Abstract Precise control of substrate transport across compartment boundaries is critical for the design of enzymatic nanoreactors, yet selective and tunable molecular flux remains a major bottleneck in confined reaction systems. Outer membrane porins offer a promising solution, as their transport properties can be modulated through protein engineering. Among these, the moderately anion-selective porin PhoE from Escherichia coli is an attractive candidate due to its defined pore architecture. Several PhoE mutants have previously been described and analyzed, predominantly in whole-cell assays. However, such systems limit the quantitative resolution of substrate transport, as specific flux cannot easily be disentangled from expression levels, membrane insertion efficiency, and variations in the amount of functional protein. To overcome these limitations, we employed enzyme-loaded nanoreactors as a minimal and well-defined platform to directly quantify PhoE-mediated substrate flux independent of cellular complexity. Wild-type PhoE was compared to mutants exhibiting distinct transport behaviors in bacterial cells, with F111C showing approximately 50% lower transport. Additionally, we designed and characterized the novel deletion mutant PhoEΔ102–107, which exhibited 2.4-fold higher transport than wild-type PhoE. The bottom-up synthetic biology system enabled a direct comparison of specific transport rates, demonstrating the potential of these variants for transport tuning in engineered enzymatic nanoreactors. Complementary molecular dynamics simulations provided structural insight into the molecular basis of the observed transport behavior of the PhoE mutants.
Directed evolution is a powerful approach for engineering new biomolecular and cellular functions [1–3]. In contrast to rational design approaches, directed evolution exploits diversity and evolution to shape the behavior of biological matter by applying the Darwinian cycle of mutation, selection, and amplification of genes and genomes. By doing so, the field of directed evolution has generated important insights into the evolutionary process [4–6] as well as useful RNAs, proteins, and systems with wide-ranging applications across biotechnology and medicine [7–11]. To mimic the evolutionary process, classical directed evolution approaches carry out cycles of ex vivo diversification on genes of interest (GOIs), transformation of the resulting gene libraries into cells, and selection of the desired function (Figure 1.1). Each iteration of this cycle is defined as a round of evolution, and as selection stringency increases over rounds, either automatically through competition or manually through changing conditions (or both), this process can lead GOIs closer and closer to the desired function. This overall process makes practical sense for a number of reasons, especially for the goal of protein engineering (i.e. GOI encodes a protein). First, ex vivo diversification is appropriate, because test tube molecular biology techniques such as DNA shuffling, site-directed saturation mutagenesis, and error-prone (ep) polymerase chain reaction (PCR) [2] are capable of generating exceptionally high and precise levels of sequence diversity for any GOI. Second, transforming diversified libraries of the GOI into cells is appropriate, because each GOI variant needs to be translated into a protein in order to express its function, and cells, especially model microbes, are naturally robust hosts for protein expression. Third, carrying out selection inside cells is appropriate, because (i) cells automatically maintain the genotype–phenotype connection between the GOI and expressed protein that is necessary for amplification of desired variants,
Genetic engineering of enzymes has played a significant role in multiple pharmaceutical synthetic processes. The need for selective, robust catalysts that can operate under process chemistry conditions has driven the need for mutagenesis approaches, which have successfully delivered a variety of process-optimized biocatalysts. In this chapter we give an overview of different directed evolution methods that have been applied to pharmaceutical intermediates during the last decade. The exciting progress that has been made since the landmark sitagliptin study in 2010 demonstrates the role that engineered biocatalysts can play in the manufacture of active pharmaceutical ingredients (APIs).
Artificial metalloenzymes are an emerging class of functional biomacromolecules that both provide useful information for native metalloenzymes and have the potential to catalyze important transformations in industrial biocatalysis. Metalloenzymes can be engineered by two complementary methods: rational design and directed evolution. Rational design uses predetermined knowledge of biochemical systems to design a novel protein, often with the aid of computation, while directed evolution exploits the genetic nature of proteins to select for a desired attribute, such as reactivity or stability. Both methods have resulted in the development of metalloenzymes that catalyze abiological reactions, use abiological cofactors, and produce commercially significant compounds. This chapter seeks to provide a survey of the state-of-the-art in the field of metalloenzyme engineering.
In vivo biosensors enable protein functional development, by associating genetic changes to distinguishable traits, in high throughput directed evolution methodologies. With advances in molecular tools for biosensor design and discovery, the scope of tunable proteins and characteristics has expanded considerably in recent years. As the capacity to modulate independent and metabolic pathway proteins increases, directed protein evolution techniques are becoming more relevant in promising sectors such as value-added chemicals, nutraceuticals, and therapeutics biosynthesis. In vivo biosensors can deliver rapid evaluation of protein intracellular responses, including their behaviors in the context of an entire proteome. This chapter presents an overview on the types of in vivo biosensors commonly used in directed protein evolution and some key considerations for biosensor design and selection. Recent novel and inventive applications of biosensors toward single and multivariate directed protein evolution will also be discussed.
