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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

Phylogenetic profile analysis. Average number of clusters of phylogenetically related proteins relative to the number of phylogenetically related proteins. Phylogenetically related proteins are taken from the STRING database. (A) The histogram of number of phylogenetically related proteins. (B) Functional clustering using Funsim (BP, MF, CC) score with thresholds between 0.1 and 1.0. (C) Functional clustering using Funsim (BP) score with thresholds from 0.1 to 1.0.
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Fig8: Phylogenetic profile analysis. Average number of clusters of phylogenetically related proteins relative to the number of phylogenetically related proteins. Phylogenetically related proteins are taken from the STRING database. (A) The histogram of number of phylogenetically related proteins. (B) Functional clustering using Funsim (BP, MF, CC) score with thresholds between 0.1 and 1.0. (C) Functional clustering using Funsim (BP) score with thresholds from 0.1 to 1.0.

Mentions: We further analyzed genes that have similar comparative genomic context to the moonlighting proteins [41]. Using the STRING database, for a protein of interest, we selected proteins as phylogenetically related if they were located in the neighbourhood of the target genes, were found to co-occur or co-absent, or were fused in multiple genomes. Concretely, genes that have a sufficient score (> 0.7 as recommended by STRING) at “neighborhood”, “co-occurrence”, or “gene-fusion” in the STRING database [83] were selected. It has been observed that phylogenetically co-related proteins are functionally related in many cases [41]. Figure 8 shows the clustering profiles of phylogenetically related proteins of the moonlighting and non-moonlighting proteins.Figure 8


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)

Phylogenetic profile analysis. Average number of clusters of phylogenetically related proteins relative to the number of phylogenetically related proteins. Phylogenetically related proteins are taken from the STRING database. (A) The histogram of number of phylogenetically related proteins. (B) Functional clustering using Funsim (BP, MF, CC) score with thresholds between 0.1 and 1.0. (C) Functional clustering using Funsim (BP) score with thresholds from 0.1 to 1.0.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig8: Phylogenetic profile analysis. Average number of clusters of phylogenetically related proteins relative to the number of phylogenetically related proteins. Phylogenetically related proteins are taken from the STRING database. (A) The histogram of number of phylogenetically related proteins. (B) Functional clustering using Funsim (BP, MF, CC) score with thresholds between 0.1 and 1.0. (C) Functional clustering using Funsim (BP) score with thresholds from 0.1 to 1.0.
Mentions: We further analyzed genes that have similar comparative genomic context to the moonlighting proteins [41]. Using the STRING database, for a protein of interest, we selected proteins as phylogenetically related if they were located in the neighbourhood of the target genes, were found to co-occur or co-absent, or were fused in multiple genomes. Concretely, genes that have a sufficient score (> 0.7 as recommended by STRING) at “neighborhood”, “co-occurrence”, or “gene-fusion” in the STRING database [83] were selected. It has been observed that phylogenetically co-related proteins are functionally related in many cases [41]. Figure 8 shows the clustering profiles of phylogenetically related proteins of the moonlighting and non-moonlighting proteins.Figure 8

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