Evolution Of Enzymes Research Articles

Commonly used surfactants sodium dodecyl sulphate, cetylpyridinium chloride and sodium laureth sulphate and their effects on antioxidant defence system and oxidative stress indices in Cyprinus carpio L.: an integrated in silico and in vivo approach.

The present study evaluated the homology modelling, in silico prediction and characterization of Cyprinus carpio cytochrome P450, as well as molecular docking experiments between the modelled protein and the surfactants sodium dodecyl sulphate (SDS), sodium laureth sulphate (SLES) and cetylpyridinium chloride (CPC). Homology modelling of cytochrome P450 was performed using the best fit template structure. The structure was optimized with 3D refine, and the ultimate 3D structure was checked with PROCHEK and ERRATA. ExPASy's ProtParam was likewise used to analyse the modelled protein's physiochemical and stereochemical attributes. To establish the binding pattern of each ligand to the targeted protein and its effect on the overall protein conformation, molecular docking calculations and protein-ligand interactions were performed. Our in silico analysis revealed that hydrophobic interactions with the active site amino acid residues of cytochrome p450 were more prevalent than hydrogen bonds and salt bridges. The in vivo analysis exhibited that exposure of fish to sublethal concentrations (10% and 30% of 96h LC50) of SDS (0.34 and 1.02mg/l), CPC (0.002 and 0.006mg/l) and SLES (0.69 and 2.07mg/l) at 15d, 30d and 45d adversely affected the oxidative stress and antioxidant enzymes (CAT, SOD, GST, GPx and MDA) in the liver of Cyprinus carpio. As a result, the study suggests that elicited oxidative stress, prompted by the induction of antioxidant enzymes activity, could be attributable to the stable binding of cytochrome P450 with SDS, CPC and SLES which ultimately leads to the evolution of antioxidant enzymes for its neutralization.

Use of directed enzyme evolution to create novel biosynthetic pathways for production of rare or non-natural carotenoids.

In recent years, advances in bioengineering and synthetic biology techniques have been used to create carotenoid diversity in the laboratory. In this chapter, we describe the step-by-step method to perform directed evolution of carotenoid biosynthetic enzymes. We first explain how to establish an efficient Escherichia coli colony-based screening, including a detailed description of plasmid DNA construction design as well as tips and tricks to handle and manipulate cells to produce stable colonies. As an example for the directed evolution experiment, we engineer a bacterial phytoene desaturase CrtI to obtain a C50-phytoene desaturase, which catalyzes formation of a non-natural long-chain carotenoid. The method described in this chapter can be applied to many carotenoid biosynthetic enzymes, whose numbers have been rapidly expanding with recent advances in genomics. The use of directed evolution for carotenoid enzymes will contribute not only to the discovery of novel carotenoids but also to a deeper understanding of the creation and evolution of carotenoid biosynthetic pathways in nature.

Directed Evolution of the Fusion Enzyme for Improving Astaxanthin Biosynthesis in Saccharomyces Cerevisiae

Directed Evolution of the Fusion Enzyme for Improving Astaxanthin Biosynthesis in Saccharomyces Cerevisiae

Recombination of Single Beneficial Substitutions Obtained from Protein Engineering by Computer-Assisted Recombination (CompassR).

A large number of beneficial substitutions can be obtained from a successful directed enzyme evolution campaign and/or (semi)rational design. It is expected that the recombination of some beneficial substitutions leads to a much higher degree of performance through synergistic effect. However, systematic recombination studies show that poorly performing variants are often obtained after recombination of three to four individual beneficial substitutions and this limits protein engineers to exploit nature's potential in generating better performing enzymes. Computer-assisted Recombination (CompassR) strategy allows the recombination of identified beneficial substitutions in an effective and efficient manner in order to generate active enzymes with improved performance. Here, we describe in detail the CompassR procedure with an example of recombining four substitutions and discuss some important practical issues that should be considered (such as the selection of protein structures, number of FoldX runs, evaluation of calculations) for application of the CompassR rule. The core part of this protocol (system setup, ΔΔGfold calculation, and CompassR application) is transferable to other enzymes and any recombination of single beneficial substitutions.

Enzyme ancestral sequence reconstruction and directed evolution

The amino acid sequence of ancestral enzymes from extinct organisms can be deduced through in silico approach termed ancestral sequence reconstruction (ASR). ASR usually has six steps, which are the collection of nucleic acid/amino acid sequences of modern enzymes, multiple sequence alignment, phylogenetic tree construction, computational deduction of ancestral enzyme sequence, gene cloning, and characterization of enzyme properties. This method is widely used to study the adaptation and evolution mechanism of molecules to the changing environmental conditions on planetary time scale. As enzymes play key roles in biocatalysis, this method has become a powerful method for studying the relationship among the sequence, structure, and function of enzymes. Notably, most of the ancestral enzymes show better temperature stability and mutation stability, making them ideal protein scaffolds for further directed evolution. This article summarizes the computer algorithms, applications, and commonly used computer software of ASR, and discusses the potential application in directed evolution of enzymes.

Using the Evolutionary History of Proteins to Engineer Insertion-Deletion Mutants from Robust, Ancestral Templates Using Graphical Representation of Ancestral Sequence Predictions (GRASP).

Analyzing the natural evolution of proteins by ancestral sequence reconstruction (ASR) can provide valuable information about the changes in sequence and structure that drive the development of novel protein functions. However, ASR has also been used as a protein engineering tool, as it often generates thermostable proteins which can serve as robust and evolvable templates for enzyme engineering. Importantly, ASR has the potential to provide an insight into the history of insertions and deletions that have occurred in the evolution of a protein family. Indels are strongly associated with functional change during enzyme evolution and represent a largely unexplored source of genetic diversity for designing proteins with novel or improved properties. Current ASR methods differ in the way they handle indels; inclusion or exclusion of indels is often managed subjectively, based on assumptions the user makes about the likelihood of each recombination event, yet most currently available ASR tools provide limited, if any, opportunities for evaluating indel placement in a reconstructed sequence. Graphical Representation of Ancestral Sequence Predictions (GRASP) is an ASR tool that maps indel evolution throughout a reconstruction and enables the evaluation of indel variants. This chapter provides a general protocol for performing a reconstruction using GRASP and using the results to create indel variants. The method addresses protein template selection, sequence curation, alignment refinement, tree building, ancestor reconstruction, evaluation of indel variants and approaches to library development.

Biocatalytic Alkylation Chemistry: Building Molecular Complexity with High Selectivity.

Biocatalysis has traditionally been viewed as a field that primarily enables access to chiral centers. This includes the synthesis of chiral alcohols, amines and carbonyl compounds, often through functional group interconversion via hydrolytic or oxidation-reduction reactions. This limitation is partly being overcome by the design and evolution of new enzymes. Here, we provide an overview of a recently thriving research field that we summarize as biocatalytic alkylation chemistry. In the past 3-4 years, numerous new enzymes have been developed that catalyze sp3 C-C/N/O/S bond formations. These enzymes utilize different mechanisms to generate molecular complexity by coupling simple fragments with high activity and selectivity. In many cases, the engineered enzymes perform reactions that are difficult or impossible to achieve with current small-molecule catalysts such as organocatalysts and transition-metal complexes. This review further highlights that the design of new enzyme function is particularly successful when off-the-shelf synthetic reagents are utilized to access non-natural reactive intermediates. This underscores how biocatalysis is gradually moving to a field that build molecules through selective bond forming reactions.

In Silico Prediction Methods for Site-Saturation Mutagenesis.

Directed enzyme evolution has proven to be a powerful means to endow biocatalysts with novel catalytic repertoires. Apart from completely random gene mutagenesis, site-directed or site-saturation mutagenesis requires a semi-rational selection of the amino acid positions or the substituted residues, which can dramatically reduce the screening efforts in protein engineering. To this end, in silico prediction methods play a pivotal role in targeting site-saturation mutagenesis. In this chapter, we provide two distinct computational methods, (a) conformational dynamics-guided design and (b) protein-ligand interaction fingerprinting analysis, to identify specific positions for site-saturation mutagenesis toward manipulating substrate specificity/stereoselectivity of an alcohol dehydrogenase, and improving activity of a carboxylic acid reductase, respectively.

Machine-Learning-Guided Library Design Cycle for Directed Evolution of Enzymes: The Effects of Training Data Composition on Sequence Space Exploration

Machine learning (ML) is becoming an attractive tool in mutagenesis-based protein engineering because of its ability to design a variant library containing proteins with a desired function. However, it remains unclear how ML guides directed evolution in sequence space depending on the composition of training data. Here, we present a ML-guided directed evolution study of an enzyme to investigate the effects of a known “highly positive” variant (i.e., variant known to have high enzyme activity) in training data. We performed two separate series of ML-guided directed evolution of Sortase A with and without a known highly positive variant called 5M in training data. In each series, two rounds of ML were conducted: variants predicted by the initial round were experimentally evaluated and used as additional training data for the second-round of prediction. The improvements in enzyme activity were comparable between the two series, both achieving enzyme activity 2.2–2.5 times higher than 5M. Intriguingly, the sequences of the improved variants were largely different between the two series, indicating that ML guided the directed evolution to the distinct regions of sequence space depending on the presence/absence of the highly positive variant in the training data. This suggests that the sequence diversity of improved variants can be expanded not only by conventional ML using the whole training data but also by ML using a subset of the training data even when it lacks highly positive variants. In summary, this study demonstrates the importance of regulating the composition of training data in ML-guided directed evolution.

Using continuous directed evolution to improve enzymes for plant applications.

Continuous directed evolution of enzymes and other proteins in microbial hosts is capable of outperforming classical directed evolution by executing hypermutation and selection concurrently in vivo, at scale, with minimal manual input. Provided that a target enzyme’s activity can be coupled to growth of the host cells, the activity can be improved simply by selecting for growth. Like all directed evolution, the continuous version requires no prior mechanistic knowledge of the target. Continuous directed evolution is thus a powerful way to modify plant or non-plant enzymes for use in plant metabolic research and engineering. Here, we first describe the basic features of the yeast (Saccharomyces cerevisiae) OrthoRep system for continuous directed evolution and compare it briefly with other systems. We then give a step-by-step account of three ways in which OrthoRep can be deployed to evolve primary metabolic enzymes, using a THI4 thiazole synthase as an example and illustrating the mutational outcomes obtained. We close by outlining applications of OrthoRep that serve growing demands (i) to change the characteristics of plant enzymes destined for return to plants, and (ii) to adapt (“plantize”) enzymes from prokaryotes—especially exotic prokaryotes—to function well in mild, plant-like conditions.

The Birth of Genomic Enzymology: Discovery of the Mechanistically Diverse Enolase Superfamily.

Enzymes are categorized into superfamilies by sequence, structural, and mechanistic similarities. The evolutionary implications can be profound. Until the mid-1990s, the approach was fragmented largely due to limited sequence and structural data. However, in 1996, Babbitt et al. published a paper in Biochemistry that demonstrated the potential power of mechanistically diverse superfamilies to identify common ancestry, predict function, and, in some cases, predict specificity. This Perspective describes the findings of the original work and reviews the current understanding of structure and mechanism in the founding family members. The outcomes of the genomic enzymology approach have reached far beyond the functional assignment of members of the enolase superfamily, inspiring the study of superfamilies and the adoption of sequence similarity networks and genome context and yielding fundamental insights into enzyme evolution.

Reconstruction of evolving gene variants and fitness from short sequencing reads.

Directed evolution can generate proteins with tailor-made activities. However, full-length genotypes, their frequencies, and fitnesses are difficult to measure for evolving gene-length biomolecules using most high-throughput DNA sequencing methods as short read lengths can lose mutation linkages in haplotypes. We present Evoracle, a machine learning method that accurately reconstructs full-length genotypes (R2 = 0.94) and fitness using short-read data from directed evolution experiments, with substantial improvements over related methods. We validate Evoracle on phage-assisted continuous evolution (PACE), phage-assisted non-continuous evolution (PANCE) of adenine base editors, and OrthoRep evolution of drug-resistant enzymes. Evoracle retains strong performance (R2 = 0.86) on data with complete linkage loss between neighboring nucleotides and large measurement noise such as pooled Sanger sequencing data (~$10/timepoint), and broadens the accessibility of training machine learning models on gene variant fitnesses. Evoracle can also identify high-fitness variants, including low-frequency ‘rising stars’, well before they are identifiable from consensus mutations.

Enhanced Production of Pterostilbene in Escherichia coli Through Directed Evolution and Host Strain Engineering.

Pterostilbene is a derivative of resveratrol with a higher bioavailability and biological activity, which shows antioxidant, anti-inflammatory, antitumor, and antiaging activities. Here, directed evolution and host strain engineering were used to improve the production of pterostilbene in Escherichia coli. First, the heterologous biosynthetic pathway enzymes of pterostilbene, including tyrosine ammonia lyase, p-coumarate: CoA ligase, stilbene synthase, and resveratrol O-methyltransferase, were successively directly evolved through error-prone polymerase chain reaction (PCR). Four mutant enzymes with higher activities of in vivo and in vitro were obtained. The directed evolution of the pathway enzymes increased the pterostilbene production by 13.7-fold. Then, a biosensor-guided genome shuffling strategy was used to improve the availability of the precursor L-tyrosine of the host strain E. coli TYR-30 used for the production of pterostilbene. A shuffled E. coli strain with higher L-tyrosine production was obtained. The shuffled strain harboring the evolved pathway produced 80.04 ± 5.58 mg/l pterostilbene, which is about 2.3-fold the highest titer reported in literatures.

The Macroevolutionary Consequences of Niche Construction in Microbial Metabolism.

Microorganisms display a stunning metabolic diversity. Understanding the origin of this diversity requires understanding how macroevolutionary processes such as innovation and diversification play out in the microbial world. Metabolic networks, which govern microbial resource use, can evolve through different mechanisms, e.g., horizontal gene transfer or de novo evolution of enzymes and pathways. This process is governed by a combination of environmental factors, selective pressures, and the constraints imposed by the genetic architecture of metabolic networks. In addition, many independent results hint that the process of niche construction, by which organisms actively modify their own and each other’s niches and selective pressures, could play a major role in microbial innovation and diversification. Yet, the general principles by which niche construction shapes microbial macroevolutionary patterns remain largely unexplored. Here, we discuss several new hypotheses and directions, and suggest metabolic modeling methods that could allow us to explore large-scale empirical genotype-phenotype-(G-P)-environment spaces in order to study the macroevolutionary effects of niche construction. We hope that this short piece will further stimulate a systematic and quantitative characterization of macroevolutionary patterns and processes in microbial metabolism.

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

Amplicon Remodeling and Genomic Mutations Drive Population Dynamics after Segmental Amplification.

New enzymes often evolve by duplication and divergence of genes encoding enzymes with promiscuous activities that have become important in the face of environmental opportunities or challenges. Amplifications that increase the copy number of the gene under selection commonly amplify many surrounding genes. Extra copies of these coamplified genes must be removed, either during or after evolution of a new enzyme. Here we report that amplicon remodeling can begin even before mutations occur in the gene under selection. Amplicon remodeling and mutations elsewhere in the genome that indirectly increase fitness result in complex population dynamics, leading to emergence of clones that have improved fitness by different mechanisms. In this work, one of the two most successful clones had undergone two episodes of amplicon remodeling, leaving only four coamplified genes surrounding the gene under selection. Amplicon remodeling in the other clone resulted in removal of 111 genes from the genome, an acceptable solution under these selection conditions, but one that would certainly impair fitness under other environmental conditions.

Quantitation and speciation of residual protein within active pharmaceutical ingredients using image analysis with SDS-PAGE

Recent advances in biocatalysis and directed enzyme evolution has led to a variety of enzymatically-driven, elegant processes for active pharmaceutical ingredient (API) production. For biocatalytic processes, quantitation of any residual protein within a given API is of great importance to ensure process robustness and quality, pure pharmaceutical products. Typical analytical methods for analyzing residual enzymes within an API, such as enzyme-linked immunosorbent assays (ELISA), colorimetric assays, and liquid chromatographic techniques, are limited for determining only the concentration of known proteins and require harsh solvents with high API levels for analysis. For the first time, total residual protein content in a small molecule API was quantitated using image analysis applied to SDS-PAGE. Herein, a proposed methodology for residual protein detection, quantitation, and size-based speciation is presented, in which an orthogonal technique is offered to traditional analysis methods, such as ELISA. Results indicate that our application of the analytical methodology is able to reliably quantitate both protein standards and the total residual protein present within a final API, with good agreement as compared to traditional ELISA results. Further, speciation of the residual protein within the API provides key information concerning the individual residual proteins present, including their molecular weight, which can lead to improved process development efforts for residual protein rejection and control. This analytical methodology thus offers an alternative tool for easily identifying, quantitating, and speciating residual protein content in the presence of small molecule APIs, with potential for wide applicability across industry for biocatalytic or directed enzyme evolution efforts within process development.

Exploiting enzyme evolution for computational protein design

Exploiting enzyme evolution for computational protein design

Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis.

Ligand-dependent biosensors are valuable tools for coupling the intracellular concentrations of small molecules to easily detectable readouts such as absorbance, fluorescence, or cell growth. While ligand-dependent biosensors are widely used for monitoring the production of small molecules in engineered cells and for controlling or optimizing biosynthetic pathways, their application to directed evolution for biocatalysts remains underexplored. As a consequence, emerging continuous evolution technologies are rarely applied to biocatalyst evolution. Here, we develop a panel of ligand-dependent biosensors that can detect a range of small molecules. We demonstrate that these biosensors can link enzymatic activity to the production of an essential phage protein to enable biocatalyst-dependent phage-assisted continuous evolution (PACE) and phage-assisted continuous selection (PACS). By combining these phage-based evolution and library selection technologies, we demonstrate that we can evolve enzyme variants with improved and expanded catalytic properties. Finally, we show that the genetic diversity resulting from a highly mutated PACS library is enriched for active enzyme variants with altered substrate scope. These results lay the foundation for using phage-based continuous evolution and selection technologies to engineer biocatalysts with novel substrate scope and reactivity.

Witnessing the structural evolution of an RNA enzyme.

An RNA polymerase ribozyme that has been the subject of extensive directed evolution efforts has attained the ability to synthesize complex functional RNAs, including a full-length copy of its own evolutionary ancestor. During the course of evolution, the catalytic core of the ribozyme has undergone a major structural rearrangement, resulting in a novel tertiary structural element that lies in close proximity to the active site. Through a combination of site-directed mutagenesis, structural probing, and deep sequencing analysis, the trajectory of evolution was seen to involve the progressive stabilization of the new structure, which provides the basis for improved catalytic activity of the ribozyme. Multiple paths to the new structure were explored by the evolving population, converging upon a common solution. Tertiary structural remodeling of RNA is known to occur in nature, as evidenced by the phylogenetic analysis of extant organisms, but this type of structural innovation had not previously been observed in an experimental setting. Despite prior speculation that the catalytic core of the ribozyme had become trapped in a narrow local fitness optimum, the evolving population has broken through to a new fitness locale, raising the possibility that further improvement of polymerase activity may be achievable.