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

By Andrea Procaccini, Bryan Lunt, H. Szurmant, Terence Hwa, Martin Weigt. 2011

89 citations recorded by the source corpus.

Predictive understanding of the myriads of signal transduction pathways in a cell is an outstanding challenge of systems biology. Such pathways are primarily mediated by specific but transient protein-protein interactions, which are difficult to study experimentally. In this study, we dissect the specificity of protein-protein interactions governing two-component signaling (TCS) systems ubiquitously used in bacteria. Exploiting the large number of sequenced bacterial genomes and an operon structure which packages many pairs of interacting TCS proteins together, we developed a computational approach to extract a molecular interaction code capturing the preferences of a small but critical number of directly interacting residue pairs. This code is found to reflect physical interaction mechanisms, with the strongest signal coming from charged amino acids. It is used to predict the specificity of TCS interaction: Our results compare favorably to most available experimental results, including the prediction of 7 (out of 8 known) interaction partners of orphan signaling proteins in Caulobacter crescentus. Surveying among the available bacterial genomes, our results suggest 15∼25% of the TCS proteins could participate in out-of-operon “crosstalks”. Additionally, we predict clusters of crosstalking candidates, expanding from the anecdotally known examples in model organisms. The tools and results presented here can be used to guide experimental studies towards a system-level understanding of two-component signaling.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics, Network Science & Complex Systems

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, neutral): “[37, 38,63] is obtained by setting Λ = λ/(λ+M).”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “2014) protein structure prediction; and shed light on specificity and crosstalk in bacterial signal transduction (Procaccini et al. 2011; Cheng et al. 2014).”
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014, methodology): “Results can be compared with those presented in [18], where the original message-passing DCA was applied to the same data-set, and 9 true contact prediction were reported before the first false positive appeared.”
Inferring interaction partners from protein sequences (2016, methodology): “[9, 15, 29] is obtained by setting Λ = λ/(λ+Meff).”
Inferring interaction partners from protein sequences using mutual information (2018, methodology): “Caulobacter crescentus CC 1062 DivK CC 2463 [14] 46 14 2 Worse**”
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis (2016, background): “(17), which collects 8,998 interacting (so-called cognate) protein pairs from 712 distinct species (Methods).”
Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information (2014, background): “(37) demonstrated that the direct couplings inferred from DCA can be used to quantify interaction specificity among HK and RR proteins.”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “Sequence coevolution analysis revealed interacting residue pairs, including those that determine interaction specificity, and allowed researchers to mutate residues to rewire the specificity of many TCS pairs [49,92,93].”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, methodology): “DCA has also been applied to quantify the determinants of interaction specificity between TCS proteins (Procaccini et al. 2011; Cheng et al. 2014), building on earlier coevolutionary approaches (Li et al. 2003; Burger and van Nimwegen 2008).”