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Genome-scale identification and characterization of moonlighting proteins.

Khan I, Chen Y, Dong T, Hong X, Takeuchi R, Mori H, Kihara D - Biol. Direct (2014)

Bottom Line: We found that the GO annotations of moonlighting proteins can be clustered into multiple groups reflecting their diverse functions.We found that moonlighting proteins physically interact with a higher number of distinct functional classes of proteins than non-moonlighting ones and also found that most of the physically interacting partners of moonlighting proteins share the latter's primary functions.Interestingly, we also found that moonlighting proteins tend to interact with other moonlighting proteins.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Moonlighting proteins perform two or more cellular functions, which are selected based on various contexts including the cell type they are expressed, their oligomerization status, and the binding of different ligands at different sites. To understand overall landscape of their functional diversity, it is important to establish methods that can identify moonlighting proteins in a systematic fashion. Here, we have developed a computational framework to find moonlighting proteins on a genome scale and identified multiple proteomic characteristics of these proteins.

Results: First, we analyzed Gene Ontology (GO) annotations of known moonlighting proteins. We found that the GO annotations of moonlighting proteins can be clustered into multiple groups reflecting their diverse functions. Then, by considering the observed GO term separations, we identified 33 novel moonlighting proteins in Escherichia coli and confirmed them by literature review. Next, we analyzed moonlighting proteins in terms of protein-protein interaction, gene expression, phylogenetic profile, and genetic interaction networks. We found that moonlighting proteins physically interact with a higher number of distinct functional classes of proteins than non-moonlighting ones and also found that most of the physically interacting partners of moonlighting proteins share the latter's primary functions. Interestingly, we also found that moonlighting proteins tend to interact with other moonlighting proteins. In terms of gene expression and phylogenetically related proteins, a weak trend was observed that moonlighting proteins interact with more functionally diverse proteins. Structural characteristics of moonlighting proteins, i.e. intrinsic disordered regions and ligand binding sites were also investigated.

Conclusion: Additional functions of moonlighting proteins are difficult to identify by experiments and these proteins also pose a significant challenge for computational function annotation. Our method enables identification of novel moonlighting proteins from current functional annotations in public databases. Moreover, we showed that potential moonlighting proteins without sufficient functional annotations can be identified by analyzing available omics-scale data. Our findings open up new possibilities for investigating the multi-functional nature of proteins at the systems level and for exploring the complex functional interplay of proteins in a cell.

Reviewers: This article was reviewed by Michael Galperin, Eugine Koonin, and Nick Grishin.

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Genetic interaction network analysis. The number of interacting proteins in the genetic interaction network of E. coli. (A) The number of interacting proteins selected with a Pearson correlation cutoff of 0.2. E. coli MP and non-MP, multi-domain multi-functional proteins, and the first category E. coli MPs are plotted. (B) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.2. (C) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.6.
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Fig9: Genetic interaction network analysis. The number of interacting proteins in the genetic interaction network of E. coli. (A) The number of interacting proteins selected with a Pearson correlation cutoff of 0.2. E. coli MP and non-MP, multi-domain multi-functional proteins, and the first category E. coli MPs are plotted. (B) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.2. (C) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.6.

Mentions: The last omics data we analyzed were genetic interactions. A genetically interacting gene pair was identified by examining the growth curves of a single gene knockout mutant and a double gene knockout mutant. In general, genes in the same pathway tend to show positive interaction and those in parallel pathways show negative or synthetic lethality [116]. Genetic interactions in E. coli were identified by Takeuchi et al. [117] using conjugation methods reported as GIANT-coli [118] and eSGA [119] with an improved quantitative measurement [120]. This dataset includes genetic interaction data for 215 genes against 3868 genes, which results in total of 813,560 gene combinations. Among them, 2009 pairs were identified as genetically interacting, which were defined as those have a correlation coefficient of over 0.2 in the maximum growth rate in time-series measurements [117]. The interacting gene pairs overlap with a small portion of the E. coli moonlighting and non-moonlighting proteins: 5 out of 33 moonlighting proteins, 3 out of 16 first category moonlighting proteins, and 5 out of 150 non-moonlighting proteins. Using these shared proteins, we performed the clustering profile analysis (FigureĀ 9).Figure 9


Genome-scale identification and characterization of moonlighting proteins.

Khan I, Chen Y, Dong T, Hong X, Takeuchi R, Mori H, Kihara D - Biol. Direct (2014)

Genetic interaction network analysis. The number of interacting proteins in the genetic interaction network of E. coli. (A) The number of interacting proteins selected with a Pearson correlation cutoff of 0.2. E. coli MP and non-MP, multi-domain multi-functional proteins, and the first category E. coli MPs are plotted. (B) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.2. (C) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.6.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4307903&req=5

Fig9: Genetic interaction network analysis. The number of interacting proteins in the genetic interaction network of E. coli. (A) The number of interacting proteins selected with a Pearson correlation cutoff of 0.2. E. coli MP and non-MP, multi-domain multi-functional proteins, and the first category E. coli MPs are plotted. (B) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.2. (C) The number of clusters of interacting proteins for individual E. coli moonlighting (blue) and non-moonlighting (red) proteins at BP-funsim threshold of 0.6.
Mentions: The last omics data we analyzed were genetic interactions. A genetically interacting gene pair was identified by examining the growth curves of a single gene knockout mutant and a double gene knockout mutant. In general, genes in the same pathway tend to show positive interaction and those in parallel pathways show negative or synthetic lethality [116]. Genetic interactions in E. coli were identified by Takeuchi et al. [117] using conjugation methods reported as GIANT-coli [118] and eSGA [119] with an improved quantitative measurement [120]. This dataset includes genetic interaction data for 215 genes against 3868 genes, which results in total of 813,560 gene combinations. Among them, 2009 pairs were identified as genetically interacting, which were defined as those have a correlation coefficient of over 0.2 in the maximum growth rate in time-series measurements [117]. The interacting gene pairs overlap with a small portion of the E. coli moonlighting and non-moonlighting proteins: 5 out of 33 moonlighting proteins, 3 out of 16 first category moonlighting proteins, and 5 out of 150 non-moonlighting proteins. Using these shared proteins, we performed the clustering profile analysis (FigureĀ 9).Figure 9

Bottom Line: We found that the GO annotations of moonlighting proteins can be clustered into multiple groups reflecting their diverse functions.We found that moonlighting proteins physically interact with a higher number of distinct functional classes of proteins than non-moonlighting ones and also found that most of the physically interacting partners of moonlighting proteins share the latter's primary functions.Interestingly, we also found that moonlighting proteins tend to interact with other moonlighting proteins.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Moonlighting proteins perform two or more cellular functions, which are selected based on various contexts including the cell type they are expressed, their oligomerization status, and the binding of different ligands at different sites. To understand overall landscape of their functional diversity, it is important to establish methods that can identify moonlighting proteins in a systematic fashion. Here, we have developed a computational framework to find moonlighting proteins on a genome scale and identified multiple proteomic characteristics of these proteins.

Results: First, we analyzed Gene Ontology (GO) annotations of known moonlighting proteins. We found that the GO annotations of moonlighting proteins can be clustered into multiple groups reflecting their diverse functions. Then, by considering the observed GO term separations, we identified 33 novel moonlighting proteins in Escherichia coli and confirmed them by literature review. Next, we analyzed moonlighting proteins in terms of protein-protein interaction, gene expression, phylogenetic profile, and genetic interaction networks. We found that moonlighting proteins physically interact with a higher number of distinct functional classes of proteins than non-moonlighting ones and also found that most of the physically interacting partners of moonlighting proteins share the latter's primary functions. Interestingly, we also found that moonlighting proteins tend to interact with other moonlighting proteins. In terms of gene expression and phylogenetically related proteins, a weak trend was observed that moonlighting proteins interact with more functionally diverse proteins. Structural characteristics of moonlighting proteins, i.e. intrinsic disordered regions and ligand binding sites were also investigated.

Conclusion: Additional functions of moonlighting proteins are difficult to identify by experiments and these proteins also pose a significant challenge for computational function annotation. Our method enables identification of novel moonlighting proteins from current functional annotations in public databases. Moreover, we showed that potential moonlighting proteins without sufficient functional annotations can be identified by analyzing available omics-scale data. Our findings open up new possibilities for investigating the multi-functional nature of proteins at the systems level and for exploring the complex functional interplay of proteins in a cell.

Reviewers: This article was reviewed by Michael Galperin, Eugine Koonin, and Nick Grishin.

Show MeSH
Related in: MedlinePlus