Computational verification of protein-protein interactions by orthologous co-expression

BACKGROUND: High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor. RESULTS: We introduce a novel computational method for verification of protein-protein interactions based on the co-expression of orthologs of interacting partners. The performance of our method is analysed using known S. cerevisiae interactions, and is shown to overcome limitations of previous methods. We present specific examples of known and putative interactions that are detected by our method and not by previous methods, and suggest that they represent transient interactions that might have been conserved and stabilized in other species. CONCLUSION: Co-expression of orthologous protein-pairs can be used to increase the confidence of hypothetical protein-protein interactions in S. cerevisiae as well as in other species. This approach may be especially useful for species with no available expression profiles and for transient interactions

Identification of candidate regulatory sequences in mammalian 3' UTRs by statistical analysis of oligonucleotide distributions

3' untranslated regions (3' UTRs) contain binding sites for many regulatory elements, and in particular for microRNAs (miRNAs). The importance of miRNA-mediated post-transcriptional regulation has become increasingly clear in the last few years. We propose two complementary approaches to the statistical analysis of oligonucleotide frequencies in mammalian 3' UTRs aimed at the identification of candidate binding sites for regulatory elements. The first method is based on the identification of sets of genes characterized by evolutionarily conserved overrepresentation of an oligonucleotide. The second method is based on the identification of oligonucleotides showing statistically significant strand asymmetry in their distribution in 3' UTRs. Both methods are able to identify many previously known binding sites located in 3'UTRs, and in particular seed regions of known miRNAs. Many new candidates are proposed for experimental verification.Comment: Added two reference

Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

<p>Abstract</p> <p>Background</p> <p>Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.</p> <p>Results</p> <p>In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in <it>Arabidopsis thaliana</it>. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, <it>C. elegans </it>and <it>D. melanogaster</it>. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.</p> <p>Conclusion</p> <p>We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.</p

Improved prediction of protein interaction from microarray data using asymmetric correlation

Background:Detection of correlated gene expression is a fundamental process in the characterization of gene functions using microarray data. Commonly used methods such as the Pearson correlation can detect only a fraction of interactions between genes or their products. However, the performance of correlation analysis can be significantly improved either by providing additional biological information or by combining correlation with other techniques that can extract various mathematical or statistical properties of gene expression from microarray data. In this article, I will test the performance of three correlation methods-the Pearson correlation, the rank (Spearman) correlation, and the Mutual Information approach-in detection of protein-protein interactions, and I will further examine the properties of these techniques when they are used together. I will also develop a new correlation measure which can be used with other measures to improve predictive power.&#xd;&#xa; &#xd;&#xa;Results:Using data from 5,896 microarray hybridizations, the three measures were obtained for 30,499 known protein-interacting pairs in the Human Protein Reference Database (HPRD). Pearson correlation showed the best sensitivity (0.305) but the three measures showed similar specificity (0.240 - 0.257). When the three measures were compared, it was found that better specificity could be obtained at a high Pearson coefficient combined with a low Spearman coefficient or Mutual Information. Using a toy model of two gene interactions, I found that such measure combinations were most likely to exist at stronger curvature. I therefore introduced a new measure, termed asymmetric correlation (AC), which directly quantifies the degree of curvature in the expression levels of two genes as a degree of asymmetry. I found that AC performed better than the other measures, particularly when high specificity was required. Moreover, a combination of AC with other measures significantly improved specificity and sensitivity, by up to 50%. &#xd;&#xa; &#xd;&#xa;Conclusions: A combination of correlation measures, particularly AC and Pearson correlation, can improve prediction of protein-protein interactions. Further studies are required to assess the biological significance of asymmetry in expression patterns of gene pairs. &#xd;&#xa

A Genomewide Functional Network for the Laboratory Mouse

Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms

Background: Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. Results: In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. Conclusion: This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application

Annotation and curation of hypothetical proteins: prioritizing targets for experimental study

Completely sequenced organisms have some uncharacterized proteins that are gene-encoded products. These proteins can be predicted through in-silico approaches and their biological activities are not proved by experimental evidence and known as hypothetical proteins (HPs). These proteins are important due to their excessive involvement in different cellular and signaling pathways. Structural and functional characterization of HPs reveal crucial roles in microorganisms, especially in pathogens related to human diseases. Here, we discussed all possibilities of in-silico analysis tools and other recently reported methods for hypothetical protein characterization and biomedical applications, including drug and vaccine development. Different methodologies, including meta-proteomics have been used to study protein expression by identification of HPs and comparative genomics have also come under observation due to the emergence of evolutionary study among different organisms. Structural characterization of proteins acts as a base for their functional prediction, novel drug target identification for disease treatment, vaccine production and sero-diagnosis. HPs have played major roles in different vital phenomenon for life including host adaptation, wound healing and chemotaxis. In the current era of drug and antibiotic resistance, HPs can be novel targets to treat related diseases. Identification and characterization of most HPs are under observation and will be the most promising genomic and bioinformatics techniques in structure-based drug designing and vaccine production in future