Enzyme optimization: moving from blind evolution to statistical exploration of sequence-function space

A patent-based consideration of latest platforms in the art of directed evolution: a decade long untold story

Directed (or in vitro) evolution of proteins and metabolic pathways requires tools for creating genetic diversity and identifying protein variants with new or improved functional properties. Besides simplicity, reliability, speed, versatility, universal applicability and economy of the technique, the new science of synthetic biology requires improved means for construction of smart and high-quality mutant libraries to better navigate the sequence diversity. In vitro CRISPR/Cas9-mediated mutagenic (ICM) system and machine-learning (ML)-assisted approaches to directed evolution are now in the field to achieve the goal. This review describes the gene diversification strategies, screening and selection methods, in silico (computer-aided), Cas9-mediated and ML-based approaches to mutagenesis, developed especially in the last decade, and their patent position. The objective behind is to emphasize researchers the need for noting which mutagenesis, screening or selection method is patented and then selecting a suitable restriction-free approach to sequence diversity. Techniques and evolved products subject to patent rights need commercial license if their use is for purposes other than private or experimental research.

Chromohalobacter salixigens Uronate Dehydrogenase: Directed Evolution for Improved Thermal Stability and Mutant CsUDH-inc X-ray Crystal Structure

Chromohalobacter salixigens contains a uronate dehydrogenase termed CsUDH that can convert uronic acids to their corresponding C1,C6-dicarboxy aldaric acids, an important enzyme reaction applicable for biotechnological use of sugar acids. To increase the thermal stability of this enzyme for biotechnological processes, directed evolution using gene family shuffling was applied, and the hits selected from 2-tier screening of a shuffled gene family library contained in total 16 mutations, only some of which when examined individually appreciably increased thermal stability. Most mutations, while having minimal or no effect on thermal stability when tested in isolation, were found to exhibit synergy when combined; CsUDH-inc containing all 16 mutations had ΔK _t ^0.5 +18 °C, such that k _cat was unaffected by incubation for 1 hr at ~70 °C. X-ray crystal structure of CsUDH-inc showed tight packing of the mutated residue side-chains, and comparison of rescaled B-values showed no obvious differences between wild type and mutant structures. Activity of CsUDH-inc was severely depressed on glucuronic and galacturonic acids. Combining select combinations of only three mutations resulted in good or comparable activity on these uronic acids, while maintaining some improved thermostability with ΔK _t ^0.5 ~+ 10 °C, indicating potential to further thermally optimize CsUDH for hyperthermophilic reaction environments.

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype-phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward.

Forty Years of Directed Evolution and its Continuously Evolving Technology Toolbox - A Review of the Patent Landscape

Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for decades. Rational design and directed evolution are two major pathways to achieve the desired ends. Whilst rational protein design approach has made substantial progress, the idea of using a method based on cycles of mutagenesis and natural selection to develop novel binding proteins, enzymes and structures has attracted great attention. Laboratory evolution of proteins/enzymes requires new tools and analytical approaches to create genetic diversity and identifying variants with desired traits. In this pursuit, construction of sufficiently large libraries of target molecules to search for improved variants and the need for new protocols to alter the properties of target molecules has been a continuing challenge in the directed evolution experiments. This review will discuss the in vivo and in vitro gene diversification tools, library screening or selection approaches, and artificial intelligence/machine-learning-based strategies to mutagenesis developed in the last forty years to accelerate the natural process of evolution in creating new functional protein variants, optimization of microbial strains and transformation of enzymes into industrial machines. Analyzing patent position over these techniques and mechanisms also constitutes an integral and distinctive part of this review. The aim is to provide an up-to-date resource/technology toolbox for research-based and pharmaceutical companies to discover the boundaries of competitor's intellectual property (IP) portfolio, their freedom-to-operate in the relevant IP landscape, and the need for patent due diligence analysis to rule out whether use of a particular patented mutagenesis method, library screening/selection technique falls outside the safe harbor of experimental use exemption. While so doing, we have referred to some recent cases that emphasize the significance of selecting a suitable gene diversification strategy in directed evolution experiments. This article is protected by copyright. All rights reserved.

New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review

Today's environmental balance has been compromised by the unreasonable and sometimes dangerous actions committed by humans to maintain their dominance over the Earth's natural resources. As a result, oceans are contaminated by the different types of plastic trash, crude oil coming from mismanagement of transporting ships spilling it in the water, and air pollution due to increasing production of greenhouse gases, such as CO₂ and CH₄ etc., into the atmosphere. The lands, agricultural fields, and groundwater are also contaminated by the infamous chemicals viz., polycyclic aromatic hydrocarbons, pyrethroids pesticides, bisphenol-A, and dioxanes. Therefore, bioremediation might function as a convenient alternative to restore a clean environment. However, at present, the majority of bioremediation reports are limited to the natural capabilities of microbial enzymes. Synthetic biology with uncompromised supervision of ethical standards could help to outsmart nature's engineering, such as the CETCH cycle for improved CO₂ fixation. Additionally, a blend of synthetic biology with machine learning algorithms could expand the possibilities of bioengineering. This review summarized current state-of-the-art knowledge of the data-assisted enzyme redesigning to actively promote new research on important enzymes to ameliorate the environment.

Machine learning for enzyme engineering, selection and design

Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.

Broadening the scope of biocatalytic C-C bond formation

The impeccable control over chemo-, site-, and stereoselectivity possible in enzymatic reactions has led to a surge in the development of new biocatalytic methods. Despite carbon-carbon (C-C) bonds providing the central framework for organic molecules, development of biocatalytic methods for their formation has been largely confined to the use of a select few lyases over the last several decades, limiting the types of C-C bond-forming transformations possible through biocatalytic methods. This Review provides an update on the suite of enzymes available for highly selective biocatalytic C-C bond formation. Examples will be discussed in reference to the (1) native activity of enzymes, (2) alteration of activity through protein or substrate engineering for broader applicability, and (3) utility of the biocatalyst for abiotic synthesis.

GeneORator: An Effective Strategy for Navigating Protein Sequence Space More Efficiently through Boolean OR-Type DNA Libraries

Directed evolution requires the creation of genetic diversity and subsequent screening or selection for improved variants. For DNA mutagenesis, conventional site-directed methods implicitly utilize the Boolean AND operator (creating all mutations simultaneously), producing a combinatorial explosion in the number of genetic variants as the number of mutations increases. We introduce GeneORator, a novel strategy for creating DNA libraries based on the Boolean logical OR operator. Here, a single library is divided into many subsets, each containing different combinations of the desired mutations. Consequently, the effect of adding more mutations on the number of genetic combinations is additive (Boolean OR logic) and not exponential (AND logic). We demonstrate this strategy with large-scale mutagenesis studies, using monoamine oxidase-N ( Aspergillus niger) as the exemplar target. First, we mutated every residue in the secondary structure-containing regions (276 out of a total 495 amino acids) to screen for improvements in k_cat. Second, combinatorial OR-type libraries permitted screening of diverse mutation combinations in the enzyme active site to detect activity toward novel substrates. In both examples, OR-type libraries effectively reduced the number of variants searched up to 10¹⁰-fold, dramatically reducing the screening effort required to discover variants with improved and/or novel activity. Importantly, this approach enables the screening of a greater diversity of mutation combinations, accessing a larger area of a protein's sequence space. OR-type libraries can be applied to any biological engineering objective requiring DNA mutagenesis, and the approach has wide ranging applications in, for example, enzyme engineering, antibody engineering, and synthetic biology.

Enhancement of biocatalyst activity and protection against stressors using a microbial exoskeleton

Whole cell biocatalysts can perform numerous industrially-relevant chemical reactions. While they are less expensive than purified enzymes, whole cells suffer from inherent reaction rate limitations due to transport resistance imposed by the cell membrane. Furthermore, it is desirable to immobilize the biocatalysts to enable ease of separation from the reaction mixture. In this study, we used a layer-by-layer (LbL) self-assembly process to create a microbial exoskeleton which, simultaneously immobilized, protected, and enhanced the reactivity of a whole cell biocatalyst. As a proof of concept, we used Escherichia coli expressing homoprotocatechuate 2,3-dioxygenase (HPCD) as a model biocatalyst and coated it with up to ten alternating layers of poly(diallyldimethylammonium chloride) (PDADMAC) and silica. The microbial exoskeleton also protected the biocatalyst against a variety of external stressors including: desiccation, freeze/thaw, exposure to high temperatures, osmotic shock, as well as against enzymatic attack by lysozyme, and predation by protozoa. While we observed increased permeability of the outer membrane after exoskeleton deposition, this had a moderate effect on the reaction rate (up to two-fold enhancement). When the exoskeleton construction was followed by detergent treatment to permeabilize the cytoplasmic membrane, up to 15-fold enhancement in the reaction rate was reached. With the exoskeleton, we increased in the reaction rate constants as much as 21-fold by running the biocatalyst at elevated temperatures ranging from 40 °C to 60 °C, a supraphysiologic temperature range not accessible by unprotected bacteria.

A strategy to identify a ketoreductase that preferentially synthesizes pharmaceutically relevant (S)-alcohols using whole-cell biotransformation

Introduction

Chemical industries are constantly in search of an expeditious and environmentally benign method for producing chiral synthons. Ketoreductases have been used as catalysts for enantioselective conversion of desired prochiral ketones to their corresponding alcohol. We chose reported promiscuous ketoreductases belonging to different protein families and expressed them in E. coli to evaluate their ability as whole-cell catalysts for obtaining chiral alcohol intermediates of pharmaceutical importance. Apart from establishing a method to produce high value (S)-specific alcohols that have not been evaluated before, we propose an in silico analysis procedure to predict product chirality.

Results

Six enzymes originating from Sulfolobus sulfotaricus, Zygosaccharomyces rouxii, Hansenula polymorpha, Corynebacterium sp. ST-10, Synechococcus sp. PCC 7942 and Bacillus sp. ECU0013 with reported efficient activity for dissimilar substrates are compared here to arrive at an optimal enzyme for the method. Whole–cell catalysis of ketone intermediates for drugs like Aprepitant, Sitagliptin and Dolastatin using E. coli over-expressing these enzymes yielded (S)-specific chiral alcohols. We explain this chiral specificity for the best-performing enzyme, i.e., Z. rouxii ketoreductase using in silico modelling and MD simulations. This rationale was applied to five additional ketones that are used in the synthesis of Crizotinib, MA-20565 (an antifungal agent), Sulopenem, Rivastigmine, Talampanel and Barnidipine and predicted the yield of (S) enantiomers. Experimental evaluation matched the in silico analysis wherein ~ 95% (S)-specific alcohol with a chemical yield of 23–79% was obtained through biotransformation. Further, the cofactor re-cycling was optimized by switching the carbon source from glucose to sorbitol that improved the chemical yield to 85–99%.

Conclusions

Here, we present a strategy to synthesize pharmaceutically relevant chiral alcohols by ketoreductases using a cofactor balanced whole-cell catalysis scheme that is useful for the industry. Based on the results obtained in these trials, Zygosaccharomyces rouxii ketoreductase was identified as a proficient enzyme to obtain (S)-specific alcohols from their respective ketones. The whole–cell catalyst when combined with nutrient modulation of using sorbitol as a carbon source helped obtain high enantiomeric and chemical yield.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-1036-2) contains supplementary material, which is available to authorized users.

Novel form of the Michaelis-Menten equation that enables accurate estimation of (kcat/KM)*KI with just two rate measurements; utility in directed evolution

One of applications of directed evolution is to desensitize an enzyme to an inhibitor. kcat,1/KM and KI are three dimensions that when multiplied measure an enzyme's intrinsic capacity for catalysis in the presence of an inhibitor. The ideal values for the individual dimensions depend on substrate and inhibitor concentrations under the conditions of the application. When attempting to optimize those values by directed evolution, (kcat/KM)*KI can be an informative parameter for evaluating libraries of variants, but throughput is limited. We describe a manipulation of the Michaelis-Menten equation for competitive inhibition that isolates (kcat/KM)*KI on one side of the equation. If velocity is measured at constant enzyme and substrate concentrations with two different inhibitor concentrations (one of which can be 0), the data are sufficient to calculate (kcat/KM)*KI with just two rate measurements. The procedure is validated by correlating values obtained by the rapid method with those obtained by substrate saturation kinetics.

Exploring experimental fitness landscapes for chemical synthesis and property optimization

The topology of experimental fitness landscapes for chemical optimization objectives is assessed through svr-based HDMR modeling.

E Pluribus Unum: 50 Years of Research, Millions of Viruses, and One Goal--Tailored Acceleration of AAV Evolution

Fifty years ago, a Science paper by Atchison et al. reported a newly discovered virus that would soon become known as adeno-associated virus (AAV) and that would subsequently emerge as one of the most versatile and most auspicious vectors for human gene therapy. A large part of its attraction stems from the ease with which the viral capsid can be engineered for particle retargeting to cell types of choice, evasion from neutralizing antibodies or other desirable properties. Particularly powerful and in the focus of the current review are high-throughput methods aimed at expanding the repertoire of AAV vectors by means of directed molecular evolution, such as random mutagenesis, DNA family shuffling, in silico reconstruction of ancestral capsids, or peptide display. Here, unlike the wealth of prior reviews on this topic, we especially emphasize and critically discuss the practical aspects of the different procedures that affect the ultimate outcome, including diversification protocols, combinatorial library complexity, and selection strategies. Our overall aim is to provide general guidance that should help users at any level, from novice to expert, to safely navigate through the rugged space of directed AAV evolution while avoiding the pitfalls that are associated with these challenging but promising technologies.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Exploring the enantioselective mechanism of halohydrin dehalogenase from Agrobacterium radiobacter AD1 by iterative saturation mutagenesis

Halohydrin dehalogenase from Agrobacterium radiobacter AD1 (HheC) shows great potential in producing valuable chiral epoxides and β-substituted alcohols. The wild-type (WT) enzyme displays a high R-enantiopreference toward most aromatic substrates, whereas no S-selective HheC has been reported to date. To obtain more enantioselective enzymes, seven noncatalytic active-site residues were subjected to iterative saturation mutagenesis (ISM). After two rounds of screening aspects of both activity and enantioselectivity (E), three outstanding mutants (Thr134Val/Leu142Met, Leu142Phe/Asn176His, and Pro84Val/Phe86Pro/Thr134Ala/Asn176Ala mutants) with divergent enantioselectivity were obtained. The two double mutants displayed approximately 2-fold improvement in R-enantioselectivity toward 2-chloro-1-phenylethanol (2-CPE) without a significant loss of enzyme activity compared with the WT enzyme. Strikingly, the Pro84Val/Phe86Pro/Thr134Ala/Asn176Ala mutant showed an inverted enantioselectivity (from an ER of 65 [WT] to an ES of 101) and approximately 100-fold-enhanced catalytic efficiency toward (S)-2-CPE. Molecular dynamic simulation and docking analysis revealed that the phenyl side chain of (S)-2-CPE bound at a different location than that of its R-counterpart; those mutations generated extra connections for the binding of the favored enantiomer, while the eliminated connections reduced binding of the nonfavored enantiomer, all of which could contribute to the observed inverted enantiopreference.

SpeedyGenes: an improved gene synthesis method for the efficient production of error-corrected, synthetic protein libraries for directed evolution

The de novo synthesis of genes is becoming increasingly common in synthetic biology studies. However, the inherent error rate (introduced by errors incurred during oligonucleotide synthesis) limits its use in synthesising protein libraries to only short genes. Here we introduce SpeedyGenes, a PCR-based method for the synthesis of diverse protein libraries that includes an error-correction procedure, enabling the efficient synthesis of large genes for use directly in functional screening. First, we demonstrate an accurate gene synthesis method by synthesising and directly screening (without pre-selection) a 747 bp gene for green fluorescent protein (yielding 85% fluorescent colonies) and a larger 1518 bp gene (a monoamine oxidase, producing 76% colonies with full catalytic activity, a 4-fold improvement over previous methods). Secondly, we show that SpeedyGenes can accommodate multiple and combinatorial variant sequences while maintaining efficient enzymatic error correction, which is particularly crucial for larger genes. In its first application for directed evolution, we demonstrate the use of SpeedyGenes in the synthesis and screening of large libraries of MAO-N variants. Using this method, libraries are synthesised, transformed and screened within 3 days. Importantly, as each mutation we introduce is controlled by the oligonucleotide sequence, SpeedyGenes enables the synthesis of large, diverse, yet controlled variant sequences for the purposes of directed evolution.

MDC-Analyzer: a novel degenerate primer design tool for the construction of intelligent mutagenesis libraries with contiguous sites

Recent computational and bioinformatics advances have enabled the efficient creation of novel biocatalysts by reducing amino acid variability at hot spot regions. To further expand the utility of this strategy, we present here a tool called Multi-site Degenerate Codon Analyzer (MDC-Analyzer) for the automated design of intelligent mutagenesis libraries that can completely cover user-defined randomized sequences, especially when multiple contiguous and/or adjacent sites are targeted. By initially defining an objective function, the possible optimal degenerate PCR primer profiles could be automatically explored using the heuristic approach of Greedy Best-First-Search. Compared to the previously developed DC-Analyzer, MDC-Analyzer allows for the existence of a small amount of undesired sequences as a tradeoff between the number of degenerate primers and the encoded library size while still providing all the benefits of DC-Analyzer with the ability to randomize multiple contiguous sites. MDC-Analyzer was validated using a series of randomly generated mutation schemes and experimental case studies on the evolution of halohydrin dehalogenase, which proved that the MDC methodology is more efficient than other methods and is particularly well-suited to exploring the sequence space of proteins using data-driven protein engineering strategies.

Computational tools for designing smart libraries

Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such "smart" libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.

Design and development of reactive injectable and settable polymeric biomaterials

Injectable and settable biomaterials are a growing class of therapeutic technologies within the field of regenerative medicine. These materials offer advantages compared to prefabricated implants because of their ability to be utilized as part of noninvasive surgical procedures, fill complex defect shapes, cure in situ, and incorporate cells and other active biologics. However, there are significant technical barriers to clinical translation of injectable and settable biomaterials, such as achieving clinically relevant handling properties and benign reaction conditions. This review focuses on the engineering challenges associated with the design and development of injectable and chemically settable polymeric biomaterials. Additionally, specific examples of the diverse chemistries utilized to overcome these challenges are covered. The future translation of injectable and settable biomaterials is anticipated to improve patient outcomes for a number of clinical conditions.

Integrating the protein and metabolic engineering toolkits for next-generation chemical biosynthesis

Through microbial engineering, biosynthesis has the potential to produce thousands of chemicals used in everyday life. Metabolic engineering and synthetic biology are fields driven by the manipulation of genes, genetic regulatory systems, and enzymatic pathways for developing highly productive microbial strains. Fundamentally, it is the biochemical characteristics of the enzymes themselves that dictate flux through a biosynthetic pathway toward the product of interest. As metabolic engineers target sophisticated secondary metabolites, there has been little recognition of the reduced catalytic activity and increased substrate/product promiscuity of the corresponding enzymes compared to those of central metabolism. Thus, fine-tuning these enzymatic characteristics through protein engineering is paramount for developing high-productivity microbial strains for secondary metabolites. Here, we describe the importance of protein engineering for advancing metabolic engineering of secondary metabolism pathways. This pathway integrated enzyme optimization can enhance the collective toolkit of microbial engineering to shape the future of chemical manufacturing.

Computational protein engineering: bridging the gap between rational design and laboratory evolution

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.

Construction of "small-intelligent" focused mutagenesis libraries using well-designed combinatorial degenerate primers

Site-saturation mutagenesis is a powerful tool for protein optimization due to its efficiency and simplicity. A degenerate codon NNN or NNS (K) is often used to encode the 20 standard amino acids, but this will produce redundant codons and cause uneven distribution of amino acids in the constructed library. Here we present a novel "small-intelligent" strategy to construct mutagenesis libraries that have a minimal gene library size without inherent amino acid biases, stop codons, or rare codons of Escherichia coli by coupling well-designed combinatorial degenerate primers with suitable PCR-based mutagenesis methods. The designed primer mixture contains exactly one codon per amino acid and thus allows the construction of small-intelligent mutagenesis libraries with one gene per protein. In addition, the software tool DC-Analyzer was developed to assist in primer design according to the user-defined randomization scheme for library construction. This small-intelligent strategy was successfully applied to the randomization of halohydrin dehalogenases with one or two randomized sites. With the help of DC-Analyzer, the strategy was proven to be as simple as NNS randomization and could serve as a general tool to efficiently randomize target genes at positions of interest.

Metabolic engineering for the production of natural products

Natural products and their derivatives play an important role in modern healthcare as frontline treatments for many diseases and as inspiration for chemically synthesized therapeutics. With advances in sequencing and recombinant DNA technology, many of the biosynthetic pathways responsible for the production of these chemically complex yet valuable compounds have been elucidated. With an ever-expanding toolkit of biosynthetic components, metabolic engineering is an increasingly powerful method to improve natural product titers and generate novel compounds. Heterologous production platforms have enabled access to pathways from difficult to culture strains, systems biology and metabolic modeling tools have resulted in increasing predictive and analytic capabilities, advances in expression systems and regulation have enabled the fine-tuning of pathways for increased efficiency, and characterization of individual pathway components has facilitated the construction of hybrid pathways for the production of new compounds. These advances in the many aspects of metabolic engineering not only have yielded fascinating scientific discoveries but also make it an increasingly viable approach for the optimization of natural product biosynthesis.

Enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm

Directed evolution is a broadly successful strategy for protein engineering in the quest to enhance the stereoselectivity, activity, and thermostability of enzymes. To increase the efficiency of directed evolution based on iterative saturation mutagenesis, the adaptive substituent reordering algorithm (ASRA) is introduced here as an alternative to traditional quantitative structure-activity relationship (QSAR) methods for identifying potential protein mutants with desired properties from minimal sampling of focused libraries. The operation of ASRA depends on identifying the underlying regularity of the protein property landscape, allowing it to make predictions without explicit knowledge of the structure-property relationships. In a proof-of-principle study, ASRA identified all or most of the best enantioselective mutants among the synthesized epoxide hydrolase from Aspergillus niger, in the absence of peptide seeds with high E-values. ASRA even revealed a laboratory error from irregularities of the reordered E-value landscape alone.

Complex recombination patterns arising during geminivirus coinfections preserve and demarcate biologically important intra-genome interaction networks

Genetic recombination is an important process during the evolution of many virus species and occurs particularly frequently amongst begomoviruses in the single stranded DNA virus family, Geminiviridae. As in many other recombining viruses it is apparent that non-random recombination breakpoint distributions observable within begomovirus genomes sampled from nature are the product of variations both in basal recombination rates across genomes and in the over-all viability of different recombinant genomes. Whereas factors influencing basal recombination rates might include local degrees of sequence similarity between recombining genomes, nucleic acid secondary structures and genomic sensitivity to nuclease attack or breakage, the viability of recombinant genomes could be influenced by the degree to which their co-evolved protein-protein and protein-nucleotide and nucleotide-nucleotide interactions are disreputable by recombination. Here we investigate patterns of recombination that occur over 120 day long experimental infections of tomato plants with the begomoviruses Tomato yellow leaf curl virus and Tomato leaf curl Comoros virus. We show that patterns of sequence exchange between these viruses can be extraordinarily complex and present clear evidence that factors such as local degrees of sequence similarity but not genomic secondary structure strongly influence where recombination breakpoints occur. It is also apparent from our experiment that over-all patterns of recombination are strongly influenced by selection against individual recombinants displaying disrupted intra-genomic interactions such as those required for proper protein and nucleic acid folding. Crucially, we find that selection favoring the preservation of co-evolved longer-range protein-protein and protein DNA interactions is so strong that its imprint can even be used to identify the exact sequence tracts involved in these interactions.

Genetic recombination between viruses is a form of parasexual reproduction during which two parental viruses each contribute genetic information to an offspring, or recombinant, virus. Unlike with sexual reproduction, however, recombination in viruses can even involve the transfer of sequences between the members of distantly related species. When parental genomes are very distantly related, it is anticipated that recombination between them runs the risk of producing defective offspring. The reason for this is that the interactions between different parts of genomes and the proteins they encode (such as between different viral proteins or between viral proteins and the virus genomic DNA or RNA) often depend on particular co-evolved binding sites that recognize one another. When in a recombinant genome the partners in a binding site pair are each inherited from different parents there is a possibility that they will not interact with one another properly. Here we examine recombinant genomes arising during experimental mixed infections of two distantly related viruses to detect evidence that intra-genome interaction networks are broadly preserved in these genomes. We show this preservation is so strict that patterns of recombination in these viruses can even be used to identify the interacting regions within their genomes.

Protein design in metabolic engineering and synthetic biology

Starting from experimental data on sequence, structure or biochemical properties of enzymes, protein design seeks to construct enzymes with desired activity, stability, specificity and selectivity. Two strategies are widely used to investigate sequence-structure-function relationships: statistical methods to analyse protein families or mutant libraries, and molecular modelling methods to study proteins and their interaction with ligands or substrates. On the basis of these methods, protein design has been successfully applied to fine-tune bottleneck enzymes in metabolic engineering and to design enzymes with new substrate spectra and new functions. However, constructing efficient metabolic pathways by integrating individual enzymes into a complex system is challenging. The field of synthetic biology is still in its infancy, but promising results have demonstrated the feasibility and usefulness of the concept.

Why is chemical synthesis and property optimization easier than expected?

Identifying optimal conditions for chemical and material synthesis as well as optimizing the properties of the products is often much easier than simple reasoning would predict. The potential search space is infinite in principle and enormous in practice, yet optimal molecules, materials, and synthesis conditions for many objectives can often be found by performing a reasonable number of distinct experiments. Considering the goal of chemical synthesis or property identification as optimal control problems provides insight into this good fortune. Both of these goals may be described by a fitness function J that depends on a suitable set of variables (e.g., reactant concentrations, components of a material, processing conditions, etc.). The relationship between J and the variables specifies the fitness landscape for the target objective. Upon making simple physical assumptions, this work demonstrates that the fitness landscape for chemical optimization contains no local sub-optimal maxima that may hinder attainment of the absolute best value of J. This feature provides a basis to explain the many reported efficient optimizations of synthesis conditions and molecular or material properties. We refer to this development as OptiChem theory. The predicted characteristics of chemical fitness landscapes are assessed through a broad examination of the recent literature, which shows ample evidence of trap-free landscapes for many objectives. The fundamental and practical implications of OptiChem theory for chemistry are discussed.

Engineering enzyme specificity using computational design of a defined-sequence library

Engineered biosynthetic pathways have the potential to produce high-value molecules from inexpensive feedstocks, but a key limitation is engineering enzymes with high activity and specificity for new reactions. Here, we developed a method for combining structure-based computational protein design with library-based enzyme screening, in which inter-residue correlations favored by the design are encoded into a defined-sequence library. We validated this approach by engineering a glucose 6-oxidase enzyme for use in a proposed pathway to convert D-glucose into D-glucaric acid. The most active variant, identified after only one round of diversification and screening of only 10,000 wells, is approximately 400-fold more active on glucose than is the wild-type enzyme. We anticipate that this strategy will be broadly applicable to the discovery of new enzymes for engineered biological pathways.

Iterative saturation mutagenesis accelerates laboratory evolution of enzyme stereoselectivity: rigorous comparison with traditional methods

Efficacy in laboratory evolution of enzymes is currently a pressing issue, making comparative studies of different methods and strategies mandatory. Recent reports indicate that iterative saturation mutagenesis (ISM) provides a means to accelerate directed evolution of stereoselectivity and thermostability, but statistically meaningful comparisons with other methods have not been documented to date. In the present study, the efficacy of ISM has been rigorously tested by applying it to the previously most systematically studied enzyme in directed evolution, the lipase from Pseudomonas aeruginosa as a catalyst in the stereoselective hydrolytic kinetic resolution of a chiral ester. Upon screening only 10,000 transformants, unprecedented enantioselectivity was achieved (E = 594). ISM proves to be considerably more efficient than all previous systematic efforts utilizing error-prone polymerase chain reaction at different mutation rates, saturation mutagenesis at hot spots, and/or DNA shuffling, pronounced positive epistatic effects being the underlying reason.

Improving biocatalyst performance by integrating statistical methods into protein engineering

Directed evolution and rational design were used to generate active variants of toluene-4-monooxygenase (T4MO) on 2-phenylethanol (PEA), with the aim of producing hydroxytyrosol, a potent antioxidant. Due to the complexity of the enzymatic system-four proteins encoded by six genes-mutagenesis is labor-intensive and time-consuming. Therefore, the statistical model of Nov and Wein (J. Comput. Biol. 12:247-282) was used to reduce the number of variants produced and evaluated in a lab. From an initial data set of 24 variants, with mutations at nine positions, seven double or triple mutants were identified through statistical analysis. The average activity of these mutants was 4.6-fold higher than the average activity of the initial data set. In an attempt to further improve the enzyme activity to obtain PEA hydroxylation, a second round of statistical analysis was performed. Nine variants were considered, with 3, 4, and 5 point mutations. The average activity of the variants obtained in the second statistical round was 1.6-fold higher than in the first round and 7.3-fold higher than that of the initial data set. The best variant discovered, TmoA I100A E214G D285Q, exhibited an initial oxidation rate of 4.4 ± 0.3 nmol/min/mg protein, which is 190-fold higher than the rate obtained by the wild type. This rate was also 2.6-fold higher than the activity of the wild type on the natural substrate toluene. By considering only 16 preselected mutants (out of ∼13,000 possible combinations), a highly active variant was discovered with minimum time and effort.

Enzyme-catalysis breathes new life into polyester condensation polymerizations

Traditional chemical catalysts for polyester synthesis have enabled the generation of important commercial products. Undesirable characteristics of chemically catalyzed condensation polymerizations include the need to conduct reactions at high temperatures (150-280 degrees C) with metal catalysts that are toxic and lack selectivity. The latter is limiting when aspiring towards synthesis of increasingly complex and well-defined polyesters. This review describes an exciting technology that makes use of immobilized enzyme-catalysts for condensation polyester synthesis. Unlike chemical catalysts, enzymes function under mild conditions (< or =100 degrees C), which enables structure retention when polymerizing unstable monomers, circumvents the introduction of metals, and also provides selectivity that avoids protection-deprotection steps and presents unique options for structural control. Examples are provided that describe the progress made in enzyme-catalyzed polymerizations, as well as current limitations and future prospects for developing more efficient enzyme-catalysts for industrial processes.