Exploiting spatial dimensions to enable parallelized continuous directed evolution

Empowering continuous evolution of proteins by in vivo mutagenesis

In vivo mutagenesis enriches genetic polymorphism within cells, which is pivotal for triggering continuous evolution. Remarkable strides have been made in this field. Here, we summarize the current in vivo mutagenesis methods focusing on the theme of mutation range and provide an outlook on their future directions, offering inspiration to relevant researchers.

Engineered Phage Enables Efficient Control of Gene Expression upon Infection of the Host Cell

Recently, we developed a spatial phage-assisted continuous evolution (SPACE) system. This system utilizes chemotaxis coupled with the growth of motile bacteria during their spatial range expansion in soft agar to provide fresh host cells for iterative phage infection and selection pressure for preserving evolved genes of interest carried by phage mutants. Controllable mutagenesis activated only in a subpopulation of the migrating cells is essential in this system to efficiently generate mutated progeny phages from which desired individuals are selected during the directed evolution process. But, the widely adopted small molecule-dependent inducible system could hardly fulfill this purpose because it always affects all cells homogeneously. In this study, we developed a phage infection-induced gene expression system using modified Escherichia coli (E. coli) phage shock protein operon or sigma factors from Bacillus subtilis. Results showed that this system enabled efficient control of gene expression upon phage infection with dynamic output ranges from small to large using combinations of different engineered phages and corresponding promoters. This system was incorporated into SPACE to function as a phage infection-induced mutagenesis module and successfully facilitated the evolution of T7 RNA polymerase, which generated diverse mutants with altered promoter recognition specificity. We expect that phage infection-induced gene expression system could be further extended to more applications involving partial induction in a portion of a population and targeted induction in specific strains among a mixed bacterial community, which provides an important complement to small molecule-dependent inducible systems.

Cyanophage Engineering for Algal Blooms Control

Cyanobacteria represent a prevalent category of photosynthetic autotrophs capable of generating deleterious algal blooms, commonly known as cyanobacteria harmful algal blooms (cyanoHABs). These blooms often produce cyanotoxins, which pose risks to public health and ecosystems by contaminating surface waters and drinking water sources. Traditional treatment methods have limited effectiveness. Therefore, there is an urgent need for a new approach to effectively manage cyanoHABs. One promising approach is the use of cyanophages, which are viruses that specifically target cyanobacteria. Cyanophages serve as an effective biological control method for reducing cyanoHABs in aquatic systems. By engineering cyanophages, it is possible to develop a highly specific control strategy that minimally impacts non-target species and their propagation in the environment. This review explores the potential application of cyanophages as a strategy for controlling cyanoHABs. It includes the identification and isolation of broad-spectrum and novel cyanophages, with a specific focus on freshwater Microcystis cyanophages, highlighting their broad spectrum and high efficiency. Additionally, recent advancements in cyanophage engineering are discussed, including genome modification, functional gene identification, and the construction of artificial cyanophages. Furthermore, the current state of application is addressed. Cyanophage is a promising control strategy for effectively managing cyanoHABs in aquatic environments.

UniKP: a unified framework for the prediction of enzyme kinetic parameters

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.