Prediction of Protein‐Protein Interaction Networks
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- Abstract
- Table of Contents
- Figures
- Literature Cited
Abstract
This unit offers a general overview of several techniques that have been developed for inferring functional and/or protein?protein interaction networks. The majority of these use whole?genome sequences as their primary input source of data. In addition, a few methods that utilize both protein features and experimental protein?protein interaction data directly in the prediction of new interactions have recently been developed. While an exhaustive list of approaches is not presented, it is hoped that the reader will gain a sense of how these approaches are implemented and an idea of their relative strengths and weaknesses, and a broader perspective on the type of work being conducted in this highly active area of research. Curr. Protoc. Bioinform. 22:8.2.1?8.2.14. © 2008 by John Wiley & Sons, Inc.
Keywords: protein interactions; bioinformatics; interaction networks
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- Introduction
- Approaches
- Observations and Conclusions
- Literature Cited
- Figures
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Figure 8.2.1 Diagram of conserved gene cluster approach used by Overbeek et al. (). View Image -
Figure 8.2.2 Commonly used descriptors of prediction accuracy. In this example, a true positive (TP) is one which the interaction is both known to exist and predicted to exist. A false positive (FP) is one in which the interaction is known not to exist, but predicted as existing. True (TN) and false negatives (FN) are the negatives of these conditions, respectively. Based on this table, the success rate or total accuracy is equal to (TP + TN)/(TP + FP + TN + FN); the sensitivity, TP rate, or recall is equal to TP/(TP + FN); the specificity or precision is equal to TP/(TP + FP); and the FP rate is equal to FP/(FP + TN). View Image -
Figure 8.2.3 One method of gene fusion. Individual proteins, A and B, from one genome can often be found as a single fused protein, C, in another genome. The finding of such a fused protein suggests that protein A and B interact either physically or functionally. View Image -
Figure 8.2.4 The phylogenetic profile method. Genomes (G1 to G6) are searched for the absence (0) or presence (1) of proteins (P1 to P6). Genes with identical profiles, or perhaps differing at a single position, can be linked into functionally related groups. View Image -
Figure 8.2.5 Coevolution and correlation of phylogenetic distances. (A ) Trees or sequence alignments of two possibly interacting protein families are first generated along with the 16S ribosomal RNA sequence alignments for the same taxa. (B ) Distance matrices are generated from the alignments (with tree‐of‐life distances subtracted from the distance matrices in the case of the tol‐mirrortree approach) and the correlation (C ) between matrices determined, typically, using the Pearson correlation coefficient. View Image -
Figure 8.2.6 Extraction of domain data for the prediction of protein interactions. Given a set of protein interactions, all individual domain‐domain interactions are extracted and counted. After training, counts are converted into probabilities of domain‐domain interaction as well as protein‐protein interaction. In the second stage, network topology is incorporated to improve predictions. See text for details. View Image -
Figure 8.2.7 A sample connectivity distribution for a yeast protein network extracted from the DIP database (see Internet Resources). The majority of proteins will have few interactions (left end of the x axis); however, a few will be highly connected (right end). View Image
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Literature Cited
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| Internet Resources | |
| http://dip.doe‐mbi.ucla.edu | |
| The Database of Interacting Proteins (DIP). A database of both manually and automatically curated experimental protein‐protein interactions. | |
| http://string.embl.de | |
| STRING is a database of known and predicted protein‐protein interactions. The interactions include direct (physical) and indirect (functional) associations taken from high‐throughput experiments, genomic context, coexpression, and literature. | |
| http://www.bind.ca | |
| The Biomolecular Interaction Network Database (BIND). Database of interactions, molecular complexes, and pathways. Includes interactions other than protein‐protein (e.g., protein‐DNA). | |
| http://cbm.bio.uniroma2.it/mint | |
| The Molecular Interactions Database (MINT). A manually curated database designed to store functional interactions between biological molecules (i.e., proteins RNA and DNA). | |
| http://portal.curagen.com/extpc/com.curagen.portal.servlet.Yeast | |
| PathCalling Yeast Interaction Database. Database of results from Uetz et al. (). | |
| http://wit.mcs.anl.gov/WIT2 | |
| The WIT homepage. A Web site of reconstructed metabolic pathways for a number of genomes. | |
| http://mips.gsf.de | |
| The Munich Information Center for Protein Sequences (MIPS) homepage. Maintains curated database designed to store functional interactions between biological molecules (e.g., proteins, RNA, DNA). | |
| http://www.genome.ad.jp/kegg | |
| KEGG: Kyoto Encyclopedia of Genes and Genomes. In addition to other material, this site provides a database of molecular interactions as well as metabolic and signal transduction pathways. | |
| http://www.ecocyc.org | |
| The Encyclopedia of Escherichia coli Genes and Metabolism (EcoCyc) Web site. | |
| http://pim.hybrigenics.com | |
| Web site for Hybrigenics’ Protein Interaction Map (PIM) functional proteomics software platform. |
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