Computation-guided redesign of promoter specificity of a bacterial RNA polymerase

Abstract

Synthetic genetic circuits enable precise and dynamic control of gene expression, making them useful tools in metabolic engineering and synthetic biology for regulating cellular responses under transient states, such as metabolic flux and stress. However, the complexity and utility of these systems are restricted by the limited set of available regulatory parts. To engineer circuits with desirable levels of programmable control, these components must be orthogonal from the host expression systems. Bacterial sigma factors and RNA polymerases are ideal targets for synthetic regulation, as they represent the first level of transcriptional control and modulate gene expression in endogenous systems. While attempts have been made to engineer orthogonality through heterologous sigmas, a lack of diversity in consensus promoter sequences and toxicity often limit the utility of these sigmas. In this work, we leverage computational design and high-throughput screening to engineer σ70 bacterial transcription factors capable of targeting any promoter sequence to maximize orthogonality. To demonstrate the power of this approach, we engineered several σ70 variants, each capable of targeting one of five different orthogonal promoter sequences, leading to the expression of a downstream reporter gene. By engineering these sigma variants, we present a generalized workflow capable to produce orthogonal sigma factors that may serve as a vital tool in the design of customizable synthetic genetic circuits.

Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes

“… 2022 ). In addition, a recent study by Liu et al ( 2022 ) has further defined the role and impact of certain positions of the −35 box on closed complex formation through re-engineering the interaction between the −35 box and the sigma factor, giving a further example of how this knowledge can be used in a promoter engineering context to yield promoter sequences that function orthogonally to the host metabolism.…”

Computational design of sequence-specific DNA-binding proteins

“…Despite intensive study, the DNA binding affinity and specificity of natural proteins remain difficult to predict (4), and the high free energetic cost of desolvating the highly polar DNA surface presents a challenge to the de novo design of new DBPs. For these reasons, while computational de novo design has had considerable recent success in generating binders to arbitrary protein structures (5), mostly at hydrophobic patches, computational approaches for DBP engineering have thus far been limited to redesigning interfaces of existing native protein-DNA complex structures (6)(7)(8)(9)(10). These efforts have been constrained by the rigid geometry of the starting scaffold shape and orientation relative to DNA (11), which restrict the possible target sequences that can be recognized (12).…”