Do current sequence analysis algorithms
disclose multifunctional (moonlighting) proteins?

Gómez, Antonio; Domedel, Nuria; Cedano, Juan; Piñol, Jaume; Querol, Enrique

doi:10.1093/bioinformatics/btg111

Cited by 42 publications

(37 citation statements)

References 5 publications

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“…The difficulty in prediction is reflected by work done with known moonlighting proteins as a starting point to test different algorithms. Although domains for alternative functions were found by bioinformatic analysis, the predictive power appeared to be very low (57). The limitations currently associated with automated protein function prediction have been reviewed by Friedberg (52).…”

Section: Final Remarksmentioning

confidence: 99%

Moonlighting Proteins in Yeasts

Gancedo

Flores

2008

Microbiol Mol Biol Rev

149

123

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SUMMARY Proteins able to participate in unrelated biological processes have been grouped under the generic name of moonlighting proteins. Work with different yeast species has uncovered a great number of moonlighting proteins and shown their importance for adequate functioning of the yeast cell. Moonlighting activities in yeasts include such diverse functions as control of gene expression, organelle assembly, and modification of the activity of metabolic pathways. In this review, we consider several well-studied moonlighting proteins in different yeast species, paying attention to the experimental approaches used to identify them and the evidence that supports their participation in the unexpected function. Usually, moonlighting activities have been uncovered unexpectedly, and up to now, no satisfactory way to predict moonlighting activities has been found. Among the well-characterized moonlighting proteins in yeasts, enzymes from the glycolytic pathway appear to be prominent. For some cases, it is shown that despite close phylogenetic relationships, moonlighting activities are not necessarily conserved among yeast species. Organisms may utilize moonlighting to add a new layer of regulation to conventional regulatory networks. The existence of this type of proteins in yeasts should be taken into account when designing mutant screens or in attempts to model or modify yeast metabolism.

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Section: Final Remarksmentioning

confidence: 99%

Moonlighting Proteins in Yeasts

Gancedo

Flores

2008

Microbiol Mol Biol Rev

149

123

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“…First, they rely heavily on the existence of functional annotation of a protein (Chapple et al, 2015;Pritykin et al, 2015), which is a major bottleneck of the problem. Second, all the existing methods address different aspects of MPs' functional diversity: sequence similarity (Gomez et al, 2003;Khan et al, 2012), motifs/ domains, structural disorder (Hern andez et al, 2011), or proteinprotein interaction (PPI) patterns combined with existing gene ontology (GO) annotations (Chapple et al, 2015;G omez et al, 2011;Pritykin et al, 2015). However, the diverse nature of MPs' functions, cellular locations, function switching mechanisms, and the organisms in which they are found gives compelling evidence that in order to understand and identify the overall functional aspects of these proteins, one should characterize these proteins in a wider functional/proteomic space.…”

Section: Introductionmentioning

confidence: 99%

Genome-scale prediction of moonlighting proteins using diverse protein association information

Khan¹

2016

Bioinformatics

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Motivation: Moonlighting proteins (MPs) show multiple cellular functions within a single polypeptide chain. To understand the overall landscape of their functional diversity, it is important to establish a computational method that can identify MPs on a genome scale. Previously, we have systematically characterized MPs using functional and omics-scale information. In this work, we develop a computational prediction model for automatic identification of MPs using a diverse range of protein association information. Results: We incorporated a diverse range of protein association information to extract characteristic features of MPs, which range from gene ontology (GO), protein-protein interactions, gene expression, phylogenetic profiles, genetic interactions and network-based graph properties to protein structural properties, i.e. intrinsically disordered regions in the protein chain. Then, we used machine learning classifiers using the broad feature space for predicting MPs. Because many known MPs lack some proteomic features, we developed an imputation technique to fill such missing features. Results on the control dataset show that MPs can be predicted with over 98% accuracy when GO terms are available. Furthermore, using only the omics-based features the method can still identify MPs with over 75% accuracy. Last, we applied the method on three genomes: Saccharomyces cerevisiae, Caenorhabditis elegans and Homo sapiens, and found that about 2-10% of proteins in the genomes are potential MPs. Availability and Implementation: Code available at http://kiharalab.org/MPprediction

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“…Conventional sequence-based functional annotation methods that are based on the concept of homology [21] or conserved motifs/domains [22][23][24] will have problems for identifying secondary functions because there are cases that a homolog of a moonlighting protein does not possess the secondary function [25] or has a different secondary function [16,26,27]. There are two studies that have investigated whether existing sequencebased function prediction methods can identify distinct dual functions of moonlighting proteins [28,29]. Gomez et al compared eleven methods and reported that PSI-BLAST [21] performed relatively well in identifying moonlighting functions [28].…”

Section: Introductionmentioning

confidence: 99%

“…There are two studies that have investigated whether existing sequencebased function prediction methods can identify distinct dual functions of moonlighting proteins [28,29]. Gomez et al compared eleven methods and reported that PSI-BLAST [21] performed relatively well in identifying moonlighting functions [28]. We have compared our function prediction tools, PFP [30,31] and ESG [32], with PSI-BLAST and showed that PFP, which mines function information from weakly similar sequences, had the best performance in predicting two distinct functions of moonlighting proteins [29].…”

Section: Introductionmentioning

confidence: 99%

Genome-scale identification and characterization of moonlighting proteins

et al. 2014

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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.

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Do current sequence analysis algorithms disclose multifunctional (moonlighting) proteins?

Abstract: A number of multifunctional, 'moonlighting', proteins have been analyzed by different current programs to test whether they identify both functions. PSI-BLAST and PRODOM perform best in predicting the alternative function.

Cited by 42 publications

References 5 publications

Moonlighting Proteins in Yeasts

Moonlighting Proteins in Yeasts

Genome-scale prediction of moonlighting proteins using diverse protein association information

Genome-scale identification and characterization of moonlighting proteins

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