Phage-Assisted Continuous Evolution (PACE): A Guide Focused on Evolving Protein-DNA Interactions

Combining a Base Deaminase Mutator with Phage-Assisted Evolution

Phage-assisted evolution has emerged as a powerful technique for improving a protein's function by using mutagenesis and selective pressure. However, mutations typically occur throughout the host's genome and are not limited to the gene-of-interest (GOI): these undesirable genomic mutations can yield host cells that circumvent the system's selective pressure. Our system targets mutations specifically toward the GOI by combining T7 targeted mutagenesis and phage-assisted evolution. This system improves the structure and function of proteins by accumulating favorable mutations that can change its binding affinity, specificity, and activity.

Virus-assisted directed evolution of biomolecules

Directed evolution is a powerful technique that uses principles of natural evolution to enable the development of biomolecules with novel functions. However, the slow pace of natural evolution does not support the demand for rapidly generating new biomolecular functions in the laboratory. Viruses offer a unique path to design fast laboratory evolution experiments, owing to their innate ability to evolve much more rapidly than most living organisms, facilitated by a smaller genome size that tolerate a high frequency of mutations, as well as a fast rate of replication. These attributes offer a great opportunity to evolve various biomolecules by linking their activity to the replication of a suitable virus. This review highlights the recent advances in the application of virus-assisted directed evolution of designer biomolecules in both prokaryotic and eukaryotic cells.

Phage design and directed evolution to evolve phage for therapy

Phage therapy or Phage treatment is the use of bacteriolysing phage in treating bacterial infections by using the viruses that infects and kills bacteria. This technique has been studied and practiced very long ago, but with the advent of antibiotics, it has been neglected. This foregone technique is now witnessing a revival due to development of bacterial resistance. Nowadays, with the awareness of genetic sequence of organisms, it is required that informed choices of phages have to be made for the most efficacious results. Furthermore, phages with the evolving genes are taken into consideration for the subsequent improvement in treating the patients for bacterial diseases. In addition, direct evolution methods are increasingly developing, since these are capable of creating new biological molecules having changed or unique activities, such as, improved target specificity, evolution of novel proteins with new catalytic properties or creation of nucleic acids that are capable of recognizing required pathogenic bacteria. This system is incorporates continuous evolution such as protein or genes are put under continuous evolution by providing continuous mutagenesis with least human intervention. Although, this system providing continuous directed evolution is very effective, it imposes some challenges due to requirement of heavy investment of time and resources. This chapter focuses on development of phage as a therapeutic agent against various bacteria causing diseases and it improvement using direct evolution of proteins and nucleic acids such that they target specific organisms.

Genetic engineering of bacteriophages: Key concepts, strategies, and applications

Bacteriophages are the most abundant biological entity in the world and hold a tremendous amount of unexplored genetic information. Since their discovery, phages have drawn a great deal of attention from researchers despite their small size. The development of advanced strategies to modify their genomes and produce engineered phages with desired traits has opened new avenues for their applications. This review presents advanced strategies for developing engineered phages and their potential antibacterial applications in phage therapy, disruption of biofilm, delivery of antimicrobials, use of endolysin as an antibacterial agent, and altering the phage host range. Similarly, engineered phages find applications in eukaryotes as a shuttle for delivering genes and drugs to the targeted cells, and are used in the development of vaccines and facilitating tissue engineering. The use of phage display-based specific peptides for vaccine development, diagnostic tools, and targeted drug delivery is also discussed in this review. The engineered phage-mediated industrial food processing and biocontrol, advanced wastewater treatment, phage-based nano-medicines, and their use as a bio-recognition element for the detection of bacterial pathogens are also part of this review. The genetic engineering approaches hold great potential to accelerate translational phages and research. Overall, this review provides a deep understanding of the ingenious knowledge of phage engineering to move them beyond their innate ability for potential applications.

DNA-Encoded Libraries: Towards Harnessing their Full Power with Darwinian Evolution

DNA-encoded library (DEL) technologies are transforming the drug discovery process, enabling the identification of ligands at unprecedented speed and scale. DEL makes use of libraries that are orders of magnitude larger than traditional high-throughput screens. While a DNA tag alludes to a genotype-phenotype connection that is exploitable for molecular evolution, most of the work in the field is performed with libraries where the tag serves as an amplifiable barcode but does not allow "translation" into the synthetic product it is linked to. In this Review, we cover technologies that enable the "translation" of the genetic tag into synthetic molecules, both biochemically and chemically, and explore how it can be used to harness Darwinian evolutionary pressure.

Methods for the directed evolution of biomolecular interactions

Noncovalent interactions between biomolecules such as proteins and nucleic acids coordinate all cellular processes through changes in proximity. Tools that perturb these interactions are and will continue to be highly valuable for basic and translational scientific endeavors. By taking cues from natural systems, such as the adaptive immune system, we can design directed evolution platforms that can generate proteins that bind to biomolecules of interest. In recent years, the platforms used to direct the evolution of biomolecular binders have greatly expanded the range of types of interactions one can evolve. Herein, we review recent advances in methods to evolve protein-protein, protein-RNA, and protein-DNA interactions.

Cheating the Cheater: Suppressing False-Positive Enrichment during Biosensor-Guided Biocatalyst Engineering

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false-positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection) suppresses cheater population enrichment. Furthermore, we show that, if we couple the biosensor-based screen with an orthogonal prescreen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting. Using the approach developed here, we were successfully able to isolate PAL variants with ∼70% higher k_cat after a single sort. These mutants have tremendous potential in phenylketonuria (PKU) treatment and flavonoid production.

Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays

Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild-type variant. Even with a high-throughput screening on pooled libraries and Next-Generation Sequencing to boost the scale of read-outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in-silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino-acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio-physical rules for protein folding. Using machine learning-guided approaches, researchers can build more focused libraries, thus relieving themselves from labor-intensive screens and fast-tracking the optimization process. Here, we describe the current advances in massive-scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants.

Combining Rational Design and Continuous Evolution on Minimalist Proteins That Target the E-box DNA Site

Protein-based therapeutics are part of the next-generation arsenal of drugs being developed against proto-oncoprotein Myc. We designed protein MEF to mimic the basic region/helix-loop-helix/leucine zipper (bHLHZ) domain of Max and Myc, which bind to the E-box motif (enhancer box, CACGTG). To make MEF, we started with our rationally designed ME47, a hybrid of the Max basic region and E47 HLH, that effectively inhibited tumor growth in a mouse model of breast cancer. We used phage-assisted continuous evolution (PACE), which uncovered mutations at Arg12 that contact the DNA phosphodiester backbone. The Arg12 mutations improved ME47's stability. We replaced Cys29 with Ala to eliminate potential undesired disulfide formation and fused the designed FosW leucine zipper to mutated ME47 to increase the dimerization interface and E-box targeting activity. This "franken-protein" MEF comprises the Max basic region, E47 HLH, and FosW leucine zipper. Compared with ME47, MEF gives 2-fold stronger binding to E-box and 4-fold increased specificity for E-box over nonspecific DNA. The synergistic combination of rational design and PACE allowed us to make MEF and demonstrates the power and utility of our two-pronged approach toward development of promising protein drugs with robust structure and DNA-binding function.