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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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Related in: MedlinePlus

Clustering profiles of sets of moonlighting and non-moonlighting proteins. For each protein in a dataset, GO terms were clustered using various threshold values of SSRel and average number of GO term clusters were plotted. The datasets plotted were experimentally known moonlighting proteins (MPR1, 2, and 3) and identified moonlighting and non-moonlighting proteins in E. coli (Ecoli-MP and Ecoli-nonMP). E. coli moonlighting proteins were also plotted separately for each evidence category, 1 to 3 (Ecoli-PosMP-Cat1-3; see Methods) as well as multi-domain multi-function proteins. (A) BP GO terms were considered. (B) MF GO terms were considered.
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Fig4: Clustering profiles of sets of moonlighting and non-moonlighting proteins. For each protein in a dataset, GO terms were clustered using various threshold values of SSRel and average number of GO term clusters were plotted. The datasets plotted were experimentally known moonlighting proteins (MPR1, 2, and 3) and identified moonlighting and non-moonlighting proteins in E. coli (Ecoli-MP and Ecoli-nonMP). E. coli moonlighting proteins were also plotted separately for each evidence category, 1 to 3 (Ecoli-PosMP-Cat1-3; see Methods) as well as multi-domain multi-function proteins. (A) BP GO terms were considered. (B) MF GO terms were considered.

Mentions: Figure 4 shows clustering profiles of moonlighting proteins, where GO terms in BP and MF (Figure 4A and B) were clustered using single linkage clustering at different SSRel cutoff values. A clustering profile provides a more thorough picture of GO term similarities than clustering using a single cutoff value. It can show how the number of clusters grows at different cutoff values. Using the profiles for moonlighting proteins in MPR1 (black), MPR2 (red), and MPR3 (green) as a reference, the following three criteria were used to identify potential moonlighting proteins in E. coli: 1) proteins that have at least eight GO terms in the UniProt annotation; 2) proteins that have at least two clusters in the clustering profile at a SSRel cutoff of 0.1; 3) proteins that have at least four clusters in the clustering profile at a 0.5 SSRel. 140 proteins were found to satisfy all of these three criteria. We have also identified potential non-moonlighting proteins by applying essentially the opposite criteria to above: 1) proteins that have at least eight GO terms in the UniProt annotation; 2) proteins that have at most one cluster at a SSRel of 0.1; 3) proteins that have at most one cluster at 0.5 SSRel. There were 150 proteins that satisfied these criteria for non-moonlighting proteins.Figure 4


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)

Clustering profiles of sets of moonlighting and non-moonlighting proteins. For each protein in a dataset, GO terms were clustered using various threshold values of SSRel and average number of GO term clusters were plotted. The datasets plotted were experimentally known moonlighting proteins (MPR1, 2, and 3) and identified moonlighting and non-moonlighting proteins in E. coli (Ecoli-MP and Ecoli-nonMP). E. coli moonlighting proteins were also plotted separately for each evidence category, 1 to 3 (Ecoli-PosMP-Cat1-3; see Methods) as well as multi-domain multi-function proteins. (A) BP GO terms were considered. (B) MF GO terms were considered.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Clustering profiles of sets of moonlighting and non-moonlighting proteins. For each protein in a dataset, GO terms were clustered using various threshold values of SSRel and average number of GO term clusters were plotted. The datasets plotted were experimentally known moonlighting proteins (MPR1, 2, and 3) and identified moonlighting and non-moonlighting proteins in E. coli (Ecoli-MP and Ecoli-nonMP). E. coli moonlighting proteins were also plotted separately for each evidence category, 1 to 3 (Ecoli-PosMP-Cat1-3; see Methods) as well as multi-domain multi-function proteins. (A) BP GO terms were considered. (B) MF GO terms were considered.
Mentions: Figure 4 shows clustering profiles of moonlighting proteins, where GO terms in BP and MF (Figure 4A and B) were clustered using single linkage clustering at different SSRel cutoff values. A clustering profile provides a more thorough picture of GO term similarities than clustering using a single cutoff value. It can show how the number of clusters grows at different cutoff values. Using the profiles for moonlighting proteins in MPR1 (black), MPR2 (red), and MPR3 (green) as a reference, the following three criteria were used to identify potential moonlighting proteins in E. coli: 1) proteins that have at least eight GO terms in the UniProt annotation; 2) proteins that have at least two clusters in the clustering profile at a SSRel cutoff of 0.1; 3) proteins that have at least four clusters in the clustering profile at a 0.5 SSRel. 140 proteins were found to satisfy all of these three criteria. We have also identified potential non-moonlighting proteins by applying essentially the opposite criteria to above: 1) proteins that have at least eight GO terms in the UniProt annotation; 2) proteins that have at most one cluster at a SSRel of 0.1; 3) proteins that have at most one cluster at 0.5 SSRel. There were 150 proteins that satisfied these criteria for non-moonlighting proteins.Figure 4

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