Dataset Information

Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions.

ABSTRACT: Genetic sensors with unique combinations of DNA recognition and allosteric response can be created by hybridizing DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from distinct transcriptional repressors. This module swapping approach is limited by incompatibility between DBMs and LBMs from different proteins, due to the loss of critical module-module interactions after hybridization. We determine a design strategy for restoring key interactions between DBMs and LBMs by using a computational model informed by coevolutionary traits in the LacI family. This model predicts the influence of proposed mutations on protein structure and function, quantifying the feasibility of each mutation for rescuing hybrid repressors. We accurately predict which hybrid repressors can be rescued by mutating residues to reinstall relevant module-module interactions. Experimental results confirm that dynamic ranges of gene expression induction were improved significantly in these mutants. This approach enhances the molecular and mechanistic understanding of LacI family proteins, and advances the ability to design modular genetic parts.

SUBMITTER: Jiang XL

PROVIDER: S-EPMC8458406 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of ?-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.

Dataset Information

Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure