Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information

By Ryan R. Cheng, Faruck Morcos, Herbert Levine, José N. Onuchic. 2014

113 citations recorded by the source corpus.

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “To each possible amino-acid sequence (a1, …, aL) consisting of L amino acids or gaps (L denotes the alignment width), a quantitative phenotype φ(a1, …, aL) is assigned.”
Inter-residue, inter-protein and inter-family coevolution: bridging the scales (2017, background): “Two back-to-back papers [26,38] have now suggested heuristic strategies to progressively match proteins, reaching between 80 and 90% of correctly matched HK and RR in the TCS test case, cf. Fig.”
Inferring interaction partners from protein sequences (2016, background): “[13] Cheng RR, Morcos F, Levine H, Onuchic JN (2014) Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information.”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, methodology): “Likewise, a study examining the mutational effects on TCS phosphotransfer (Cheng et al. 2014) constructed a mutational landscape from an information-based quantity.”
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis (2016, background): “contacts between bacterial signal transduction proteins, to help to assemble protein complexes (15, 16) and shed light on interaction specificity (17, 18).”
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].”
Efficient generative modeling of protein sequences using simple autoregressive models (2021, methodology): “Promising application are the data-driven design of mutant libraries for protein optimization [11, 12, 41], and the use of Potts models as sequence landscapes in quantitative models of protein evolution [9, 16].”