Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks

Procaccini, Andrea; Lunt, Bryan; Szurmant, Hendrik; Hwa, Terence; Weigt, Martin

doi:10.1371/journal.pone.0019729

Cited by 98 publications

(163 citation statements)

References 37 publications

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“…They consist of two interacting proteins, the Histidine sensor kinase (SK), as a signal receiver, and the response regulator (RR), which, under activation, typically acts as a transcription factor and triggers a transcriptional response (30). In particular, we use the dataset of Procaccini et al (17), which collects 8,998 interacting (so-called cognate) protein pairs from 712 distinct species (Methods). A random matching between SK and RR inside species would make, on average, one correct prediction per species; that is, only a fraction of 712/8,998 = 7.9% of all matched SK/RR pairs would be correct.…”

Section: Simultaneous Identification Of Interaction Partners and Intementioning

confidence: 99%

“…First, we check which fraction of the 8,998 matched pairs actually coincides with cognate pairs (as in the dataset published in ref. 17). Second, we use the matched MSA to predict interprotein residue−residue contacts.…”

Section: Simultaneous Identification Of Interaction Partners and Intementioning

confidence: 99%

“…contacts between bacterial signal transduction proteins, to help to assemble protein complexes (15,16) and shed light on interaction specificity (17,18). The applicability of DCA and related coevolutionary approaches (19,20) to protein−protein interactions far beyond the signaling system has been recently established (21,22).…”

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confidence: 99%

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Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Gueudré

Baldassi

Zamparo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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125

173

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Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.A lmost all biological processes depend on interacting proteins.Understanding protein−protein interactions is therefore key to our understanding of complex biological systems. In this context, at least two questions are of interest: First, the question "who with whom," i.e., which proteins interact; this concerns the networks connecting specific proteins inside one organism, but alsoin the context of this article-the evolutionary perspective of protein−protein interactions, which are conserved across different species. Their coevolution is at the basis of many modern computational techniques for characterizing protein−protein interactions. The second question is the question "how" proteins interact with each other, in particular, which residues are involved in the interaction interfaces, and which residues are in contact across the interfaces. Such knowledge may provide important mechanistic insight into questions related to interaction specificity or competitive interaction with partially shared interfaces.The experimental identification of protein−protein interactions is an arduous task (for reviews, cf. refs. 1 and 2): High-throughput techniques that aim to identify protein−protein interactions in vivo or in vitro are well documented and include large-scale yeast two-hybrid assays and protein affinity mass spectrometry assays. Such large-scale efforts have revealed useful information but are hampered by high false positive and false negative error rates. Structural approaches based on protein cocrystalli...

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Section: Simultaneous Identification Of Interaction Partners and Intementioning

confidence: 99%

Section: Simultaneous Identification Of Interaction Partners and Intementioning

confidence: 99%

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Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Gueudré

Baldassi

Zamparo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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125

173

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“…The top 10 residue pairs identified by DCA were all shown to be true contacts between the TCS proteins, and they were used to guide the accurate prediction (3-Å rmsd) of the interacting TCS protein complex (18,19). Furthermore, DCA was used to shed light on interaction specificity and interpathway cross-talk in bacterial signal transduction (20).…”

mentioning

confidence: 99%

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Morcos

Pagnani

Lunt

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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1,356

2,225

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The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.statistical sequence analysis | residue-residue covariation | contact map prediction | maximum-entropy modeling

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“…(1) Developing methods to predict interaction partners. Some algorithms have been developed [47,48], but making reliable and accurate predictions, particularly for orphan signaling proteins, remains a significant challenge. (2) Adapting the ability to rewire twocomponent pathways for synthetic biology efforts.…”

Section: Discussionmentioning

confidence: 99%

Determinants of specificity in two-component signal transduction

Podgornaia

Laub

2013

Current Opinion in Microbiology

128

133

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Maintaining the faithful flow of information through signal transduction pathways is critical to the survival and proliferation of organisms. This problem is particularly challenging as many signaling proteins are part of large, paralogous families that are highly similar at the sequence and structural levels, increasing the risk of unwanted cross-talk. To detect environmental signals and process information, bacteria rely heavily on two-component signaling systems comprised of sensor histidine kinases and their cognate response regulators. Although most species encode dozens of these signaling pathways, there is relatively little cross-talk, indicating that individual pathways are well insulated and highly specific. Here, we review the molecular mechanisms that enforce this specificity. Further, we highlight recent studies that have revealed how these mechanisms evolve to accommodate the introduction of new pathways by gene duplication.3

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Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks

Cited by 98 publications

References 37 publications

Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Determinants of specificity in two-component signal transduction

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