Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

A primer to directed evolution: current methodologies and future directions

This review summarises the methods available for directed evolution, including mutagenesis and variant selection techniques. The advantages and disadvantages of each technique are presented, and future challenges in the field are discussed.

Critical assessment of structure-based approaches to improve protein resistance in aqueous ionic liquids by enzyme-wide saturation mutagenesis

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for green industrial processes involving biocatalysts, but often reduce enzyme activity. Experimental and computational methods are applied to predict favorable substitution sites and, most often, subsequent site-directed surface charge modifications are introduced to enhance enzyme resistance towards aIL. However, almost no studies evaluate the prediction precision with random mutagenesis or the application of simple data-driven filtering processes. Here, we systematically and rigorously evaluated the performance of 22 previously described structure-based approaches to increase enzyme resistance to aIL based on an experimental complete site-saturation mutagenesis library of Bacillus subtilis Lipase A (BsLipA) screened against four aIL. We show that, surprisingly, most of the approaches yield low gain-in-precision (GiP) values, particularly for predicting relevant positions: 14 approaches perform worse than random mutagenesis. Encouragingly, exploiting experimental information on the thermostability of BsLipA or structural weak spots of BsLipA predicted by rigidity theory yields GiP = 3.03 and 2.39 for relevant variants and GiP = 1.61 and 1.41 for relevant positions. Combining five simple-to-compute physicochemical and evolutionary properties substantially increases the precision of predicting relevant variants and positions, yielding GiP = 3.35 and 1.29. Finally, combining these properties with predictions of structural weak spots identified by rigidity theory additionally improves GiP for relevant variants up to 4-fold to ∼10 and sustains or increases GiP for relevant positions, resulting in a prediction precision of ∼90% compared to ∼9% in random mutagenesis. This combination should be applicable to other enzyme systems for guiding protein engineering approaches towards improved aIL resistance.

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, ΔΔG_fold calculation, and CompassR application) is transferable to other enzymes and any recombination of single beneficial substitutions.

PyPEF-An Integrated Framework for Data-Driven Protein Engineering

Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. PyPEF guides the identification and selection of beneficial proteins of a defined sequence space by systematically or randomly exploring the fitness of variants and by sampling random evolution pathways. The performance of PyPEF was evaluated concerning its predictive accuracy and throughput on four public protein and enzyme data sets using common regression models. It was proved that the program could efficiently predict the fitness of protein sequences for different target properties (predictive models with coefficient of determination values ranging from 0.58 to 0.92). By combining machine learning and protein evolution, PyPEF enabled the screening of proteins with various functions, reaching a screening capacity of more than 500,000 protein sequence variants in the timeframe of only a few minutes on a personal computer. PyPEF displayed significant accuracies on four public data sets (different proteins and properties) and underlined the potential of integrating data-driven technologies for covering different philosophies by either predicting the fitness of the variants to the highest accuracy accounting for epistatic effects or capturing the general trend of introduced mutations on the fitness in directed protein evolution campaigns. In essence, PyPEF can provide a powerful solution to current sequence exploration and combinatorial problems faced in protein engineering through exhaustive in silico screening of the sequence space.

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.

Computer-Assisted Recombination (CompassR) Teaches us How to Recombine Beneficial Substitutions from Directed Evolution Campaigns

A main remaining challenge in protein engineering is how to recombine beneficial substitutions. Systematic recombination studies show that poorly performing variants are usually obtained after recombination of 3 to 4 beneficial substitutions. This limits researchers in exploiting nature's potential in generating better enzymes. The Computer-assisted Recombination (CompassR) strategy provides a selection guide for beneficial substitutions that can be recombined to gradually improve enzyme performance by analysis of the relative free energy of folding (ΔΔGfold ). The performance of CompassR was evaluated by analysis of 84 recombinants located on 13 positions of Bacillus subtilis lipase A. The finally obtained variant F17S/V54K/D64N/D91E had a 2.7-fold improved specific activity in 18.3 % (v/v) 1-butyl-3-methylimidazolium chloride ([BMIM][Cl]). In essence, the deducted CompassR rule allows recombination of beneficial substitutions in an iterative manner and empowers researchers to generate better enzymes in a time-efficient manner.

Improvement of the Activity and Stability of Starch-Debranching Pullulanase from Bacillus naganoensis via Tailoring of the Active Sites Lining the Catalytic Pocket

Pullulanases are well-known debranching enzymes that hydrolyze α-1,6-glycosidic linkages in starch and oligosaccharides. However, most of the pullulanases exhibit limited activity for practical applications. Here, two sites (787 and 621) lining the catalytic pocket of Bacillus naganoensis pullulanase were identified as being critical for enzymatic activity by triple-code saturation mutagenesis. Subsequently, both sites were subjected to NNK-based saturation mutagenesis to obtain positive variants. Among the variants showing enhanced activity, the enzymatic activity and specific activity of D787C were 1.5-fold higher than those of the wild-type (WT). D787C also showed a 1.8-fold increase in k_cat and a 1.7-fold increase in k_cat/ K_m. In addition, D787C maintained higher activity compared with that of WT at temperatures over 60 °C. All the positive variants showed higher acid resistance, with D787C maintaining 90% residual activity at pH 4.0. Thus, enzymes with improved properties were obtained by saturation mutagenesis at the active site.

Synthetic pathways and processes for effective production of 5-hydroxytryptophan and serotonin from glucose in Escherichia coli

Background

Tryptophan derivatives such as 5-hydroxytryptophan (5HTP) and serotonin are valuable molecules with pharmaceutical interest. 5HTP is presently mainly obtained by extraction from the plant Griffonia simplicifolia and serotonin is produced by chemical synthesis. A simple biotechnological method for the production of these compounds is desired.

Results

In a first attempt to synthesize serotonin from glucose, we used a single engineered Escherichia coli strain and observed a low production of maximal 0.8 ± 0.2 mg/L of serotonin, probably due to the undesired site-reaction of direct decarboxylation of tryptophan and the consequent decrease of the precursor 5HTP. To circumvent this problem, we have constructed a stepwise system in which the 5HTP production and the serotonin conversion are separated. 962 ± 58 mg/L of 5HTP was produced in the first step using a recombinant strain with a semi-rationally engineered aromatic amino acid hydroxylase, the highest concentration reported so far. In a subsequent step of 5HTP bioconversion using a recombinant strain harboring a tryptophan decarboxylase, 154.3 ± 14.3 mg/L of serotonin was produced.

Conclusions

We present results of a two-stage fermentation process for the production of 5HTP and serotonin. The first strain is a highly efficient 5HTP producer, and after fermentation the supernatant is separated and used for the production of serotonin. This is the first report for the microbial production of serotonin from glucose.

Catalytic mechanism of phenylacetone monooxygenases for non-native linear substrates

Phenylacetone monooxygenase (PAMO) is the most stable and thermo-tolerant member of the Baeyer-Villiger monooxygenase family, and therefore it is an ideal candidate for the synthesis of industrially relevant compounds. However, its limited substrate scope has largely limited its industrial applications. In the present work, we provide, for the first time, the catalytic mechanism of PAMO for the native substrate phenylacetone as well as for a linear non-native substrate 2-octanone, using molecular dynamics simulations, quantum mechanics and quantum mechanics/molecular mechanics calculations. We provide a theoretical basis for the preference of the enzyme for the native aromatic substrate over non-native linear substrates. Our study provides fundamental atomic-level insights that can be employed in the rational engineering of PAMO for wide applications in industrial biocatalysis, in particular, in the biotransformation of long-chain aliphatic oils into potential biodiesels.

Protein Engineering: Past, Present, and Future

The last decade has seen a dramatic increase in the utilization of enzymes as green and sustainable (bio)catalysts in pharmaceutical and industrial applications. This trend has to a significant degree been fueled by advances in scientists' and engineers' ability to customize native enzymes by protein engineering. A review of the literature quickly reveals the tremendous success of this approach; protein engineering has generated enzyme variants with improved catalytic activity, broadened or altered substrate specificity, as well as raised or reversed stereoselectivity. Enzymes have been tailored to retain activity at elevated temperatures and to function in the presence of organic solvents, salts and pH values far from physiological conditions. However, readers unfamiliar with the field will soon encounter the confusingly large number of experimental techniques that have been employed to accomplish these engineering feats. Herein, we use history to guide a brief overview of the major strategies for protein engineering-past, present, and future.

Directed Evolution of Proteins Based on Mutational Scanning

Directed evolution has emerged as one of the most effective protein engineering methods in basic research as well as in applications in synthetic organic chemistry and biotechnology. The successful engineering of protein activity, allostery, binding affinity, expression, folding, fluorescence, solubility, substrate scope, selectivity (enantio-, stereo-, and regioselectivity), and/or stability (temperature, organic solvents, pH) is just limited by the throughput of the genetic selection, display, or screening system that is available for a given protein. Sometimes it is possible to analyze millions of protein variants from combinatorial libraries per day. In other cases, however, only a few hundred variants can be screened in a single day, and thus the creation of smaller yet smarter libraries is needed. Different strategies have been developed to create these libraries. One approach is to perform mutational scanning or to construct "mutability landscapes" in order to understand sequence-function relationships that can guide the actual directed evolution process. Herein we provide a protocol for economically constructing scanning mutagenesis libraries using a cytochrome P450 enzyme in a high-throughput manner. The goal is to engineer activity, regioselectivity, and stereoselectivity in the oxidative hydroxylation of a steroid, a challenging reaction in synthetic organic chemistry. Libraries based on mutability landscapes can be used to engineer any fitness trait of interest. The protocol is also useful for constructing gene libraries for deep mutational scanning experiments.

Fluorescence-Activated Cell Sorting of Human l-asparaginase Mutant Libraries for Detecting Enzyme Variants with Enhanced Activity

Immunogenicity is one of the most common complications occurring during therapy making use of protein drugs of nonhuman origin. A notable example of such a case is bacterial l-asparaginases (L-ASNases) used for the treatment of acute lymphoblastic leukemia (ALL). The replacement of the bacterial enzymes by human ones is thought to set the basis for a major improvement of antileukemic therapy. Recently, we solved the crystal structure of a human enzyme possessing L-ASNase activity, designated hASNase-3. This enzyme is expressed as an inactive precursor protein and post-translationally undergoes intramolecular processing leading to the generation of two subunits which remain noncovalently, yet tightly associated and constitute the catalytically active form of the enzyme. We discovered that this intramolecular processing can be drastically and selectively accelerated by the free amino acid glycine. In the present study, we report on the molecular engineering of hASNase-3 aiming at the improvement of its catalytic properties. We created a fluorescence-activated cell sorting (FACS)-based high-throughput screening system for the characterization of rationally designed mutant libraries, capitalizing on the finding that free glycine promotes autoproteolytic cleavage, which activates the mutant proteins expressed in an E. coli strain devoid of aspartate biosynthesis. Successive screening rounds led to the isolation of catalytically improved variants showing up to 6-fold better catalytic efficiency as compared to the wild-type enzyme. Our work establishes a powerful strategy for further exploitation of the human asparaginase sequence space to facilitate the identification of in vitro-evolved enzyme species that will lay the basis for improved ALL therapy.

Discovery, Molecular Mechanisms, and Industrial Applications of Cold-Active Enzymes

Cold-active enzymes constitute an attractive resource for biotechnological applications. Their high catalytic activity at temperatures below 25°C makes them excellent biocatalysts that eliminate the need of heating processes hampering the quality, sustainability, and cost-effectiveness of industrial production. Here we provide a review of the isolation and characterization of novel cold-active enzymes from microorganisms inhabiting different environments, including a revision of the latest techniques that have been used for accomplishing these paramount tasks. We address the progress made in the overexpression and purification of cold-adapted enzymes, the evolutionary and molecular basis of their high activity at low temperatures and the experimental and computational techniques used for their identification, along with protein engineering endeavors based on these observations to improve some of the properties of cold-adapted enzymes to better suit specific applications. We finally focus on examples of the evaluation of their potential use as biocatalysts under conditions that reproduce the challenges imposed by the use of solvents and additives in industrial processes and of the successful use of cold-adapted enzymes in biotechnological and industrial applications.

Integrated (Meta) Genomic and Synthetic Biology Approaches to Develop New Biocatalysts

In recent years, the marine environment has been the subject of increasing attention from biotechnological and pharmaceutical industries as a valuable and promising source of novel bioactive compounds. Marine biodiscovery programmes have begun to reveal the extent of novel compounds encoded within the enormous bacterial richness and diversity of the marine ecosystem. A combination of unique physicochemical properties and spatial niche-specific substrates, in wide-ranging and extreme habitats, underscores the potential of the marine environment to deliver on functionally novel biocatalytic activities. With the growing need for green alternatives to industrial processes, and the unique transformations which nature is capable of performing, marine biocatalysts have the potential to markedly improve current industrial pipelines. Furthermore, biocatalysts are known to possess chiral selectivity and specificity, a key focus of pharmaceutical drug design. In this review, we discuss how the explosion in genomics based sequence analysis, allied with parallel developments in synthetic and molecular biology, have the potential to fast-track the discovery and subsequent improvement of a new generation of marine biocatalysts.

Engineered hydrophobic pocket of (S)-selective arylmalonate decarboxylase variant by simultaneous saturation mutagenesis to improve catalytic performance

A bacterial arylmalonate decarboxylase (AMDase) catalyzes asymmetric decarboxylation of unnatural arylmalonates to produce optically pure (R)-arylcarboxylates without the addition of cofactors. Previously, we designed an AMDase variant G74C/C188S that displays totally inverted enantioselectivity. However, the variant showed a 20,000-fold reduction in activity compared with the wild-type AMDase. Further studies have demonstrated that iterative saturation mutagenesis targeting the active site residues in a hydrophobic pocket of G74C/C188S leads to considerable improvement in activity where all positive variants harbor only hydrophobic substitutions. In this study, simultaneous saturation mutagenesis with a restricted set of amino acids at each position was applied to further heighten the activity of the (S)-selective AMDase variant toward α-methyl-α-phenylmalonate. The best variant (V43I/G74C/A125P/V156L/M159L/C188G) showed 9,500-fold greater catalytic efficiency kcat/Km than that of G74C/C188S. Notably, a high level of decarboxylation of α-(4-isobutylphenyl)-α-methylmalonate by the sextuple variant produced optically pure (S)-ibuprofen, an analgesic compound which showed 2.5-fold greater activity than the (R)-selective wild-type AMDase.

The taming of oxygen: biocatalytic oxyfunctionalisations

The scope and limitations of oxygenases as catalysts for preparative organic synthesis is discussed.

Directed evolution of phenylacetone monooxygenase as an active catalyst for the Baeyer-Villiger conversion of cyclohexanone to caprolactone

Phenylacetone monooxygenase (PAMO) is an exceptionally robust Baeyer-Villiger monooxygenase, which makes it ideal for potential industrial applications. However, its substrate scope is limited, unreactive cyclohexanone being a prominent example. Such a limitation is unfortunate, because this particular transformation in an ecologically viable manner would be highly desirable, the lactone and the respective lactam being of considerable interest as monomers in polymer science. We have applied directed evolution in search of an active mutant for this valuable C-C activating reaction. Using iterative saturation mutagenesis (ISM), several active mutants were evolved, with only a minimal trade-off in terms of stability. The best mutants allow for quantitative conversion of 2 mM cyclohexanone within 1 h reaction time. In order to circumvent the NADP(+) regeneration problem, whole E. coli resting cells were successfully applied. Molecular dynamics simulations and induced fit docking throw light on the origin of enhanced PAMO activity. The PAMO mutants constitute ideal starting points for future directed evolution optimization necessary for an industrial process.

MDC-Analyzer-facilitated combinatorial strategy for improving the activity and stability of halohydrin dehalogenase from Agrobacterium radiobacter AD1

Halohydrin dehalogenase from Agrobacterium radiobacter AD1 (HheC) displays a broad substrate range with high regio- and enantioselectivity of both ring-closure and ring-opening reactions, making the enzyme a useful catalyst for the production of optically pure epoxides and β-substituted alcohols. In this study, we report a novel method using an MDC-Analyzer-facilitated combinatorial strategy to improve the activity and stability of HheC by simultaneously randomizing multiple contiguous residues. Six contiguous active-site residues, which are the hotspots for improving the activity of HheC, were simultaneously selected and randomized using the MDC-Analyzer-facilitated combinatorial strategy, resulting in a high-quality mutagenesis library. After screening a total of 1152 clones, three positive mutants were obtained, which exhibited approximately 3.5-5.9-fold higher kcat values than the wild-type HheC toward 1,3-dichloro-2-propanol (1,3-DCP). However, the inactivation half-life of the best mutant (DG9) at 55 °C decreased 9-fold compared with that of the wild-type HheC. To improve the stability of mutant DG9, seven contiguous potential surface amino acids were revealed by using the B-FITTER tool. Two charged amino acids, Glu and Lys, which are more abundant in thermophilic proteins than in their mesophilic counterparts, were selected to substitute those seven amino acids and were combined together via an MDC-Analyzer-facilitated combinatorial strategy. Two mutants displaying 1.6- and 2.3-fold higher half-life τ1/2 (55 °C) values than their DG9 template were obtained after screening only 384 clones. The results indicated that an MDC-Analyzer-facilitated combinatorial strategy represents an efficient tool for the directed evolution of functional enzymes with multiple contiguous targeting sites.

Speeding up directed evolution: Combining the advantages of solid-phase combinatorial gene synthesis with statistically guided reduction of screening effort

Efficient and economic methods in directed evolution at the protein, metabolic, and genome level are needed for biocatalyst development and the success of synthetic biology. In contrast to random strategies, semirational approaches such as saturation mutagenesis explore the sequence space in a focused manner. Although several combinatorial libraries based on saturation mutagenesis have been reported using solid-phase gene synthesis, direct comparison with traditional PCR-based methods is currently lacking. In this work, we compare combinatorial protein libraries created in-house via PCR versus those generated by commercial solid-phase gene synthesis. Using descriptive statistics and probabilistic distributions on amino acid occurrence frequencies, the quality of the libraries was assessed and compared, revealing that the outsourced libraries are characterized by less bias and outliers than the PCR-based ones. Afterward, we screened all libraries following a traditional algorithm for almost complete library coverage and compared this approach with an emergent statistical concept suggesting screening a lower portion of the protein sequence space. Upon analyzing the biocatalytic landscapes and best hits of all combinatorial libraries, we show that the screening effort could have been reduced in all cases by more than 50%, while still finding at least one of the best mutants.

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.

Finding the switch: turning a baeyer-villiger monooxygenase into a NADPH oxidase

By a targeted enzyme engineering approach, we were able to create an efficient NADPH oxidase from a monooxygenase. Intriguingly, replacement of only one specific single amino acid was sufficient for such a monooxygenase-to-oxidase switch-a complete transition in enzyme activity. Pre-steady-state kinetic analysis and elucidation of the crystal structure of the C65D PAMO mutant revealed that the mutation introduces small changes near the flavin cofactor, resulting in a rapid decay of the peroxyflavin intermediate. The engineered biocatalyst was shown to be a thermostable, solvent tolerant, and effective cofactor-regenerating biocatalyst. Therefore, it represents a valuable new biocatalytic tool.

Transitioning organic synthesis from organic solvents to water. What's your E Factor?

Traditional organic chemistry, and organic synthesis in particular, relies heavily on organic solvents, as most reactions involve organic substrates and catalysts that tend to be water-insoluble. Unfortunately, organic solvents make up most of the organic waste created by the chemical enterprise, whether from academic, industrial, or governmental labs. One alternative to organic solvents follows the lead of Nature: water. To circumvent the solubility issues, newly engineered "designer" surfactants offer an opportunity to efficiently enable many of the commonly used transition metal-catalyzed and related reactions in organic synthesis to be run in water, and usually at ambient temperatures. This review focuses on recent progress in this area, where such amphiphiles spontaneously self-aggregate in water. The resulting micellar arrays serve as nanoreactors, obviating organic solvents as the reaction medium, while maximizing environmental benefits.