Evolving small-molecule biosensors with improved performance and reprogrammed ligand specificity using OrthoRep

Genetically-encoded biosensors are valuable for the optimization of small-molecule biosynthesis pathways, because they transduce the production of small-molecule ligands into a readout compatible with high-throughput screening or selection in vivo. However, engineering biosensors with appropriate response functions and ligand specificities remains challenging. Here, we show that the continuous hypermutation system, OrthoRep, can be effectively applied to evolve biosensors with high dynamic range, reprogrammed activity towards desired non-cognate ligands, and proper operational range for coupling to biosynthetic pathways. In particular, we encoded the allosteric transcriptional factor, BenM, on OrthoRep such that propagation of host yeast cells resulted in BenM's rapid and continuous diversification. When these cells were subjected to cycles of culturing and sorting on BenM activity in the presence and absence of its cognate ligand, muconic acid, or the non-cognate ligand, adipic acid, we obtained multiple BenM variants that respond to their corresponding ligands. These biosensors outperform previously-engineered BenM-based biosensors by achieving substantially greater dynamic range (up to ~180-fold-induction) and broadened operational range. Expression of select BenM variants in the presence of a muconic acid biosynthetic pathway demonstrated sensitive biosensor activation without saturating response, which should enable pathway and host engineering for higher production of muconic and adipic acids. Given the streamlined manner in which high-performance and versatile biosensors were evolved using OrthoRep, this study provides a template for generating custom biosensors for metabolic pathway engineering and other biotechnology goals.

Established tools and emerging trends for the production of recombinant proteins and metabolites in Pichia pastoris

Besides bakers’ yeast, the methylotrophic yeast Komagataella phaffii (also known as Pichia pastoris) has been developed into the most popular yeast cell factory for the production of heterologous proteins. Strong promoters, stable genetic constructs and a growing collection of freely available strains, tools and protocols have boosted this development equally as thorough genetic and cell biological characterization. This review provides an overview of state-of-the-art tools and techniques for working with P. pastoris, as well as guidelines for the production of recombinant proteins with a focus on small-scale production for biochemical studies and protein characterization. The growing applications of P. pastoris for in vivo biotransformation and metabolic pathway engineering for the production of bulk and specialty chemicals are highlighted as well.

Concerns with computational protein engineering programmes IPRO and OptMAVEn and metabolic pathway engineering programme optStoic

It has become customary in engineering to require a modelling component in research endeavours. In addition, as the code for these models becomes more byzantine in complexity, it is difficult for reviewers and readers to discern their value and understand the underlying code. This opinion piece summarizes the negative experience of the author with the IPRO and OptMAVEn computational protein engineering models as well as problems with the optStoic metabolic pathway model. In our hands, these models often fail to predict reliable ways to engineer proteins and metabolic pathways.

Genome-based engineering of ligninolytic enzymes in fungi

Background Many fungi grow as saprobic organisms and obtain nutrients from a wide range of dead organic materials. Among saprobes, fungal species that grow on wood or in polluted environments have evolved prolific mechanisms for the production of degrading compounds, such as ligninolytic enzymes. These enzymes include arrays of intense redox-potential oxidoreductase, such as laccase, catalase, and peroxidases. The ability to produce ligninolytic enzymes makes a variety of fungal species suitable for application in many industries, including the production of biofuels and antibiotics, bioremediation, and biomedical application as biosensors. However, fungal ligninolytic enzymes are produced naturally in small quantities that may not meet the industrial or market demands. Over the last decade, combined synthetic biology and computational designs have yielded significant results in enhancing the synthesis of natural compounds in fungi. Main body of the abstract In this review, we gave insights into different protein engineering methods, including rational, semi-rational, and directed evolution approaches that have been employed to enhance the production of some important ligninolytic enzymes in fungi. We described the role of metabolic pathway engineering to optimize the synthesis of chemical compounds of interest in various fields. We highlighted synthetic biology novel techniques for biosynthetic gene cluster (BGC) activation in fungo and heterologous reconstruction of BGC in microbial cells. We also discussed in detail some recombinant ligninolytic enzymes that have been successfully enhanced and expressed in different heterologous hosts. Finally, we described recent advance in CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas (CRISPR associated) protein systems as the most promising biotechnology for large-scale production of ligninolytic enzymes. Short conclusion Aggregation, expression, and regulation of ligninolytic enzymes in fungi require very complex procedures with many interfering factors. Synthetic and computational biology strategies, as explained in this review, are powerful tools that can be combined to solve these puzzles. These integrated strategies can lead to the production of enzymes with special abilities, such as wide substrate specifications, thermo-stability, tolerance to long time storage, and stability in different substrate conditions, such as pH and nutrients.