proteomics by Mass Spectrometry;29
4.4.6;1.4.6 Identifying Interacting Proteins Using Mass Spectrometry;30
4.4.7;1.4.7 Quantitative Proteomics;31
4.5;1.5 Far-Western Blotting;33
4.6;1.6 Protein and Peptide Chips;34
4.7;1.7 Quality of Large-Scale Interaction Data;34
4.8;1.8 Comparison of Methods;36
4.8.1;1.8.1 Y2H vs. co-AP/MS;36
4.8.2;1.8.2 coAP/MS vs Protein Chips;38
4.9;1.9 Conclusions;39
5;2 Handling Diverse Protein Interaction Data: Integration, Storage and Retrieval;45
5.1;2.1 Introduction;45
5.2;2.2 Data Integration Methods;46
5.2.1;2.2.1 Statistical Meta-Analysis;46
5.2.2;2.2.2 Supervised Learning Methods;48
5.3;2.3 Protein and Domain Interaction Databases;50
5.3.1;2.3.1 Comprehensive Protein Interaction Databases;53
5.3.1.1;2.3.1.1 Database of Interacting Proteins (DIP);53
5.3.1.2;2.3.1.2 Biomolecular Object Network Databank (BOND);53
5.3.1.3;2.3.1.3 Search Tool for the Retrieval of Interacting Proteins (STRING);53
5.3.2;2.3.2 Specialized Interaction Databases;54
5.3.2.1;2.3.2.1 Human Protein Reference Database (HPRD);54
5.3.2.2;2.3.2.2 Munich MPact/MIPS Database;54
5.3.2.3;2.3.2.3 Binding Interface Databases (WikiBID and HotSprint);54
5.3.2.4;2.3.2.4 Molecule Pages Database/UCSD-Nature Signaling Gateway;54
5.3.2.5;2.3.2.5 InterDom Database;56
5.3.2.6;2.3.2.6 Domain Interaction Map (DIMA) Database;56
5.3.3;2.3.3 Interaction Databases Using Protein Structures;56
5.3.3.1;2.3.3.1 PIBASE Database;56
5.3.3.2;2.3.3.2 3did Database;57
5.3.3.3;2.3.3.3 Conserved Binding Mode (CBM) Database;57
5.3.3.4;2.3.3.4 iPfam Database;58
5.3.4;2.3.4 Interaction Network Analysis and Visualization;58
5.3.4.1;2.3.4.1 PathBLAST;58
5.3.5;2.3.5 Conclusion: A Case Study;59
6;3 Principles of Protein Recognition and Properties of Protein-protein Interfaces;64
6.1;3.1 Introduction;64
6.2;3.2 Protein Folding and Protein Binding are Similar Events;65
6.3;3.3 Types of Protein Interactions and Complexes in the Interactome;66
6.4;3.4 Classification into Three Types of Interfaces in the Interactome;68
6.5;3.5 Protein-protein Interfaces and Protein Cores are Similar;71
6.6;3.6 How are Signals Transmitted Through the Network?;72
6.7;3.7 Conclusions;74
7;4 Computational Methods to Predict Protein Interaction Partners;77
7.1;4.1 Introduction;77
7.2;4.2 Computational Methods vs. Experimental Techniques;78
7.2.1;4.2.1 Interplay Between Experimental and Computational Methods;78
7.2.2;4.2.2 Performance Comparison;79
7.3;4.3 Computational Methods Based on Sequence and Genomic Information;79
7.3.1;4.3.1 Phylogenetic Profiling;80
7.3.2;4.3.2 Similarity of Phylogenetic Trees;82
7.3.3;4.3.3 Conservation of Gene Neighboring;84
7.3.4;4.3.4 Gene Fusion;84
7.3.5;4.3.5 Other Methods;85
7.3.5.1;4.3.5.1 Co-evolving Positions;85
7.3.5.2;4.3.5.2 Training-Based Methods;85
7.3.5.3;4.3.5.3 Structure-Based Methods;86
7.4;4.4 Other Computational Methods Not Based on Sequence or Structural Information;86
7.5;4.5 Discussion and Future Trends;87
8;5 Protein Interaction Network Based Prediction of Domain-Domain and Domain-Peptide Interactions;92
8.1;5.1 Introduction;92
8.2;5.2 Predicting Domain Interactions from Protein Interaction Networks;93
8.2.1;5.2.1 Association Method;94
8.2.2;5.2.2 Maximum Likelihood Estimation (MLE);95
8.2.3;5.2.3 Domain Pair Exclusion Analysis (DPEA);96
8.2.4;5.2.4 Parsimonious Explanation (PE);97
8.2.5;5.2.5 Integrative Approaches;99
8.2.6;5.2.6 Evaluation of Domain-Domain Interaction Prediction Methods;100
8.3;5.3 Predicting Domain-Peptide Interactions from Protein Interaction Networks;101
8.3.1;5.3.1 Discovering Domain-Peptide Interactions from Protein Interaction Networks;102
8.3.2;5.3.2 Utilizing Protein Interaction Network in Discovering Phosphorylation Networks;103
8.4;5.4 Conclusions and Future Directions;104
9;6 Integrative Structure Determination of Protein Assemblies by Satisfaction of Spatial Restraints;108
9.1;6.1 Introduction;108
9.2;6.2 Sources of Spatial Information;111
9.3;6.3 Comprehensive Data Integration by Satisfaction of Spatial Restraints;111
9.4;6.4 Structural Characterization of the Nuclear Pore Complex;117
9.5;6.5 Conclusions;121
10;7 Topological and Dynamical Properties of Protein Interaction Networks;124
10.1;7.1 Introduction;124
10.2;7.2 Detecting Non-Random Topological Patterns in PPI Networks;126
10.2.1;7.2.1 Single-Node Topological Properties: Degree Distribution;126
10.2.2;7.2.2 Edge Swapping Algorithm: Constructing a Randomized Network;128
10.2.3;7.2.3 Detecting Non-Random Topological Patterns in a Network;129
10.2.4;7.2.4 An Example: Correlations Between Degrees of Neighboring Nodes;131
10.3;7.3 Equilibrium and Dynamical Properties of PPI Networks;134
10.3.1;7.3.1 The Assignment of Dissociation Constants Kij;135
10.3.2;7.3.2 Concentration-Coupled Proteins;136
10.3.3;7.3.3 Cascading Concentration Changes in PPI Networks;137
10.3.4;7.3.4 Conditions Favoring the Multi-Step Propagation of Perturbations;139
10.3.5;7.3.5 Robustness with Respect to Assignment of Dissociation Constants;142
10.3.6;7.3.6 Effects of Intracellular Noise;143
11;8 From Protein Interaction Networks to Protein Function;147
11.1;8.1 Introduction;147
11.2;8.2 Preliminaries;148
11.2.1;8.2.1 Protein Function;148
11.2.2;8.2.2 Notation;149
11.3;8.3 Assessing Interaction Reliability;149
11.4;8.4 Algorithms;150
11.4.1;8.4.1 Local Approaches;150
11.4.2;8.4.2 Graph Cuts;152
11.4.3;8.4.3 Markov Random Field;154
11.4.4;8.4.4 Network Flow-Based Methods;155
11.4.5;8.4.5 Discriminative Learning Methods;157
11.4.6;8.4.6 Clustering;158
11.4.6.1;8.4.6.1 Distance-Based Clustering;159
11.4.6.2;8.4.6.2 Network-Based Hierarchical Clustering;160
11.4.6.3;8.4.6.3 Local Clustering;161
11.4.6.4;8.4.6.4 Other Clustering Approaches;162
11.5;8.5 Evaluation of Methods;163
11.5.1;8.5.1 Testing Frameworks;163
11.5.2;8.5.2 Performance of Methods;165
11.6;8.6 Conclusions;166
12;9 Cross-Species Analysis of Protein-protein Interaction Networks;171
12.1;9.1 Introduction;171
12.2;9.2 Preliminaries;172
12.3;9.3 Methods for Pairwise Network Alignment;172
12.3.1;9.3.1 Alignment-Graph Based Methods;172
12.3.1.1;9.3.1.1 The Network Alignment Graph;173
12.3.1.2;9.3.1.2 Search Heuristic;174
12.3.1.3;9.3.1.3 NetworkBLAST;174
12.3.1.4;9.3.1.4 NetworkBLAST-E;176
12.3.1.5;9.3.1.5 MaWish;178
12.3.2;9.3.2 Match-and-Split;179
12.3.3;9.3.3 Global Network Alignment;180
12.3.3.1;9.3.3.1 Orthology Detection Using Markov Random Fields;180
12.3.3.2;9.3.3.2 ISORank;181
12.4;9.4 Multiple Network Alignment;181
12.4.1;9.4.1 Græmlin;182
12.5;9.5 Network Querying;183
12.5.1;9.5.1 MetaPathwayHunter;184
12.5.2;9.5.2 QPath and QNet;185
12.5.3;9.5.3 PathMatch;186
12.6;9.6 Evaluation Measures;187
12.6.1;9.6.1 Significance Evaluation;187
12.6.2;9.6.2 Quality Assessment;188
12.7;9.7 A Case Study;189
12.8;9.8 Discussion;190
13;Index;194

Leseproben

"Chapter 8 From Protein Interaction Networks to Protein Function (p. 139-140)

Mona Singh

Abstract The recent availability of large-scale protein-protein interaction data provides new opportunities for characterizing a protein’s function within the context of its cellular interactions, pathways and networks. In this paper, we review computational approaches that have been developed for analyzing protein interaction networks in order to predict protein function.

8.1 Introduction

A major challenge in the post-genomic era is to determine protein function at the proteomic scale. Most organisms contain a large number of proteins whose functions are currently unknown. For example, about one-third of the proteins in the baker’s yeast Saccharomyces cerevisiae—arguably one of the most wellcharacterized model organisms—remain uncharacterized. Traditionally, computational methods to assign protein function have relied largely on sequence homology. However, the recent emergence of high-throughput techniques for determining protein interactions has enabled a new line of research where protein function is predicted by utilizing interaction data.

Proteome-scale physical interaction networks, or interactomes, have been determined for several organisms, including yeast and human. These networks are comprised of direct physical interactions between proteins (typically obtained via two hybrid analysis [FS89]) as well as of interactions indicating that two proteins are part of the same multi-protein complex (review, [BK03]).

High-throughput experiments have also linked together proteins in several other ways, and it is possible to build large-scale networks consisting of links between proteins that are synthetic lethals or are coexpressed, or between proteins where one regulates or phosphorylates the other (review, [ZGS07]). In addition to interaction networks that have been determined experimentally, there are a number of computational methods for building functional interaction networks, where two proteins are linked if they are predicted to perform a shared biological task (review, [GK00])).

In this chapter, we review some of the basic computational methods developed for analyzing protein interaction networks in order to predict protein function. The majority of these methods use some version of guilt-by-association, where proteins are annotated by transferring the functions of the proteins with which they interact.

The methods differ in the extent to which they use global properties of the interactome in annotating proteins, what topological features of the interactome they exploit, and whether they rely on first identifying tight clusters of proteins within the interactome before transferring annotations. Additionally, the underlying formulations are quite diverse, typically exploiting and further developing well understood concepts from graph theory, graphical models, discriminative learning and/or clustering."