Unbiased libraries in protein directed evolution

A computational toolbox for the assembly yield of complex and heterogeneous structures

The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.

Engineering All-Round Cellulase for Bioethanol Production

One strategy to decrease both the consumption of crude oil and environmental damage is through the production of bioethanol from biomass. Cellulolytic enzyme stability and enzymatic hydrolysis play important roles in the bioethanol process. However, the gradually increased ethanol concentration often reduces enzyme activity and leads to inactivation, thereby limiting the final ethanol yield. Herein, we employed an optimized Two-Gene Recombination Process (2GenReP) approach to evolve the exemplary cellulase CBHI for practical bioethanol fermentation. Two all-round CBHI variants (named as R2 and R4) were obtained with simultaneously improved ethanol resistance, organic solvent inhibitor tolerance, and enzymolysis stability in simultaneous saccharification and fermentation (SSF). Notably, CBHI R4 had a 7.0- to 34.5-fold enhanced catalytic efficiency (kcat/KM) in the presence/absence of ethanol. Employing the evolved CBHI R2 and R4 in the 1G bioethanol process resulted in up to 10.27% (6.7 g/L) improved ethanol yield (ethanol concentration) than non-cellulase, which was far more beyond than other optimization strategies. Besides bioenergy fields, this transferable protein engineering routine holds the potential to generate all-round enzymes that meet the requirement in biotransformation and bioenergy fields.

Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations

Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6-9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3-5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.

Evolving Robust and Interpretable Enzymes for the Bioethanol Industry

Obtaining a robust and applicable enzyme for bioethanol production is a dream for biorefinery engineers. Herein, we describe a general method to evolve an all-round and interpretable enzyme that can be directly employed in the bioethanol industry. By integrating the transferable protein evolution strategy InSiReP 2.0 (In Silico guided Recombination Process), enzymatic characterization for actual production, and computational molecular understanding, the model cellulase PvCel5A (endoglucanase II Cel5A from Penicillium verruculosum) was successfully evolved to overcome the remaining challenges of low ethanol and temperature tolerance, which primarily limited biomass transformation and bioethanol yield. Remarkably, application of the PvCel5A variants in both first- and second-generation bioethanol production processes (i. Conventional corn ethanol fermentation combined with the in situ pretreatment process; ii. cellulosic ethanol fermentation process) resulted in a 5.7-10.1 % increase in the ethanol yield, which was unlikely to be achieved by other optimization techniques.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

Synthetic DNA Libraries for Protein Engineering Toward Process Improvement in Drug Synthesis

Speeding-up enzyme engineering by directed evolution is a primary target to be achieved for a wider uptake of biocatalysis in pharmaceutical process development. The capability to rapidly generate the designed sequence diversity has profound implications in the overall optimization of protein function. Drawbacks associated with traditional PCR methods for sequence diversification interfere with the generation of all the variants that have been designed. On the contrary, the enhanced quality of synthetic DNA libraries makes the exploration of sequence space more efficient. Here, methods for the effective utilization of synthetic DNA libraries are described. The overall procedure allows the generation of ready-to-screen libraries within two weeks from synthetic DNA acquisition.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Novel perspectives of environmental proteomics

The connection among genome expression, proteome alteration, metabolism regulation and phenotype change under environmental stresses is very vague. It is a tough task for the traditional research approaches to reveal the related scientific mechanisms of the above connection at molecular and systematic levels. Proteomics approach is an insightful tool for revealing the biological functions, metabolic networks and functional protein interaction networks of cells and organisms under stresses at the systematic level. The purpose of this review is to provide an insightful guideline on how to set up a proteomic investigation for revealing biomolecule mechanisms, protein biomarkers and metabolism networks related to stress response, pollutant recognition, transport and biodegradation, and providing an insightful high-throughput approach for screening functional enzymes and effective microbes based on bioinformatics and functional verification method. Furthermore, the toxicity evaluation of pollutants and byproducts by proteomics approaches provides a scientific insight for early diagnosis of ecological risk and determination of the effectiveness of pollutant treatment techniques.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

Evolution-aided engineering of plant specialized metabolism

The evolution of new traits in living organisms occurs via the processes of mutation, recombination, genetic drift, and selection. These processes that have resulted in the immense biological diversity on our planet are also being employed in metabolic engineering to optimize enzymes and pathways, create new-to-nature reactions, and synthesize complex natural products in heterologous systems. In this review, we discuss two evolution-aided strategies for metabolic engineering-directed evolution, which improves upon existing genetic templates using the evolutionary process, and combinatorial pathway reconstruction, which brings together genes evolved in different organisms into a single heterologous host. We discuss the general principles of these strategies, describe the technologies involved and the molecular traits they influence, provide examples of their use, and discuss the roadblocks that need to be addressed for their wider adoption. A better understanding of these strategies can provide an impetus to research on gene function discovery and biochemical evolution, which is foundational for improved metabolic engineering. These evolution-aided approaches thus have a substantial potential for improving our understanding of plant metabolism in general, for enhancing the production of plant metabolites, and in sustainable agriculture.

A novel framework for engineering protein loops exploring length and compositional variation

Insertions and deletions (indels) are known to affect function, biophysical properties and substrate specificity of enzymes, and they play a central role in evolution. Despite such clear significance, this class of mutation remains an underexploited tool in protein engineering with few available platforms capable of systematically generating and analysing libraries of varying sequence composition and length. We present a novel DNA assembly platform (InDel assembly), based on cycles of endonuclease restriction digestion and ligation of standardised dsDNA building blocks, that can generate libraries exploring both composition and sequence length variation. In addition, we developed a framework to analyse the output of selection from InDel-generated libraries, combining next generation sequencing and alignment-free strategies for sequence analysis. We demonstrate the approach by engineering the well-characterized TEM-1 β-lactamase Ω-loop, involved in substrate specificity, identifying multiple novel extended spectrum β-lactamases with loops of modified length and composition-areas of the sequence space not previously explored. Together, the InDel assembly and analysis platforms provide an efficient route to engineer protein loops or linkers where sequence length and composition are both essential functional parameters.

Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.

Connecting conformational dynamics and epistasis has so far been limited to a few proteins and a single fitness trait. Here, the authors provide evidence of positive epistasis on multiple catalytic traits in the evolution and dynamics of engineered cytochrome P450 monooxygenase, offering insights for in silico protein design.

Different strategies for the lipase immobilization on the chitosan based supports and their applications

Immobilized enzymes have received incredible interests in industry, pharmaceuticals, chemistry and biochemistry sectors due to their various advantages such as ease of separation, multiple reusability, non-toxicity, biocompatibility, high activity and resistant to environmental changes. This review in between various immobilized enzymes focuses on lipase as one of the most practical enzyme and chitosan as a preferred biosupport for lipase immobilization and provides a broad range of studies of recent decade. We highlight several aspects of lipase immobilization on the surface of chitosan support containing various types of lipase and immobilization techniques from physical adsorption to covalent bonding and cross-linking with their benefits and drawbacks. The recent advances and future perspectives that can improve the present problems with lipase and chitosan such as high-price of lipase and low mechanical resistance of chitosan are also discussed. According to the literature, optimization of immobilization methods, combination of these methods with other techniques, physical and chemical modifications of chitosan, co-immobilization and protein engineering can be useful as a solution to overcome the mentioned limitations.

Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic, and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multisite random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.

Directed evolution for enzyme development in biocatalysis

As an important sector of the chemical industry, biocatalysis requires the continuous development of enzymes with tailor-made activity, selectivity, stability, or tolerance to unnatural environments. This is now routinely achieved by directed evolution based on iterative cycles of genetic diversification and activity screening. Here, we highlight its recent developments. First, the design of "smarter" libraries by focused mutagenesis may be a crucial start-up for a fast and successful outcome. Then library assembly and expression are also key steps that benefits from modern molecular biology progresses. Finally, various strategies may be considered for library screening depending on the final objective: while low-throughput direct assays have been very successful in generating enzymes for important biocatalytic processes, even in bringing completely new chemistries to the enzyme world, ultrahigh-throughput screening methods are emerging as powerful approaches for engineering the next generation of industrial enzymes.

Directed Evolution. The Legacy of a Nobel Prize

This article is part of an anniversary issue of Journal of Molecular Evolution, commenting on a paper published on 1999 by the Nobel laureate Frances Arnold and her colleague Kentaro Miyazaki. The paper by Miyazaki and Arnold presented saturation mutagenesis as an alternative method to random mutagenesis for obtaining enzymes with increasing stability. Both techniques were conceived to accomplish directed evolution, an approach honoured by the Nobel Prize of Chemistry 2018. Here, I am commenting on the pros and cons of random and saturation mutagenesis, while also discussing important results from directed evolution. I conclude that molecular evolution is finding new applications in science and it is definitely an integral part of the genomic era's revolution.

Combined Approach to Engineer a Highly Active Mutant of Processive Chitinase Hydrolyzing Crystalline Chitin

Serratia marcescens chitinase A (SmChiA) processively hydrolyzes recalcitrant biomass crystalline chitin under mild conditions. Here, we combined multiple sequence alignment, site-saturation mutagenesis, and automated protein purification and activity measurement with liquid-handling robot to reduce the number of mutation trials and shorten the screening time for hydrolytic activity improvement of SmChiA. The amino acid residues, which are not conserved in the alignment and are close to the aromatic residues along the substrate-binding sites in the crystal structure, were selected for site-saturation mutagenesis. Using the previously identified highly active F232W/F396W mutant as a template, we identified the F232W/F396W/S538V mutant, which shows further improved hydrolytic activity just by trying eight different sites. Importantly, valine was not found in the multiple sequence alignment at Ser538 site of SmChiA. Our combined approach allows engineering of highly active enzyme mutants, which cannot be identified only by the introduction of predominant amino acid residues in the multiple sequence alignment.

Advances in Recombinant Lipases: Production, Engineering, Immobilization and Application in the Pharmaceutical Industry

Lipases are one of the most used enzymes in the pharmaceutical industry due to their efficiency in organic syntheses, mainly in the production of enantiopure drugs. From an industrial viewpoint, the selection of an efficient expression system and host for recombinant lipase production is highly important. The most used hosts are Escherichia coli and Komagataella phaffii (previously known as Pichia pastoris) and less often reported Bacillus and Aspergillus strains. The use of efficient expression systems to overproduce homologous or heterologous lipases often require the use of strong promoters and the co-expression of chaperones. Protein engineering techniques, including rational design and directed evolution, are the most reported strategies for improving lipase characteristics. Additionally, lipases can be immobilized in different supports that enable improved properties and enzyme reuse. Here, we review approaches for strain and protein engineering, immobilization and the application of lipases in the pharmaceutical industry.

Split & mix assembly of DNA libraries for ultrahigh throughput on-bead screening of functional proteins

Site-saturation libraries reduce protein screening effort in directed evolution campaigns by focusing on a limited number of rationally chosen residues. However, uneven library synthesis efficiency leads to amino acid bias, remedied at high cost by expensive custom synthesis of oligonucleotides, or through use of proprietary library synthesis platforms. To address these shortcomings, we have devised a method where DNA libraries are constructed on the surface of microbeads by ligating dsDNA fragments onto growing, surface-immobilised DNA, in iterative split-and-mix cycles. This method-termed SpliMLiB for Split-and-Mix Library on Beads-was applied towards the directed evolution of an anti-IgE Affibody (ZIgE), generating a 160,000-membered, 4-site, saturation library on the surface of 8 million monoclonal beads. Deep sequencing confirmed excellent library balance (5.1% ± 0.77 per amino acid) and coverage (99.3%). As SpliMLiB beads are monoclonal, they were amenable to direct functional screening in water-in-oil emulsion droplets with cell-free expression. A FACS-based sorting of the library beads allowed recovery of hits improved in Kd over wild-type ZIgE by up to 3.5-fold, while a consensus mutant of the best hits provided a 10-fold improvement. With SpliMLiB, directed evolution workflows are accelerated by integrating high-quality DNA library generation with an ultra-high throughput protein screening platform.

One Pot Use of Combilipases for Full Modification of Oils and Fats: Multifunctional and Heterogeneous Substrates

Lipases are among the most utilized enzymes in biocatalysis. In many instances, the main reason for their use is their high specificity or selectivity. However, when full modification of a multifunctional and heterogeneous substrate is pursued, enzyme selectivity and specificity become a problem. This is the case of hydrolysis of oils and fats to produce free fatty acids or their alcoholysis to produce biodiesel, which can be considered cascade reactions. In these cases, to the original heterogeneity of the substrate, the presence of intermediate products, such as diglycerides or monoglycerides, can be an additional drawback. Using these heterogeneous substrates, enzyme specificity can promote that some substrates (initial substrates or intermediate products) may not be recognized as such (in the worst case scenario they may be acting as inhibitors) by the enzyme, causing yields and reaction rates to drop. To solve this situation, a mixture of lipases with different specificity, selectivity and differently affected by the reaction conditions can offer much better results than the use of a single lipase exhibiting a very high initial activity or even the best global reaction course. This mixture of lipases from different sources has been called “combilipases” and is becoming increasingly popular. They include the use of liquid lipase formulations or immobilized lipases. In some instances, the lipases have been coimmobilized. Some discussion is offered regarding the problems that this coimmobilization may give rise to, and some strategies to solve some of these problems are proposed. The use of combilipases in the future may be extended to other processes and enzymes.

Application of directed evolution and back-to-consensus algorithms to human alpha1-antitrypsin leads to diminished anti-protease activity and augmented anti-inflammatory activities

Primarily known as an elastase inhibitor, human alpha1-antitrypsin also exerts anti-inflammatory and immunomodulatory effects, both in vitro and in vivo. While the anti-protease mechanism of alpha1-antitrypsin is attributed to a particular protein domain coined the reactive center loop, anti-inflammatory and immunomodulatory loci within the molecule remain to be identified. In the present study, directed evolution and back-to-consensus algorithms were applied to human alpha1-antitrypsin. Six unique functional candidate sites were identified on the surface of the molecule; in manipulating these sites by point mutations, a recombinant mutant form of alpha1-antitrypsin was produced, depicting a requirement for sites outside the reactive center loop as essential for protease inhibition, and displaying enhanced anti-inflammatory activities. Taken together, outcomes of the present study establish a potential use for directed evolution in advancing our understanding of site-specific protein functions, offering a platform for development of context- and disease-specific alpha1-antitrypsin-based therapeutics.