Methods for the directed evolution of proteins

The effects of the chemical modification on immobilized lipase features are affected by the enzyme crowding in the support

In this article, we have analyzed the interactions between enzyme crowding on a given support and its chemical modification (ethylenediamine modification via the carbodiimide route and picryl sulfonic (TNBS) modification of the primary amino groups) on the enzyme activity and stability. Lipase from Thermomyces lanuginosus (TLL) and lipase B from Candida antarctica (CALB) were immobilized on octyl-agarose beads at two very different enzyme loadings, one of them exceeding the capacity of the support, one well under this capacity. Chemical modifications of the highly loaded and lowly loaded biocatalysts gave very different results in terms of activity and stability, which could increase or decrease enzyme activity depending on the enzyme support loading. For example, both lowly loaded biocatalysts increased their activity after modification while the effect was the opposite for the highly loaded biocatalysts. Additionally, the modification with TNBS of highly loaded CALB biocatalyst increased its stability while decrease the activity.

CRISPR base editor-based targeted random mutagenesis (BE-TRM) toolbox for directed evolution

Directed evolution (DE) of desired locus by targeted random mutagenesis (TRM) tools is a powerful approach for generating genetic variations with novel or improved functions, particularly in complex genomes. TRM-based DE involves developing a mutant library of targeted DNA sequences and screening the variants for the desired properties. However, DE methods have for a long time been confined to bacteria and yeasts. Lately, CRISPR/Cas and DNA deaminase-based tools that circumvent enduring barriers such as longer life cycle, small library sizes, and low mutation rates have been developed to facilitate DE in native genetic environments of multicellular organisms. Notably, deaminase-based base editing-TRM (BE-TRM) tools have greatly expanded the scope and efficiency of DE schemes by enabling base substitutions and randomization of targeted DNA sequences. BE-TRM tools provide a robust platform for the continuous molecular evolution of desired proteins, metabolic pathway engineering, creation of a mutant library of desired locus to evolve novel functions, and other applications, such as predicting mutants conferring antibiotic resistance. This review provides timely updates on the recent advances in BE-TRM tools for DE, their applications in biology, and future directions for further improvements. [BMB Reports 2024; 57(1): 30-39].

ACIDES: on-line monitoring of forward genetic screens for protein engineering

Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

A metagenomic library cloning strategy that promotes high-level expression of captured genes to enable efficient functional screening

Functional screening of environmental DNA (eDNA) libraries is a potentially powerful approach to discover enzymatic "unknown unknowns", but is usually heavily biased toward the tiny subset of genes preferentially transcribed and translated by the screening strain. We have overcome this by preparing an eDNA library via partial digest with restriction enzyme FatI (cuts CATG), causing a substantial proportion of ATG start codons to be precisely aligned with strong plasmid-encoded promoter and ribosome-binding sequences. Whereas we were unable to select nitroreductases from standard metagenome libraries, our FatI strategy yielded 21 nitroreductases spanning eight different enzyme families, each conferring resistance to the nitro-antibiotic niclosamide and sensitivity to the nitro-prodrug metronidazole. We showed expression could be improved by co-expressing rare tRNAs and encoded proteins purified directly using an embedded His6-tag. In a transgenic zebrafish model of metronidazole-mediated targeted cell ablation, our lead MhqN-family nitroreductase proved ∼5-fold more effective than the canonical nitroreductase NfsB.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.

Engineered bacterial orthogonal DNA replication system for continuous evolution

Continuous evolution can generate biomolecules for synthetic biology and enable evolutionary investigation. The orthogonal DNA replication system (OrthoRep) in yeast can efficiently mutate long DNA fragments in an easy-to-operate manner. However, such a system is lacking in bacteria. Therefore, we developed a bacterial orthogonal DNA replication system (BacORep) for continuous evolution. We achieved this by harnessing the temperate phage GIL16 DNA replication machinery in Bacillus thuringiensis with an engineered error-prone orthogonal DNA polymerase. BacORep introduces all 12 types of nucleotide substitution in 15-kilobase genes on orthogonally replicating linear plasmids with a 6,700-fold higher mutation rate than that of the host genome, the mutation rate of which is unchanged. Here we demonstrate the utility of BacORep-based continuous evolution by generating strong promoters applicable to model bacteria, Bacillus subtilis and Escherichia coli, and achieving a 7.4-fold methanol assimilation increase in B. thuringiensis. BacORep is a powerful tool for continuous evolution in prokaryotic cells.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Molecular analysis of the type III interferon complex and its applications in protein engineering

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.

Evolutionary "Crowdsourcing": Alignment of Fitness Landscapes Allows for Cross-species Adaptation of a Horizontally Transferred Gene

Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds. Despite the ubiquity of cross-species HGT, the effects of switching hosts on gene evolution remains understudied. Here, we present a framework to examine the evolutionary consequences of host-switching and apply this framework to an antibiotic resistance gene commonly found on conjugative plasmids. Specifically, we determined the adaptive landscape of this gene for a small set of mutationally connected genotypes in 3 enteric species. We uncovered that the landscape topographies were largely aligned with minimal host-dependent mutational effects. By simulating gene evolution over the experimentally gauged landscapes, we found that the adaptive evolution of the mobile gene in one species translated to adaptation in another. By simulating gene evolution over artificial landscapes, we found that sufficient alignment between landscapes ensures such "adaptive equivalency" across species. Thus, given adequate landscape alignment within a bacterial community, vehicles of HGT such as plasmids may enable a distributed form of genetic evolution across community members, where species can "crowdsource" adaptation.

Growth-coupled high throughput selection for directed enzyme evolution

Directed enzyme evolution has revolutionized the rapid development of enzymes with desired properties. However, the lack of a high-throughput method to identify the most suitable variants from a large pool of genetic diversity poses a major bottleneck. To overcome this challenge, growth-coupled in vivo high-throughput selection approaches (GCHTS) have emerged as a novel selection system for enzyme evolution. GCHTS links the survival of the host cell with the properties of the target protein, resulting in a screening system that is easily measurable and has a high throughput-scale limited only by transformation efficiency. This allows for the rapid identification of desired variants from a pool of >109 variants in each experiment. In recent years, GCHTS approaches have been extensively utilized in the directed evolution of multiple enzymes, demonstrating success in catalyzing non-native substrates, enhancing catalytic activity, and acquiring novel functions. This review introduces three main strategies employed to achieve GCHTS: the elimination of toxic compounds via desired variants, enabling host cells to thrive in hazardous conditions; the complementation of an auxotroph with desired variants, where essential genes for cell growth have been eliminated; and the control of the transcription or expression of a reporter gene related to host cell growth, regulated by the desired variants. Additionally, we highlighted the recent developments in the in vivo continuous evolution of enzyme technology, including phage-assisted continuous evolution (PACE) and orthogonal DNA Replication (OrthoRep). Furthermore, this review discusses the challenges and future prospects in the field of growth-coupled selection for protein engineering.

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

Bicyclo[3.2.0]carbocyclic Molecules and Redox Biotransformations: The Evolution of Closed-Loop Artificial Linear Biocatalytic Cascades and Related Redox-Neutral Systems

The role of cofactor recycling in determining the efficiency of artificial biocatalytic cascades has become paramount in recent years. Closed-loop cofactor recycling, which initially emerged in the 1990s, has made a valuable contribution to the development of this aspect of biotechnology. However, the evolution of redox-neutral closed-loop cofactor recycling has a longer history that has been integrally linked to the enzymology of oxy-functionalised bicyclo[3.2.0]carbocyclic molecule metabolism throughout. This review traces that relevant history from the mid-1960s to current times.

Hen egg white lysozyme encapsulated in ZIF-8 for performing promiscuous enzymatic Mannich reaction

Hen egg white lysozyme (HEWL) was exploited for the synthesis of β-amino carbonyl compounds through a direct and three-component Mannich reaction in aqueous, confirming high chemoselectivity toward imine. In order to further extend the applications of the enzyme, HEWL was encapsulated using a metal-organic framework (MOF). The reactivity, stereoselectivity, and reusability of the encapsulated enzyme were investigated. The reaction was significantly enhanced as compared to the non-encapsulated enzyme. A mutated version of the enzyme, containing Asp52Ala (D52A), lacking important catalytical residue, has lost the bacterial site activity against Micrococcus luteus (M. luteus) while the D52A variant displayed an increased rate of the Mannich reaction, indicating a different catalytical residue involved in the promiscuous reaction. Based on site-directed mutagenesis, molecular docking, and molecular dynamic studies, it was proposed that π-stacking, H-bond interactions, and the presence of water in the active site may play crucial roles in the mechanism of the reaction.

Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.

The Scent of Change: Sustainable Fragrances Through Industrial Biotechnology

Current environmental and safety considerations urge innovation to address the need for sustainable high-value chemicals that are embraced by consumers. This review discusses the concept of sustainable fragrances, as high-value, everyday and everywhere chemicals. Current and emerging technologies represent an opportunity to produce fragrances in an environmentally and socially responsible way. Biotechnology, including fermentation, biocatalysis, and genetic engineering, has the potential to reduce the environmental footprint of fragrance production while maintaining quality and consistency. Computational and in silico methods, including machine learning (ML), are also likely to augment the capabilities of sustainable fragrance production. Continued innovation and collaboration will be crucial to the future of sustainable fragrances, with a focus on developing novel sustainable ingredients, as well as ethical sourcing practices.

Multivalent protein-drug conjugates - An emerging strategy for the upgraded precision and efficiency of drug delivery to cancer cells

With almost 20 million new cases per year, cancer constitutes one of the most important challenges for public health systems. Unlike traditional chemotherapy, targeted anti-cancer strategies employ sophisticated therapeutics to precisely identify and attack cancer cells, limiting the impact of drugs on healthy cells and thereby minimizing the unwanted side effects of therapy. Protein drug conjugates (PDCs) are a rapidly growing group of targeted therapeutics, composed of a cancer-recognition factor covalently coupled to a cytotoxic drug. Several PDCs, mainly in the form of antibody-drug conjugates (ADCs) that employ monoclonal antibodies as cancer-recognition molecules, are used in the clinic and many PDCs are currently in clinical trials. Highly selective, strong and stable interaction of the PDC with the tumor marker, combined with efficient, rapid endocytosis of the receptor/PDC complex and its subsequent effective delivery to lysosomes, is critical for the efficacy of targeted cancer therapy with PDCs. However, the bivalent architecture of contemporary clinical PDCs is not optimal for tumor receptor recognition or PDCs internalization. In this review, we focus on multivalent PDCs, which represent a rapidly evolving and highly promising therapeutics that overcome most of the limitations of current bivalent PDCs, enhancing the precision and efficiency of drug delivery to cancer cells. We present an expanding set of protein scaffolds used to generate multivalent PDCs that, in addition to folding into well-defined multivalent molecular structures, enable site-specific conjugation of the cytotoxic drug to ensure PDC homogeneity. We provide an overview of the architectures of multivalent PDCs developed to date, emphasizing their efficacy in the targeted treatment of various cancers.

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.

Analyzing Current Trends and Possible Strategies to Improve Sucrose Isomerases' Thermostability

Due to their ability to produce isomaltulose, sucrose isomerases are enzymes that have caught the attention of researchers and entrepreneurs since the 1950s. However, their low activity and stability at temperatures above 40 °C have been a bottleneck for their industrial application. Specifically, the instability of these enzymes has been a challenge when it comes to their use for the synthesis and manufacturing of chemicals on a practical scale. This is because industrial processes often require biocatalysts that can withstand harsh reaction conditions, like high temperatures. Since the 1980s, there have been significant advancements in the thermal stabilization engineering of enzymes. Based on the literature from the past few decades and the latest achievements in protein engineering, this article systematically describes the strategies used to enhance the thermal stability of sucrose isomerases. Additionally, from a theoretical perspective, we discuss other potential mechanisms that could be used for this purpose.

Combinatorial mixtures of organic solutes for improved liquid/liquid extraction of ions

Biological systems routinely extract and organize ions in complex yet highly ordered and active systems. Much of this function is attributed to proteins, although recent evidence indicates aggregates of lipids are also capable of molecular recognition. Here we tested the hypothesis that combinatorial mixtures of organic solutes might lead to enhanced liquid/liquid extraction. We started with liquid oleic acid as an organic phase extracting copper ions from water and added a library of additives. By using Bayesian optimization to autonomously direct the combinatorial formulation, we discovered mixtures that enhanced the extraction performance. The main additive that improved the system was octylphosphonic acid. Interestingly, the optimal mixture has a significant improvement compared to this additive alone. This suggests that the combinations of organic solutes are better than using pure components in liquid/liquid extraction. Furthermore, we found that precipitation occurs in the samples showing better extraction efficiency, which has interesting material properties and potential for new types of supramolecular biosensors.

A biocatalytic platform for asymmetric alkylation of α-keto acids by mining and engineering of methyltransferases

Catalytic asymmetric α-alkylation of carbonyl compounds represents a long-standing challenge in synthetic organic chemistry. Herein, we advance a dual biocatalytic platform for the efficient asymmetric alkylation of α-keto acids. First, guided by our recently obtained crystal structures, we develop SgvMVAV as a general biocatalyst for the enantioselective methylation, ethylation, allylation and propargylation of a range of α-keto acids with total turnover numbers (TTNs) up to 4,600. Second, we mine a family of bacterial HMTs from Pseudomonas species sharing less than 50% sequence identities with known HMTs and evaluated their activities in SAM regeneration. Our best performing HMT from P. aeruginosa, PaHMT, displays the highest SAM regeneration efficiencies (TTN up to 7,700) among HMTs characterized to date. Together, the synergistic use of SgvMVAV and PaHMT affords a fully biocatalytic protocol for asymmetric methylation featuring a record turnover efficiency, providing a solution to the notorious problem of asymmetric alkylation.

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Will tomorrow's mineral materials be grown?

Biomineralization, the capacity to form minerals, has evolved in a great diversity of bacterial lineages as an adaptation to different environmental conditions and biological functions. Microbial biominerals often display original properties (morphology, composition, structure, association with organics) that significantly differ from those of abiotically formed counterparts, altogether defining the 'mineral phenotype'. In principle, it should be possible to take advantage of microbial biomineralization processes to design and biomanufacture advanced mineral materials for a range of technological applications. In practice, this has rarely been done so far and only for a very limited number of biomineral types. This is mainly due to our poor understanding of the underlying molecular mechanisms controlling microbial biomineralization pathways, preventing us from developing bioengineering strategies aiming at improving biomineral properties for different applications. Another important challenge is the difficulty to upscale microbial biomineralization from the lab to industrial production. Addressing these challenges will require combining expertise from environmental microbiologists and geomicrobiologists, who have historically been working at the forefront of research on microbe-mineral interactions, alongside bioengineers and material scientists. Such interdisciplinary efforts may in the future allow the emergence of a mineral biomanufacturing industry, a critical tool towards the development more sustainable and circular bioeconomies.

Directed Evolution of Piperazic Acid Incorporation by a Nonribosomal Peptide Synthetase

Engineering of biosynthetic enzymes is increasingly employed to synthesize structural analogues of antibiotics. Of special interest are nonribosomal peptide synthetases (NRPSs) responsible for the production of important antimicrobial peptides. Here, directed evolution of an adenylation domain of a Pro-specific NRPS module completely switched substrate specificity to the non-standard amino acid piperazic acid (Piz) bearing a labile N-N bond. This success was achieved by UPLC-MS/MS-based screening of small, rationally designed mutant libraries and can presumably be replicated with a larger number of substrates and NRPS modules. The evolved NRPS produces a Piz-derived gramicidin S analogue. Thus, we give new impetus to the too-early dismissed idea that widely accessible low-throughput methods can switch the specificity of NRPSs in a biosynthetically useful fashion.

CRISPR-Cas9-Mediated Cytosine Base Editing Screen for the Functional Assessment of CALR Intron Variants in Japanese Encephalitis Virus Replication

The Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus that causes viral encephalitis in humans, pigs and other mammals across Asia and the Western Pacific. Genetic screening tools such as CRISPR screening, DNA sequencing and RNA interference have greatly improved our understanding of JEV replication and its potential antiviral approaches. However, information on exon and intron mutations associated with JEV replication is still scanty. CRISPR-Cas9-mediated cytosine base editing can efficiently generate C: G-to-T: A conversion in the genome of living cells. One intriguing application of base editing is to screen pivotal variants for gene function that is yet to be achieved in pigs. Here, we illustrate that CRISPR-Cas9-mediated cytosine base editor, known as AncBE4max, can be used for the functional analysis of calreticulin (CALR) variants. We conducted a CRISPR-Cas9-mediated cytosine base editing screen using 457 single guide RNAs (sgRNAs) against all exons and introns of CALR to identify loss-of-function variants involved in JEV replication. We unexpectedly uncovered that two enriched sgRNAs targeted the same site in intron-2 of the CALR gene. We found that mutating four consecutive G bases in the intron-2 of the CALR gene to four A bases significantly inhibited JEV replication. Thus, we established a CRISPR-Cas9-mediated cytosine-base-editing point mutation screening technique in pigs. Our results suggest that CRISPR-mediated base editing is a powerful tool for identifying the antiviral functions of variants in the coding and noncoding regions of the CALR gene.

High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

Charting the Evolution of Chemoenzymatic Strategies in the Syntheses of Complex Natural Products

Bolstered by recent advances in bioinformatics, genetics, and enzyme engineering, the field of chemoenzymatic synthesis has enjoyed a rapid increase in popularity and utility. This Perspective explores the integration of enzymes into multistep chemical syntheses, highlighting the unique potential of biocatalytic transformations to streamline the synthesis of complex natural products. In particular, we identify four primary conceptual approaches to chemoenzymatic synthesis and illustrate each with a number of landmark case studies. Future opportunities and challenges are also discussed.

DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering

With advances in machine learning (ML)-assisted protein engineering, models based on data, biophysics, and natural evolution are being used to propose informed libraries of protein variants to explore. Synthesizing these libraries for experimental screens is a major bottleneck, as the cost of obtaining large numbers of exact gene sequences is often prohibitive. Degenerate codon (DC) libraries are a cost-effective alternative for generating combinatorial mutagenesis libraries where mutations are targeted to a handful of amino acid sites. However, existing computational methods to optimize DC libraries to include desired protein variants are not well suited to design libraries for ML-assisted protein engineering. To address these drawbacks, we present DEgenerate Codon Optimization for Informed Libraries (DeCOIL), a generalized method that directly optimizes DC libraries to be useful for protein engineering: to sample protein variants that are likely to have both high fitness and high diversity in the sequence search space. Using computational simulations and wet-lab experiments, we demonstrate that DeCOIL is effective across two specific case studies, with the potential to be applied to many other use cases. DeCOIL offers several advantages over existing methods, as it is direct, easy to use, generalizable, and scalable. With accompanying software (https://github.com/jsunn-y/DeCOIL), DeCOIL can be readily implemented to generate desired informed libraries.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

Increase in Charge and Density Improves the Strength and Toughness of Mussel Foot Protein 5 Inspired Protein Materials

Mussel foot protein 5 (fp5) found in the adhesive byssal plaque of Mediterranean mussel Mytilus galloprovincialis exhibits exceptional underwater adhesion to diverse surfaces to the extent that adhesion strength typically exceeds the cohesive strength of the plaque. While sequence effects such as presence of charged residues, metal ion coordination, and high catechol content have been identified to govern fp5's interaction with surfaces, molecular contributors to its cohesive strength remain to be fully understood. Addressing this issue is critical for designing mussel-inspired sequences for new adhesives and biomaterials enabled by synthetic biology. Here we carry out all-atom molecular dynamics simulations on hydrated model fp5 biopolymer melts to understand how sequence features such as tyrosine and charge content affect packing density and inter-residue and ionic interaction strengths and consequently influence the cohesive strength and toughness. Systematic serine (S) substitutions for lysine (K), arginine (R) and tyrosine (Y) residues reveal that Y to S substitution surprisingly results in improvement of cohesive strength due to densification of the material by removal of steric hindrances, whereas the removal of charge in K and R to S substitutions has a detrimental impact on strength and toughness as it reduces cohesive interactions facilitated by electrostatic interactions. Additionally, melts formed from split fp5 sequences with only C or N terminal halves show distinct mechanical responses that further illustrate the role of charge. Our findings provide new insights for designing materials that could potentially surpass the performance of existing biomolecular and bioinspired adhesives, specifically by tailoring sequences for balancing charge and excluded volume effects.

Divergent directed evolution of a TetR-type repressor towards aromatic molecules

Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.

Breakthrough in CRISPR/Cas system: Current and future directions and challenges

Targeted genome editing (GE) technology has brought a significant revolution in fictional genomic research and given hope to plant scientists to develop desirable varieties. This technology involves inducing site-specific DNA perturbations that can be repaired through DNA repair pathways. GE products currently include CRISPR-associated nuclease DNA breaks, prime editors generated DNA flaps, single nucleotide-modifications, transposases, and recombinases. The discovery of double-strand breaks, site-specific nucleases (SSNs), and repair mechanisms paved the way for targeted GE, and the first-generation GE tools, ZFNs and TALENs, were successfully utilized in plant GE. However, CRISPR-Cas has now become the preferred tool for GE due to its speed, reliability, and cost-effectiveness. Plant functional genomics has benefited significantly from the widespread use of CRISPR technology for advancements and developments. This review highlights the progress made in CRISPR technology, including multiplex editing, base editing (BE), and prime editing (PE), as well as the challenges and potential delivery mechanisms.

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

Construction and Applications of Mammalian Cell-Based DNA-Encoded Peptide/Protein Libraries

DNA-encoded peptide/protein libraries are the starting point for protein evolutionary modification and functional peptide/antibody selection. Different display technologies, protein directed evolution, and deep mutational scanning (DMS) experiments employ DNA-encoded libraries to provide sequence variations for downstream affinity- or function-based selections. Mammalian cells promise the inherent post-translational modification and near-to-natural conformation of exogenously expressed mammalian proteins and thus are the best platform for studying transmembrane proteins or human disease-related proteins. However, due to the current technical bottlenecks of constructing mammalian cell-based large size DNA-encoded libraries, the advantages of mammalian cells as screening platforms have not been fully exploited. In this review, we summarize the current efforts in constructing DNA-encoded libraries in mammalian cells and the existing applications of these libraries in different fields.

A novel computationally engineered collagenase reduces the force required for tooth extraction in an ex-situ porcine jaw model

The currently employed tooth extraction methods in dentistry involve mechanical disruption of the periodontal ligament fibers, leading to inevitable trauma to the bundle bone comprising the socket walls. In our previous work, we have shown that a recombinantly expressed truncated version of clostridial collagenase G (ColG) purified from Escherichia coli efficiently reduced the force needed for tooth extraction in an ex-situ porcine jaw model, when injected into the periodontal ligament. Considering that enhanced thermostability often leads to higher enzymatic activity and to set the basis for additional rounds of optimization, we used a computational protein design approach to generate an enzyme to be more thermostable while conserving the key catalytic residues. This process generated a novel collagenase (ColG-variant) harboring sixteen mutations compared to ColG, with a nearly 4℃ increase in melting temperature. Herein, we explored the potential of ColG-variant to further decrease the physical effort required for tooth delivery using our established ex-situ porcine jaw model. An average reduction of 11% was recorded in the force applied to extract roots of mandibular split first and second premolar teeth treated with ColG-variant, relative to those treated with ColG. Our results show for the first time the potential of engineering enzyme properties for dental medicine and further contribute to minimally invasive tooth extraction.

Enzymatic Characterization of the Isocitrate Dehydrogenase with Dual Coenzyme Specificity from the Marine Bacterium Umbonibacter marinipuiceus

Isocitrate dehydrogenase (IDH) can be divided into NAD⁺-dependent and NADP⁺-dependent types based on the coenzyme specificity. It is worth noting that some IDHs exhibit dual coenzyme specificity characteristics. Herein, a dual coenzyme-dependent IDH from Umbonibacter Marinipuiceus (UmIDH) was expressed, purified, and identified in detail for the first time. SDS-PAGE and Gel filtration chromatography analyses showed that UmIDH is an 84.7 kDa homodimer in solution. The K_m values for NAD⁺ and NADP⁺ are 1800.0 ± 64.4 μM and 1167.7 ± 113.0 μM in the presence of Mn²⁺, respectively. Meanwhile, the catalytic efficiency (k_cat/K_m) of UmIDH is only 2.3-fold greater for NADP⁺ than NAD⁺. The maximal activity for UmIDH occurred at pH 8.5 (with Mn²⁺) or pH 8.7 (with Mg²⁺) and at 35 °C (with Mn²⁺ or Mg²⁺). Heat inactivation assay revealed that UmIDH sustained 50% of maximal activity after incubation at 57 °C for 20 min with either Mn²⁺ or Mg²⁺. Moreover, three putative core coenzyme binding residues (R345, L346, and V352) of UmIDH were evaluated by site-directed mutagenesis. This recent work identified a unique dual coenzyme-dependent IDH and achieved the groundbreaking bidirectional modification of this specific IDH's coenzyme dependence for the first time. This provides not only a reference for the study of dual coenzyme-dependent IDH, but also a basis for the investigation of the coenzyme-specific evolutionary mechanisms of IDH.

High-throughput library transgenesis in Caenorhabditis elegans via Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS)

High-throughput transgenesis using synthetic DNA libraries is a powerful method for systematically exploring genetic function. Diverse synthesized libraries have been used for protein engineering, identification of protein-protein interactions, characterization of promoter libraries, developmental and evolutionary lineage tracking, and various other exploratory assays. However, the need for library transgenesis has effectively restricted these approaches to single-cell models. Here, we present Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS), a simple yet powerful approach to large-scale transgenesis that overcomes typical limitations encountered in multicellular systems. TARDIS splits the transgenesis process into a two-step process: creation of individuals carrying experimentally introduced sequence libraries, followed by inducible extraction and integration of individual sequences/library components from the larger library cassette into engineered genomic sites. Thus, transformation of a single individual, followed by lineage expansion and functional transgenesis, gives rise to thousands of genetically unique transgenic individuals. We demonstrate the power of this system using engineered, split selectable TARDIS sites in Caenorhabditis elegans to generate (1) a large set of individually barcoded lineages and (2) transcriptional reporter lines from predefined promoter libraries. We find that this approach increases transformation yields up to approximately 1000-fold over current single-step methods. While we demonstrate the utility of TARDIS using C. elegans, in principle the process is adaptable to any system where experimentally generated genomic loci landing pads and diverse, heritable DNA elements can be generated.

Rational Design of the β-Bulge Gate in a Green Fluorescent Protein Accelerates the Kinetics of Sulfate Sensing

Detection of anions in complex aqueous media is a fundamental challenge with practical utility that can be addressed by supramolecular chemistry. Biomolecular hosts such as proteins can be used and adapted as an alternative to synthetic hosts. Here, we report how the mutagenesis of the β-bulge residues (D137 and W138) in mNeonGreen, a bright, monomeric fluorescent protein, unlocks and tunes the anion preference at physiological pH for sulfate, resulting in the turn-off sensor SulfOFF-1. This unprecedented sensing arises from an enhancement in the kinetics of binding, largely driven by position 138. In line with these data, molecular dynamics (MD) simulations capture how the coordinated entry and gating of sulfate into the β-barrel is eliminated upon mutagenesis to facilitate binding and fluorescence quenching.

A dual gene-specific mutator system installs all transition mutations at similar frequencies in vivo

Targeted in vivo hypermutation accelerates directed evolution of proteins through concurrent DNA diversification and selection. Although systems employing a fusion protein of a nucleobase deaminase and T7 RNA polymerase present gene-specific targeting, their mutational spectra have been limited to exclusive or dominant C:G→T:A mutations. Here we describe eMutaT7transition, a new gene-specific hypermutation system, that installs all transition mutations (C:G→T:A and A:T→G:C) at comparable frequencies. By using two mutator proteins in which two efficient deaminases, PmCDA1 and TadA-8e, are separately fused to T7 RNA polymerase, we obtained similar numbers of C:G→T:A and A:T→G:C substitutions at a sufficiently high frequency (∼6.7 substitutions in 1.3 kb gene during 80-h in vivo mutagenesis). Through eMutaT7transition-mediated TEM-1 evolution for antibiotic resistance, we generated many mutations found in clinical isolates. Overall, with a high mutation frequency and wider mutational spectrum, eMutaT7transition is a potential first-line method for gene-specific in vivo hypermutation.

Enzyme Stability-Activity Trade-Off: New Insights from Protein Stability Weaknesses and Evolutionary Conservation

A general limitation of the use of enzymes in biotechnological processes under sometimes nonphysiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures and applied it to a large set of globular enzymes with known experimental structure and catalytic sites. Our analysis showed a striking oscillatory pattern of free energy compensation centered on the catalytic region. Indeed, catalytic residues are usually nonoptimal with respect to stability, but residues in the first shell around the catalytic site are, on the average, stability strengths and thus compensate for this lack of stability; residues in the second shell are weaker again, and so on. This trend is consistent across all enzyme families. It is accompanied by a similar, but less pronounced, pattern of residue conservation across evolution. In addition, we analyzed cold- and heat-adapted enzymes separately and highlighted different patterns of stability strengths and weaknesses, which provide insight into the longstanding problem of catalytic rate enhancement in cold environments. The successful comparison of our stability and conservation results with experimental fitness data, obtained by deep mutagenesis scanning, led us to propose criteria for improving catalytic activity while maintaining enzyme stability, a key goal in enzyme design.

Nuclear Imaging of CAR T Immunotherapy to Solid Tumors: In Terms of Biodistribution, Viability, and Cytotoxic Effect

Immunotherapy has become a mainstay of cancer therapy. Since chimeric antigen receptor (CAR) T immunotherapy achieves unprecedented success in curing hematological malignancies, the possibility of it revolutionizing the paradigm of solid tumors has aroused increasing attention. However, the restricted accessibility to tumor parenchyma, the immunosuppressive tumor microenvironment, and antigen heterogeneity of solid tumors make it difficult to replicate its success. Therefore, dynamic evaluation of CAR T cells' tumor accessibility, intratumoral viability, and anti-tumor cytotoxicity is necessary to facilitate its translation to solid tumors. Besides, real-timely imaging above events in vivo can help evaluate therapeutic responses and optimize CAR T immunotherapy for solid tumors. Nuclear imaging, including positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging, is frequently applied for evaluating adoptive cell therapies owing to its excellent sensitivity, high tissue penetration, and great translation potential. In addition, quantitative analysis can be performed in dynamic and noninvasive patterns. This review focuses on recent advances in PET/SPECT technologies and imaging probes in monitoring CAR T cells' migration, viability, and cytotoxicity to solid tumors post-administration. Prospects of what should be done in the next stage to promote CAR T therapy's application in solid tumors are also discussed.

Identification, characterization, and engineering of glycosylation in thrombolyticsa

Cardiovascular diseases, such as myocardial infarction, ischemic stroke, and pulmonary embolism, are the most common causes of disability and death worldwide. Blood clot hydrolysis by thrombolytic enzymes and thrombectomy are key clinical interventions. The most widely used thrombolytic enzyme is alteplase, which has been used in clinical practice since 1986. Another clinically used thrombolytic protein is tenecteplase, which has modified epitopes and engineered glycosylation sites, suggesting that carbohydrate modification in thrombolytic enzymes is a viable strategy for their improvement. This comprehensive review summarizes current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modification of glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of this engineering strategy. Outstanding combinatorics originating from multiple glycosylation sites and the vast variety of covalently attached glycan species can be addressed by directed evolution or rational design. Directed evolution pipelines would benefit from more efficient cell-free expression and high-throughput screening assays, while rational design must employ structure prediction by machine learning and in silico characterization by supercomputing. Perspectives on challenges and opportunities for improvement of thrombolytic enzymes by engineering and evolution of protein glycosylation are provided.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁-M8 were devised from scratch. More importantly, the designed 1a8uD₁-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

Engineering TNA polymerases through iterative cycles of directed evolution

DNA polymerases are important tools for biotechnology, synthetic biology, and chemical biology as they are routinely used to amplify and edit genetic information. However, natural polymerases do not recognize artificial genetic polymers (also known as xeno-nucleic acids or XNAs) with unique sugar-phosphate backbone structures. Directed evolution offers a possible solution to this problem by facilitating the discovery of engineered versions of natural polymerases that can copy genetic information back and forth between DNA and XNA. Here we report a directed evolution strategy for discovering polymerases that can synthesize threose nucleic acid (TNA) on DNA templates. The workflow involves library generation and expression in E. coli, high-throughput microfluidics-based screening of uniform water-in-oil droplets, plasmid recovery, secondary screening, and library regeneration. This technique is sufficiently general that it could be applied to a wide range of problems involving DNA modifying enzymes.

Fluorescence coupling strategies in fluorescence-activated droplet sorting (FADS) for ultrahigh-throughput screening of enzymes, metabolites, and antibodies

Fluorescence-activated droplet sorting (FADS) has emerged as a powerful tool for ultrahigh-throughput screening of enzymes, metabolites, and antibodies. Fluorescence coupling strategies (FCSs) are key to the development of new FADS methods through their coupling of analyte properties such as concentration, activities, and affinity with fluorescence signals. Over the last decade, a series of FCSs have been developed, greatly expanding applications of FADS. Here, we review recent advances in FCS for different analyte types, providing a critical comparison of the available FCSs and further classification into four categories according to their principles. We also summarize successful FADS applications employing FCSs in enzymes, metabolites, and antibodies. Further, we outline possible future developments in this area.

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.

Updated analysis to reject the laboratory-engineering hypothesis of SARS-CoV-2

A clear understanding of the origin of SARS-CoV-2 is important for future pandemic preparedness. Here, I provided an updated analysis of the type IIS endonuclease maps in genomes of alphacoronavirus, betacoronavirus, and SARS-CoV-2. Scenarios to engineer SARS-CoV-2 in the laboratory and the associated workload was also discussed. The analysis clearly shows that the endonuclease fingerprint does not indicate a synthetic origin of SARS-CoV-2 and engineering a SARS-CoV-2 virus in the laboratory is extremely challenging both scientifically and financially. On the contrary, current scientific evidence does support the animal origin of SARS-CoV-2.