A primer to directed evolution: current methodologies and future directions

Engineering Phages to Fight Multidrug-Resistant Bacteria

Facing the global "superbug" crisis due to the emergence and selection for antibiotic resistance, phages are among the most promising solutions. Fighting multidrug-resistant bacteria requires precise diagnosis of bacterial pathogens and specific cell-killing. Phages have several potential advantages over conventional antibacterial agents such as host specificity, self-amplification, easy production, low toxicity as well as biofilm degradation. However, the narrow host range, uncharacterized properties, as well as potential risks from exponential replication and evolution of natural phages, currently limit their applications. Engineering phages can not only enhance the host bacteria range and improve phage efficacy, but also confer new functions. This review first summarizes major phage engineering techniques including both chemical modification and genetic engineering. Subsequent sections discuss the applications of engineered phages for bacterial pathogen detection and ablation through interdisciplinary approaches of synthetic biology and nanotechnology. We discuss future directions and persistent challenges in the ongoing exploration of phage engineering for pathogen control.

Directed evolution and metabolic engineering generate an Escherichia coli cell factory for de novo production of 4-hydroxymandelate

4-hydroxymandelate is a high-value aromatic compound used in the medicine, cosmetics, food, and chemical industry. However, existing natural extraction and chemical synthesis methods are costly and lead to environmental pollution. This study employed metabolic engineering and directed evolution strategies for de novo 4-hydroxymandelate biosynthesis. Two key challenges were addressed: insufficient precursor supply and limited activity of crucial enzymes. Through gene overexpression and multi-level gene interference using CRISPRi, An Escherichia coli chassis capable of producing the key precursor 4-hydroxyphenylpyruvate and the titer reached 5.05 mM (0.91 g/L). A mutant clone was obtained, HmaSV152G, which showed a 5.13-fold improvement in the catalytic rate. During fermentation, a high production of 194.87 mM (32.768 g/L) 4-hydroxymandelate was achieved in 76 h with a batch supply of glucose in a 5-L bioreactor. This study demonstrated the great potential of biosensors in protein engineering and provides a reference for large-scale production of other high-value aromatic compounds.

Advances in stress-tolerance elements for microbial cell factories

Microorganisms, particularly extremophiles, have evolved multiple adaptation mechanisms to address diverse stress conditions during survival in unique environments. Their responses to environmental coercion decide not only survival in severe conditions but are also an essential factor determining bioproduction performance. The design of robust cell factories should take the balance of their growing and bioproduction into account. Thus, mining and redesigning stress-tolerance elements to optimize the performance of cell factories under various extreme conditions is necessary. Here, we reviewed several stress-tolerance elements, including acid-tolerant elements, saline-alkali-resistant elements, thermotolerant elements, antioxidant elements, and so on, providing potential materials for the construction of cell factories and the development of synthetic biology. Strategies for mining and redesigning stress-tolerance elements were also discussed. Moreover, several applications of stress-tolerance elements were provided, and perspectives and discussions for potential strategies for screening stress-tolerance elements were made.

Proteomics and transcriptomic analyses provide new insights into the pectin polysaccharide biosynthesis in Premna puberula Pamp

Premna puberula Pamp. is a plant with similarities to both food and medicine, which has attracted the attention of researchers because of the rich pectin in its leaves. However, the mechanism of efficient pectin accumulation remains unclear. Through transcriptomic and proteomic analyses, this study investigated the differentially expressed genes and proteins associated with pectin during various developmental stages of P. puberula. The combined omics approach revealed that the majority of differential genes are mainly involved in the anabolic metabolism of primary and secondary metabolites. Notable differential pathways include starch and sucrose metabolism, amino acid sugar metabolism, and nucleotide sugar metabolism. The SWEET gene family was identified and analyzed, leading to the cloning of PpSWEET15 and subcellular localization. Overexpression of PpSWEET15 resulted in a significant decrease in sucrose content in leaves from 134.44 μg·g-1 to <10 μg·g-1. Glucose content decreased from 407.75 μg·g-1 to 318.38 μg·g-1, while fructose levels showed no significant difference between the two groups of leaves. This comprehensive transcriptomic and proteomic analysis provides valuable insights into the molecular mechanisms underlying the efficient accumulation of pectin in P. puberula.

Enzyme Engineering: Performance Optimization, Novel Sources, and Applications in the Food Industry

This review summarizes the latest progress in enzyme preparation, including enzyme design and modification technology, exploration of new enzyme sources, and application of enzyme preparation in food processing, detection, and preservation. The directed evolution technology improved the stability and catalytic efficiency of enzymes, while enzyme immobilization technology enhanced reusability and industrial applicability. Extremozymes and biomimetic enzymes exhibit excellent performance under harsh conditions. In food processing, enzyme preparation can improve food quality and flavor. In food detection, enzymes combined with immune detection and biosensors realize rapid detection of allergens, pollutants, and pesticide residues. In food preservation, enzymes enhance food quality by extending shelf life and inhibiting microbial growth. In the future, enzyme engineering will be combined with computer-aided design, artificial intelligence, and new material technology to promote intelligent enzyme design and multifunctional enzyme preparation development and help the technological upgrading and sustainable development of the food industry and green chemistry.

Designing proteins: Mimicking natural protein sequence heterogeneity

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins. Folding free energy computations demonstrated that the designed sequences refold to their native structures with high precision, except for proteins with large disordered regions. In addition, direct coupling analysis showed a strong correlation between predicted and actual protein contacts, with accuracy exceeding 82% for a large number of top pairs (>4L). The algorithm also resolved biases in previous designs, ensuring a more accurate representation of protein interactions. Overall, it not only mimics the natural heterogeneity of proteins but also ensures correct folding, marking a significant advancement in protein design and engineering.

Structural Elucidation of a Metagenomic Urethanase and Its Engineering Towards Enhanced Hydrolysis Profiles

While plastics like polyethylene terephthalate can already be degraded efficiently by the activity of hydrolases, other synthetic polymers like polyurethanes (PUs) and polyamides (PAs) largely resist biodegradation. In this study, we solved the first crystal structure of the metagenomic urethanase UMG-SP-1, identified highly flexible loop regions to comprise active site residues, and targeted a total of 20 potential hot spots by site-saturation mutagenesis. Engineering campaigns yielded variants with single mutations, exhibiting almost 3- and 8-fold improved activity against highly stable N-aryl urethane and amide bonds, respectively. Furthermore, we demonstrated the release of the corresponding monomers from a thermoplastic polyester-PU and a PA (nylon 6) by the activity of a single, metagenome-derived urethanase after short incubation times. Thereby, we expanded the hydrolysis profile of UMG-SP-1 beyond the reported low-molecular weight carbamates. Together, these findings promise advanced strategies for the bio-based degradation and recycling of plastic materials and waste, aiding efforts to establish a circular economy for synthetic polymers.

Enhancing Cellular and Enzymatic Properties Through In Vivo Continuous Evolution

Directed evolution seeks to evolve target genes at a rate far exceeding the natural mutation rate, thereby endowing cellular and enzymatic properties with desired traits. In vivo continuous directed evolution achieves these purposes by generating libraries within living cells, enabling a continuous cycle of mutant generation and selection, enhancing the exploration of gene variants. Continuous evolution has become powerful tools for unraveling evolution mechanism and improving cellular and enzymatic properties. This review categorizes current continuous evolution into three distinct classes: non-targeted chromosomal, targeted chromosomal, and extra-chromosomal hypermutation approaches. It also compares various continuous evolution strategies based on different principles, providing a reference for selecting suitable methods for specific evolutionary goals. Furthermore, this review discusses the two primary limitations for further widespread application of in vivo continuous evolution, which are lack of general applicability and insufficient mutagenic capability. We envision that developing generally applicable mutagenic components and methods to enhance mutation rates for in vivo continuous evolution are promising future directions for wide range applications of continuous evolution.

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion

Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.

Integrating Computational Design and Experimental Approaches for Next-Generation Biologics

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

Direct Screening of PET Hydrolase Activity in Culture Medium Based on Turbidity Reduction

The development of an efficient screening method for the activity of PET-degrading enzymes represents a significant technological advance in the field of enzyme research, with the potential to facilitate the advancement of enzymes for PET recycling. By examining the stable conditions of PET suspension and enzyme production conditions, we developed a method to quantify PET-degrading enzyme activity in E. coli culture medium using turbidity reduction as an indicator. High PET concentration or ionic strength caused aggregation of PET, and the best condition for activity detection was 0.5 mg mL-1 PET in 50 mM sodium phosphate pH 7.0. Preculture of E. coli increased the purity of enzyme secreted in medium. To evaluate the screening method, 720 colonies of the PET2-7M-H229X-F233X mutant library were analyzed and three candidates of high-activity mutants were obtained. The thermostability of the mutants could also be easily measured by measuring the residual activity after heat treatment. The H229T-F233M mutant showed 3.4 times higher degradation rate against PET film than the template enzyme at the initial time. The molecular dynamics simulation implied that the F233M mutation makes space for making an α helix and that the H229T mutation resolved the steric hindrance with Trp199. These mutations were speculated to change the angle of the Trp199 side chain of PET2 to an angle similar to that of the Trp185 of IsPETase, making it suitable for PET binding to the active center. Screening of activity using PET suspensions is compatible with robotic automation and is expected to be useful for validating computationally predicted mutations.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach

BACKGROUND

Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach.

RESULTS

According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3.

CONCLUSIONS

In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.

Shifting the pH profiles of Staphylococcus epidermidis lipase (SEL) and Staphylococcus hyicus lipase (SHL) through generating chimeric lipases by DNA shuffling strategy

Enzymes are often required to function in a particular reaction condition by the industrial procedure. In order to identify critical residues affecting the optimum pH of Staphylococcal lipases, chimeric lipases from homologous lipases were generated via a DNA shuffling strategy. Chimeric 1 included mutations of G166S, K212E, T243A, H271Y. Chimeric 2 consisted of substitutions of K212E, T243A, H271Y. Chimeric 3 contained substitutions of K212E, R359L. From the screening results, the pH profiles for chimeric 1 and 2 lipases were shifted from pH 7 to 6. While the pH of chimeric 3 was shifted to 8. It seems the mutation of K212E in chimeric 1 and 2 decreased the pH to 6 by changing the electrostatic potential surface. Furthermore, chimeric 3 showed 10 ˚C improvement in the optimum temperature due to the rigidification of the catalytic loop through the hydrophobic interaction network. Moreover, the substrate specificity of chimeric 1 and 2 was increased towards the longer carbon length chains due to the mutation of T243A adjacent to the lid region through increasing the flexibility of the lid. Current study illustrated that directed evolution successfully modified lipase properties including optimum pH, temperature and substrate specificity through mutations, especially near catalytic and lid regions.

Protein engineering in the computational age: An open source framework for exploring mutational landscapes in silico

The field of protein engineering has seen tremendous expansion in the last decade, with researchers developing novel proteins with specialised functionalities for a range of uses, from drug discovery to industrial biotechnology. The emergence of computational tools and high-throughput screening technology has substantially sped up the process of protein engineering. However, much of the expertise required to engage in such projects is still concentrated in the hands of a few specialised individuals, including computational biologists and structural biochemists. The international Genetically Engineered Machine (iGEM) competition represents a platform for undergraduate students to innovate in synthetic biology. Yet, due to their complexity, arduous protein engineering projects are hindered by the resources available and strict timelines of the competition. The authors highlight how the 2022 iGEM Team, 'Sporadicate', set out to develop InFinity 1.0, a computational framework for increased accessibility to effective protein engineering, hoping to increase awareness and accessibility to novel in silico tools.

The directed evolution of NDM-1

β-Lactam antibiotics are among the most frequently prescribed therapeutic agents. A common mechanism of resistance toward β-lactam antibiotics is the production of β-lactamases. These enzymes are capable of hydrolyzing the β-lactam bond, rendering the drug inactive. Among the four described classes, the metallo- β-lactamases (MBLs, class B) employ one or two zinc ions in the active site for catalysis. One of the three most clinically relevant MBLs is New Delhi Metallo- β-Lactamase (NDM-1). The current study sought to investigate the in vitro protein evolution of NDM-1 β-lactamase using error-prone polymerase chain reaction. Evaluation revealed that variants were not found to confer higher levels of resistance toward meropenem based on amino acid substitutions. Thus, we postulate that increases in transcription or changes in zinc transport may be clinically more relevant to meropenem resistance than amino acid substitutions.

Exploring the Protein Sequence Space with Global Generative Models

Recent advancements in specialized large-scale architectures for training images and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, have demonstrated exceptional capabilities in processing, translating, and generating human language. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Several of these models have been developed with the goal of generating sequences in novel regions of the protein space. In this work, we provide an overview of the use of protein generative models, reviewing (1) language models for the design of novel artificial proteins, (2) works that use non-transformer architectures, and (3) applications in directed evolution approaches.

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Genetic engineering for heterologous expression has advanced in recent years. Model systems such as Escherichia coli, Bacillus subtilis and Pichia pastoris are often used as host microorganisms for the enzymatic production of L-asparaginase, an enzyme widely used in the clinic for the treatment of leukemia and in bakeries for the reduction of acrylamide. Newly developed recombinant L-asparaginase (L-ASNase) may have a low affinity for asparagine, reduced catalytic activity, low stability, and increased glutaminase activity or immunogenicity. Some successful commercial preparations of L-ASNase are now available. Therefore, obtaining novel L-ASNases with improved properties suitable for food or clinical applications remains a challenge. The combination of rational design and/or directed evolution and heterologous expression has been used to create enzymes with desired characteristics. Computer design, combined with other methods, could make it possible to generate mutant libraries of novel L-ASNases without costly and time-consuming efforts. In this review, we summarize the strategies and approaches for obtaining and developing L-ASNase with improved properties.

Unlocking chloride sensing in the red at physiological pH with a fluorescent rhodopsin-based host

Chloride is a vital ion for all forms of life. Protein-based fluorescent biosensors can enable researchers to visualize chloride in cells but remain underdeveloped. Here, we demonstrate how a single point mutation in an engineered microbial rhodopsin results in ChloRED-1-CFP. This membrane-bound host is a far-red emitting, ratiometric sensor that provides a reversible readout of chloride in live bacteria at physiological pH, setting the stage to investigate the roles of chloride in diverse biological contexts.

The Surprising Ease of Finding Optimal Solutions for Controlling Nonlinear Phenomena in Quantum and Classical Complex Systems

This Perspective addresses the often observed surprising ease of achieving optimal control of nonlinear phenomena in quantum and classical complex systems. The circumstances involved are wide-ranging, with scenarios including manipulation of atomic scale processes, maximization of chemical and material properties or synthesis yields, Nature's optimization of species' populations by natural selection, and directed evolution. Natural evolution will mainly be discussed in terms of laboratory experiments with microorganisms, and the field is also distinct from the other domains where a scientist specifies the goal(s) and oversees the control process. We use the word "control" in reference to all of the available variables, regardless of the circumstance. The empirical observations on the ease of achieving at least good, if not excellent, control in diverse domains of science raise the question of why this occurs despite the generally inherent complexity of the systems in each scenario. The key to addressing the question lies in examining the associated control landscape, which is defined as the optimization objective as a function of the control variables that can be as diverse as the phenomena under consideration. Controls may range from laser pulses, chemical reagents, chemical processing conditions, out to nucleic acids in the genome and more. This Perspective presents a conjecture, based on present findings, that the systematics of readily finding good outcomes from controlled phenomena may be unified through consideration of control landscapes with the same common set of three underlying assumptions─the existence of an optimal solution, the ability for local movement on the landscape, and the availability of sufficient control resources─whose validity needs assessment in each scenario. In practice, many cases permit using myopic gradient-like algorithms while other circumstances utilize algorithms having some elements of stochasticity or introduced noise, depending on whether the landscape is locally smooth or rough. The overarching observation is that only relatively short searches are required despite the common high dimensionality of the available controls in typical scenarios.