Designing modular genetic sensors with TtgR/AcrR family transcriptional regulators by using a statistical and data-driven approach.

Module swapping is an emerging strategy for creating transcriptional regulators with tailored combinations of signal detection and promoter recognition. By hybridizing DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from the same protein family, resulting regulators can be harnessed to establish new genetic connections. This approach has been applied to a range of regulator families; however, a portion of resulting hybrid regulators are poorly functional, which can be due to incompatibility between DBMs and LBMs, as critical module-module interactions are lost after hybridization. To address this issue, we developed an approach to design modular regulators and applied it to the TtgR/AcrR regulator family. Our approach involves identifying key residue pairs for DBM-LBM interactions by statistical analyses of coevolutionary traits among family members and experimental results from hybrid regulator characterization. These residue pairs were harnessed to develop a computational model for predicting compatibility between DBMs and LBMs. Using this predictive model, we designed mutations to reinstall critical interactions, which rescued protein activities. These hybrid regulators were harnessed to construct a genetic circuit for a three-input logic AND operation, demonstrating that our approach is effective for studying and designing new TtgR/AcrR modular regulators.