We construct a statistical model of bacterial two-component signaling (TCS) proteins for predicting amino acid configurations that allow signaling partners to preferentially interact. This model is applied to a recent exhaustive mutational experiment of 4 interfacial residues on the histidine kinase of the magnesium response TCS system in E. coli. We demonstrate that our top mutational predictions can accurately capture experimentally observed mutational variants that preserve interaction specificity between TCS partners. Interestingly, we can isolate the true positive predictions by focusing on mutations that we predict to limit signal transfer with non-partner TCS proteins (i.e., “cross-talk”). This demonstrates that our model also captures the amino acid configurations that lead to “cross-talk” between non-partner TCS proteins, which can be used to engineer specificity. We further supplement our analysis by calculating the mutational change in the binding affinity between TCS partners, supporting the intuitive concept that overly destabilizing mutations disrupt TCS.