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Cotranslational protein-RNA associations predict protein-protein interactions.

ABSTRACT: Most cellular proteins function as part of stable protein complexes. We recently showed that around 38% of proteins associate with mRNAs that encode interacting proteins, reflecting the cotranslational formation of the complex between the bait protein and the nascent peptides encoded by the interacting mRNAs. Here we hypothesise that these cotranslational protein-mRNA associations can be used to predict protein-protein interactions.We found that the fission yeast Exo2 protein, which encodes an exonuclease of the XRN1 family, coimmunoprecipitates with the eti1 mRNA, which codes for a protein of unknown function and uninformative sequence. Based on this protein-mRNA association, we predicted that the Exo2 and Eti1 protein are part of the same complex, and confirmed this hypothesis by coimmunoprecipitation and colocalization of the proteins. Similarly, we show that the cotranslational interaction between the Sty1 MAP kinase and the cip2 mRNA, which encodes an RNA-binding protein, predicts a complex between Sty1 and Cip2.Our results demonstrate that cotranslational protein-mRNA associations can be used to identify new components of protein complexes.

SUBMITTER: Duncan CD

PROVIDER: S-EPMC4234486 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Cotranslational protein-RNA associations predict protein-protein interactions.

Duncan Caia D S CD Mata Juan J

BMC genomics 20140422

<h4>Background</h4>Most cellular proteins function as part of stable protein complexes. We recently showed that around 38% of proteins associate with mRNAs that encode interacting proteins, reflecting the cotranslational formation of the complex between the bait protein and the nascent peptides encoded by the interacting mRNAs. Here we hypothesise that these cotranslational protein-mRNA associations can be used to predict protein-protein interactions.<h4>Results</h4>We found that the fission yea ...[more]

PMID: 24755092

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Project description:UnlabelledPotato virus A (PVA) is a single-stranded positive-sense RNA virus and a member of the family Potyviridae. The PVA coat protein (CP) has an intrinsic capacity to self-assemble into filamentous virus-like particles, but the mechanism responsible for the initiation of viral RNA encapsidation in vivo remains unclear. Apart from virion assembly, PVA CP is also involved in the inhibition of viral RNA translation. In this study, we show that CP inhibits PVA RNA translation in a dose-dependent manner, through a mechanism involving the CP-encoding region. Analysis of this region, however, failed to identify any RNA secondary structure(s) preferentially recognized by CP, suggesting that the inhibition depends on CP-CP rather than CP-RNA interactions. In agreement with this possibility, insertion of an in-frame stop codon upstream of the CP sequence led to a marked decrease in the inhibition of viral RNA translation. Based on these results, we propose a model in which the cotranslational interactions between excess CP accumulating in trans and CP translated from viral RNA in cis are required to initiate the translational repression. This model suggests a mechanism for how viral RNA can be sequestered from translation and specifically selected for encapsidation at the late stages of viral infection.ImportanceThe main functions of the CP during potyvirus infection are to protect viral RNA from degradation and to transport it locally, systemically, and from host to host. Although virion assembly is a key step in the potyviral infectious cycle, little is known about how it is initiated and how viral RNA is selected for encapsidation. The results presented here suggest that CP-CP rather than CP-RNA interactions are predominantly involved in the sequestration of viral RNA away from translation. We propose that the cotranslational nature of these interactions may represent a mechanism for the selection of viral RNA for encapsidation. A better understanding of the mechanism of virion assembly may lead to development of crops resistant to potyviruses at the level of viral RNA encapsidation, thereby reducing the detrimental effects of potyvirus infections on food production.

Project description:BackgroundKnowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.ResultsDomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.ConclusionWe envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.

Project description:BackgroundProtein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution.ResultsIn this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively.ConclusionsIn the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies.

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Cotranslational protein-RNA associations predict protein-protein interactions.

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Cotranslational protein-RNA associations predict protein-protein interactions.

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