Catalytic efficiency of designed catalytic proteins

Catalytic Nanomaterials by Conjugation of an Artificial Heme-Peroxidase to Amyloid Fibrils

The self-assembly of proteins and peptides into fibrillar amyloid aggregates is a highly promising route to define the next generation of functional nanomaterials. Amyloid fibrils, traditionally associated with neurodegenerative diseases, offer exceptional conformational and chemical stability and mechanical properties, and resistance to degradation. Here, we report the development of catalytic amyloid nanomaterials through the conjugation of a miniaturized artificial peroxidase (FeMC6*a) to a self-assembling amyloidogenic peptide derived from human transthyretin, TTR(105-115), whose sequence is YTIAALLSPYS. Our synthetic approach relies on fast and selective click ligation upon proper modification of both the peptide and FeMC6*a, leading to TTRLys108@FeMC6*a. Mixing unmodified TTR(105-115) with TTRLys108@FeMC6*a allowed the generation of enzyme-loaded amyloid fibrils, namely, FeMC6*a@fibrils. Catalytic studies, performed in aqueous solution at nearly neutral pH, using ABTS as a model substrate and H2O2 as the oxidizing agent revealed that the enzyme retains its catalytic activity. Moreover, the activity was found to depend on the TTRLys108@FeMC6*a/unmodified TTR(105-115) peptide ratio. In particular, those with the 2:100 ratio showed the highest activity in terms of initial rates and substrate conversion among the screened nanoconjugates and compared to the freely diffusing enzyme. Finally, the newly developed nanomaterials were integrated into a flow system based on a polyvinylidene difluoride membrane filter. Within this flow-reactor, multiple reaction cycles were performed, showcasing the reusability and stability of the catalytic amyloids over extended periods, thus offering significantly improved characteristics compared to the isolated FeMC6*a in the application to a number of practical scenarios.

Stopped-flow measurement of CO2 hydration activity by catalytic amyloids

With the ever-increasing rates of catalysis shown by catalytic amyloids, the use of faster characterization techniques is required for proper kinetic studies. The same is true for inherently fast chemical reactions. Carbon dioxide hydration is of significant interest to the field of enzyme design, given both carbonic anhydrases' status as a "perfect enzyme" and the central role carbonic anhydrase plays in the respiration and existence of all carbon-based life. Carbon dioxide is an underexplored hydrolysis substrate within the literature, and a lack of a direct spectroscopic marker for reaction monitoring can make studies more complex and require specialist equipment. Within this article we present a method for measuring the carbon dioxide hydration activity of amyloid fibrils.

Screening of oxidative behavior in catalytic amyloid assemblies

Once considered a thermodynamic minimum of the protein fold or as simply by-products of a misfolding process, amyloids are increasingly showing remarkable potential for promoting enzyme-like catalysis. Recent studies have demonstrated a diverse range of catalytic behaviors that amyloids can promote way beyond the hydrolytic behaviors originally reported. We and others have demonstrated the strong propensity of catalytic amyloids to facilitate redox reactions both in the presence and in the absence of metal cofactors. Here, we present a detailed protocol for measuring the oxidative ability of supramolecular peptide assemblies.

Simulating chemical reactions promoted by self-assembled peptides with catalytic properties

Peptides that self-assemble exhibit distinct three-dimensional structures and attributes, positioning them as promising candidates for biocatalysts. Exploring their catalytic processes enhances our comprehension of the catalytic actions inherent to self-assembling peptides, laying a theoretical foundation for creating novel biocatalysts. The investigation into the intricate reaction mechanisms of these entities is rendered challenging due to the vast variability in peptide sequences, their aggregated formations, supportive elements, structures of active sites, types of catalytic reactions, and the interplay between these variables. This complexity hampers the elucidation of the linkage between sequence, structure, and catalytic efficiency in self-assembling peptide catalysts. This chapter delves into the latest progress in understanding the mechanisms behind peptide self-assembly, serving as a catalyst in hydrolysis and oxidation reactions, and employing computational analyses. It discusses the establishment of models, selection of computational strategies, and analysis of computational procedures, emphasizing the application of modeling techniques in probing the catalytic mechanisms of peptide self-assemblies. It also looks ahead to the potential future trajectories within this research domain. Despite facing numerous obstacles, a thorough investigation into the structural and catalytic mechanisms of peptide self-assemblies, combined with the ongoing advancement in computational simulations and experimental methodologies, is set to offer valuable theoretical insights for the development of new biocatalysts, thereby significantly advancing the biocatalysis field.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

From random to rational: improving enzyme design through electric fields, second coordination sphere interactions, and conformational dynamics

Enzymes are versatile and efficient biological catalysts that drive numerous cellular processes, motivating the development of enzyme design approaches to tailor catalysts for diverse applications. In this perspective, we investigate the unique properties of natural, evolved, and designed enzymes, recognizing their strengths and shortcomings. We highlight the challenges and limitations of current enzyme design protocols, with a particular focus on their limited consideration of long-range electrostatic and dynamic effects. We then delve deeper into the impact of the protein environment on enzyme catalysis and explore the roles of preorganized electric fields, second coordination sphere interactions, and protein dynamics for enzyme function. Furthermore, we present several case studies illustrating successful enzyme-design efforts incorporating enzyme strategies mentioned above to achieve improved catalytic properties. Finally, we envision the future of enzyme design research, spotlighting the challenges yet to be overcome and the synergy of intrinsic electric fields, second coordination sphere interactions, and conformational dynamics to push the state-of-the-art boundaries.

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.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

Ensemble-function relationships to dissect mechanisms of enzyme catalysis

Decades of structure-function studies have established our current extensive understanding of enzymes. However, traditional structural models are snapshots of broader conformational ensembles of interchanging states. We demonstrate the need for conformational ensembles to understand function, using the enzyme ketosteroid isomerase (KSI) as an example. Comparison of prior KSI cryogenic x-ray structures suggested deleterious mutational effects from a misaligned oxyanion hole catalytic residue. However, ensemble information from room-temperature x-ray crystallography, combined with functional studies, excluded this model. Ensemble-function analyses can deconvolute effects from altering the probability of occupying a state (P-effects) and changing the reactivity of each state (k-effects); our ensemble-function analyses revealed functional effects arising from weakened oxyanion hole hydrogen bonding and substrate repositioning within the active site. Ensemble-function studies will have an integral role in understanding enzymes and in meeting the future goals of a predictive understanding of enzyme catalysis and engineering new enzymes.

Native Glucagon Amyloids Catalyze Key Metabolic Reactions

Glucagon is a prominent peptide hormone, playing central roles in the regulation of glucose blood-level and lipid metabolism. Formation of glucagon amyloid fibrils has been previously reported, although no biological functions of such fibrils are known. Here, we demonstrate that glucagon amyloid fibrils catalyze biologically important reactions, including esterolysis, lipid hydrolysis, and dephosphorylation. In particular, we found that glucagon fibrils catalyze dephosphorylation of adenosine triphosphate (ATP), a core metabolic reaction in cell biology. Comparative analysis of several glucagon variants allowed mapping the catalytic activity to an enzymatic pocket-like triad formed at the glucagon fibril surface, comprising the histidyl-serine domain at the N-terminus of the peptide. This study may point to previously unknown physiological roles and pathological consequences of glucagon fibrillation and supports the hypothesis that catalytic activities of native amyloid fibrils play functional roles in human physiology and disease.

The evolution of paramagnetic NMR as a tool in structural biology

Paramagnetic NMR data contain extremely accurate long-range information on metalloprotein structures and, when used in the frame of integrative structural biology approaches, they allow for the retrieval of structural details to a resolution that is not achievable using other techniques. Paramagnetic data thus represent an extremely powerful tool to refine protein models in solution, especially when coupled to X-ray or cryoelectron microscopy data, to monitor the formation of complexes and determine the relative arrangements of their components, and to highlight the presence of conformational heterogeneity. More recently, theoretical and computational advancements in quantum chemical calculations of paramagnetic NMR observables are progressively opening new routes in structural biology, because they allow for the determination of the structure within the coordination sphere of the metal center, thus acting as a loupe on sites that are difficult to observe but very important for protein function.

Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA

Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

A bound iron porphyrin is redox active in hybrid bacterial reaction centers modified to possess a four-helix bundle domain

In this paper we report the design of hybrid reaction centers with a novel redox-active cofactor. Reaction centers perform the primary photochemistry of photosynthesis, namely the light-induced transfer of an electron from the bacteriochlorophyll dimer to a series of electron acceptors. Hybrid complexes were created by the fusion of an artificial four-helix bundle to the M-subunit of the reaction center. Despite the large modification, optical spectra show that the purified hybrid reaction centers assemble as active complexes that retain the characteristic cofactor absorption peaks and are capable of light-induced charge separation. The four-helix bundle could bind iron-protoporphyrin in either a reduced and oxidized state. After binding iron-protoporphyrin to the hybrid reaction centers, light excitation results in a new derivative signal with a maximum at 402 nm and minimum at 429 nm. This signal increases in amplitude with longer light durations and persists in the dark. No signal is observed when iron-protoporphyrin is added to reaction centers without the four-helix bundle domain or when a redox-inactive zinc-protoporphyrin is bound. The results are consistent with the signal arising from a new redox reaction, electron transfer from the iron-protoporphyrin to the oxidized bacteriochlorophyll dimer. These outcomes demonstrate the feasibility of binding porphyrins to the hybrid reaction centers to gain new light-driven functions.

Enzyme modification using mutation site prediction method for enhancing the regioselectivity of substrate reaction sites

Enzymes with low regioselectivity of substrate reaction sites may produce multiple products from a single substrate. When a target product is produced industrially using these enzymes, the production of non-target products (byproducts) causes adverse effects such as increased processing costs for purification and the amount of raw material. Thus it is required the development of modified enzymes to reduce the amount of byproducts’ production. In this paper, we report a method called mutation site prediction for enhancing the regioselectivity of substrate reaction sites (MSPER). MSPER takes conformational data for docking poses of an enzyme and a substrate as input and automatically generates a ranked list of mutation sites to destabilize docking poses for byproducts while maintaining those for target products in silico. We applied MSPER to the enzyme cytochrome P450 CYP102A1 (BM3) and the two substrates to enhance the regioselectivity for four target products with different reaction sites. The 13 of the total 14 top-ranked mutation sites predicted by MSPER for the four target products succeeded in selectively enhancing the regioselectivity up to 6.4-fold. The results indicate that MSPER can distinguish differences of substrate structures and the reaction sites, and can accurately predict mutation sites to enhance regioselectivity without selection by directed evolution screening.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Primitive selection of the fittest emerging through functional synergy in nucleopeptide networks

Many fundamental cellular and viral functions, including replication and translation, involve complex ensembles hosting synergistic activity between nucleic acids and proteins/peptides. There is ample evidence indicating that the chemical precursors of both nucleic acids and peptides could be efficiently formed in the prebiotic environment. Yet, studies on nonenzymatic replication, a central mechanism driving early chemical evolution, have focused largely on the activity of each class of these molecules separately. We show here that short nucleopeptide chimeras can replicate through autocatalytic and cross-catalytic processes, governed synergistically by the hybridization of the nucleobase motifs and the assembly propensity of the peptide segments. Unequal assembly-dependent replication induces clear selectivity toward the formation of a certain species within small networks of complementary nucleopeptides. The selectivity pattern may be influenced and indeed maximized to the point of almost extinction of the weakest replicator when the system is studied far from equilibrium and manipulated through changes in the physical (flow) and chemical (template and inhibition) conditions. We postulate that similar processes may have led to the emergence of the first functional nucleic-acid-peptide assemblies prior to the origin of life. Furthermore, spontaneous formation of related replicating complexes could potentially mark the initiation point for information transfer and rapid progression in complexity within primitive environments, which would have facilitated the development of a variety of functions found in extant biological assemblies.

Artificial enzymes bringing together computational design and directed evolution

Enzymes are proteins that catalyse chemical reactions and, as such, have been widely used to facilitate a variety of natural and industrial processes, dating back to ancient times. In fact, the global enzymes market is projected to reach $10.5 billion in 2024. The development of computational and DNA editing tools boosted the creation of artificial enzymes (de novo enzymes) - synthetic or organic molecules created to present abiological catalytic functions. These novel catalysts seek to expand the catalytic power offered by nature through new functions and properties. In this manuscript, we discuss the advantages of combining computational design with directed evolution for the development of artificial enzymes and how this strategy allows to fill in the gaps that these methods present individually by providing key insights about the sequence-function relationship. We also review examples, and respective strategies, where this approach has enabled the creation of artificial enzymes with promising catalytic activity. Such key enabling technologies are opening new windows of opportunity in a variety of industries, including pharmaceutical, chemical, biofuels, and food, contributing towards a more sustainable development.

A High-Resolution View of the Coordination Environment in a Paramagnetic Metalloprotein from its Magnetic Properties

Metalloproteins constitute a significant fraction of the proteome of all organisms and their characterization is critical for both basic sciences and biomedical applications. A large portion of metalloproteins bind paramagnetic metal ions, and paramagnetic NMR spectroscopy has been widely used in their structural characterization. However, the signals of nuclei in the immediate vicinity of the metal center are often broadened beyond detection. In this work, we show that it is possible to determine the coordination environment of the paramagnetic metal in the protein at a resolution inaccessible to other techniques. Taking the structure of a diamagnetic analogue as a starting point, a geometry optimization is carried out by fitting the pseudocontact shifts obtained from first principles quantum chemical calculations to the experimental ones.

Biocatalysts Based on Peptide and Peptide Conjugate Nanostructures

Peptides and their conjugates (to lipids, bulky N-terminals, or other groups) can self-assemble into nanostructures such as fibrils, nanotubes, coiled coil bundles, and micelles, and these can be used as platforms to present functional residues in order to catalyze a diversity of reactions. Peptide structures can be used to template catalytic sites inspired by those present in natural enzymes as well as simpler constructs using individual catalytic amino acids, especially proline and histidine. The literature on the use of peptide (and peptide conjugate) α-helical and β-sheet structures as well as turn or disordered peptides in the biocatalysis of a range of organic reactions including hydrolysis and a variety of coupling reactions (e.g., aldol reactions) is reviewed. The simpler design rules for peptide structures compared to those of folded proteins permit ready ab initio design (minimalist approach) of effective catalytic structures that mimic the binding pockets of natural enzymes or which simply present catalytic motifs at high density on nanostructure scaffolds. Research on these topics is summarized, along with a discussion of metal nanoparticle catalysts templated by peptide nanostructures, especially fibrils. Research showing the high activities of different classes of peptides in catalyzing many reactions is highlighted. Advances in peptide design and synthesis methods mean they hold great potential for future developments of effective bioinspired and biocompatible catalysts.

Acyl Transfer Catalytic Activity in De Novo Designed Protein with N-Terminus of α-Helix As Oxyanion-Binding Site

The design of catalytic proteins with functional sites capable of specific chemistry is gaining momentum and a number of artificial enzymes have recently been reported, including hydrolases, oxidoreductases, retro-aldolases, and others. Our goal is to develop a peptide ligase for robust catalysis of amide bond formation that possesses no stringent restrictions to the amino acid composition at the ligation junction. We report here the successful completion of the first step in this long-term project by building a completely de novo protein with predefined acyl transfer catalytic activity. We applied a minimalist approach to rationally design an oxyanion hole within a small cavity that contains an adjacent thiol nucleophile. The N-terminus of the α-helix with unpaired hydrogen-bond donors was exploited as a structural motif to stabilize negatively charged tetrahedral intermediates in nucleophilic addition-elimination reactions at the acyl group. Cysteine acting as a principal catalytic residue was introduced at the second residue position of the α-helix N-terminus in a designed three-α-helix protein based on structural informatics prediction. We showed that this minimal set of functional elements is sufficient for the emergence of catalytic activity in a de novo protein. Using peptide-αthioesters as acyl-donors, we demonstrated their catalyzed amidation concomitant with hydrolysis and proved that the environment at the catalytic site critically influences the reaction outcome. These results represent a promising starting point for the development of efficient catalysts for protein labeling, conjugation, and peptide ligation.

Extension of an Atom-Atom Dispersion Function to Halogen Bonds and Its Use for Rational Design of Drugs and Biocatalysts

A dispersion function Das in the form of a damped atom-atom asymptotic expansion fitted to ab initio dispersion energies from symmetry-adapted perturbation theory was improved and extended to systems containing heavier halogen atoms. To illustrate its performance, the revised Das function was implemented in the multipole first-order electrostatic and second-order dispersion (MED) scoring model. The extension has allowed applications to a much larger set of biocomplexes than it was possible with the original Das. A reasonable correlation between MED and experimentally determined inhibitory activities was achieved in a number of test cases, including structures featuring nonphysically shortened intermonomer distances, which constitute a particular challenge for binding strength predictions. Since the MED model is also computationally efficient, it can be used for reliable and rapid assessment of the ligand affinity or multidimensional scanning of amino acid side-chain conformations in the process of rational design of novel drugs or biocatalysts.

Enzyme Catalyst Engineering toward the Integration of Biocatalysis and Chemocatalysis

Enzymatic catalysis, which has been driving biological processes in a green, mild, and efficient manner for billions of years, is increasingly being used in industrial processes to manufacture chemicals, pharmaceuticals, and materials for human society. Since enzymes were discovered, strategies to adapt enzymes for use as catalysts for industrial processes, such as chemical modification, immobilization, site-directed mutagenesis, directed evolution of enzymes, artificial metalloenzymes, and computational design, have been continuously pursued. In contrast to these strategies, editing enzymes to easily integrate biocatalysis with chemocatalysis is a potential way to apply enzymes in industry. Enzyme catalyst editing focuses on fine-tuning the microenvironment surrounding the enzyme or achieving a new catalytic function to construct better biocatalysis under non-natural conditions for the enzyme.

Catalytic Principles from Natural Enzymes and Translational Design Strategies for Synthetic Catalysts

As biocatalysts, enzymes are characterized by their high catalytic efficiency and strong specificity but are relatively fragile by requiring narrow and specific reactive conditions for activity. Synthetic catalysts offer an opportunity for more chemical versatility operating over a wider range of conditions but currently do not reach the remarkable performance of natural enzymes. Here we consider some new design strategies based on the contributions of nonlocal electric fields and thermodynamic fluctuations to both improve the catalytic step and turnover for rate acceleration in arbitrary synthetic catalysts through bioinspired studies of natural enzymes. With a focus on the enzyme as a whole catalytic construct, we illustrate the translational impact of natural enzyme principles to synthetic enzymes, supramolecular capsules, and electrocatalytic surfaces.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Picometer Resolution Structure of the Coordination Sphere in the Metal-Binding Site in a Metalloprotein by NMR

Most of our understanding of chemistry derives from atomic-level structures obtained with single-crystal X-ray diffraction. Metal centers in X-ray structures of small organometallic or coordination complexes are often extremely well-defined, with errors in the positions on the order of 10^-4-10^-5 Å. Determining the metal coordination geometry to high accuracy is essential for understanding metal center reactivity, as even small structural changes can dramatically alter the metal activity. In contrast, the resolution of X-ray structures in proteins is limited typically to the order of 10^-1 Å. This resolution is often not sufficient to develop precise structure-activity relations for the metal sites in proteins, because the uncertainty in positions can cover all of the known ranges of bond lengths and bond angles for a given type of metal complex. Here we introduce a new approach that enables the determination of a high-definition structure of the active site of a metalloprotein from a powder sample, by combining magic-angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy, tailored radio frequency (RF) irradiation schemes, and computational approaches. This allows us to overcome the "blind sphere" in paramagnetic proteins, and to observe and assign ¹H, ¹³C, and ¹⁵N resonances for the ligands directly coordinating the metal center. We illustrate the method by determining the bond lengths in the structure of the Co^II coordination sphere at the core of human superoxide dismutase 1 (SOD) with 0.7 pm precision. The coordination geometry of the resulting structure explains the nonreactive nature of the Co^II/Zn^II centers in these proteins, which allows them to play a purely structural role.

The Quest for Xenobiotic Enzymes: From New Enzymes for Chemistry to a Novel Chemistry of Life

Enzyme engineering has made impressive progress in the past decades, paving the way for the widespread use of enzymes for various purposes. In contrast to "classical" enzyme engineering, which focuses on optimizing specific properties of natural enzymes, a more recent trend towards the creation of artificial enzymes that catalyze fundamentally distinct, new-to-nature reactions is observable. While approaches for creating such enzymes differ significantly, they share the common goal of enabling biocatalytic novelty to broaden the range of applications for enzymes. Although most artificial enzymes reported to date are only moderately active and barely function in vivo, they have the potential to endow cells with capabilities that were previously out of reach and thus herald a new wave of "functional xenobiology". Herein, we highlight recent developments in the field of artificial enzymes with a particular focus on challenges and opportunities for their use in xenobiology.

Conjugating Catalytic Polyproline Fragments with a Self-Assembling Peptide Produces Efficient Artificial Hydrolases

A polyproline fragment containing a catalytic dyad of His-His or Ser-His was coupled with a self-assembling peptide MAX1 to design new hydrolases (H2H5 and H2S5) for catalyzing ester hydrolysis. Circular dichroism measurements indicated that the peptides change their conformation from random coils to β-sheets when pH increases from 5 to 10. IR spectra also displayed the vibration modes corresponding to their β-structures at pH 9.0. Transmission electron microscopy (TEM) and atomic force microscopy (AFM) measurements showed that in solution, the designed peptides self-assemble into network fibrils having a significantly increased catalytic efficiency on ester hydrolysis. On p-nitrophenyl acetate (p-NPA) substrate, the designed peptides exhibit high catalytic efficiency at pH 9.0 (k_cat/K_M = 12.1 M^-1 s^-1 for H2H5, 13.3 M^-1 s^-1 for H2S5), and their efficiency is even better at pH 10.0 (k_cat/K_M = 24.3 M^-1 s^-1 for H2H5, 99.4 M^-1 s^-1 for H2S5). Additionally, H2H5 and H2S5 also display good activity on catalyzing the hydrolysis of p-nitrophenyl-(2-phenyl)-propanoate (p-NPP) and p-nitrophenyl methoxyacetate (p-NPMA). Combining the polyproline-based catalytic scaffold with a self-assembling peptide generates an efficient hydrolase, providing a new design for effective artificial enzymes.

Uno Ferro, a de novo Designed Protein, Binds Transition Metals with High Affinity and Stabilizes Semiquinone Radical Anion

Metalloenzymes often utilize radicals in order to facilitate chemical reactions. Recently, DeGrado and co-workers have discovered that model proteins can efficiently stabilize semiquinone radical anion produced by oxidation of 3,5-di-tert-butylcatechol (DTBC) in the presence of two zinc ions. Here, we show that the number and the nature of metal ions have relatively minor effect on semiquinone stabilization in model proteins, with a single metal ion being sufficient for radical stabilization. The radical is stabilized by both metal ion, hydrophobic sequestration, and interactions with the hydrophilic residues in the protein interior resulting in a remarkable, nearly 500 mV change in the redox potential of the SQ^{. -} /catechol couple compared to bulk aqueous solution. Moreover, we have created 4G-UFsc, a single metal ion-binding protein with pm affinity for zinc that is higher than any other reported model systems and is on par with many natural zinc-containing proteins. We expect that the robust and easy-to-modify DFsc/UFsc family of proteins will become a versatile tool for mechanistic model studies of metalloenzymes.

Minimalist de novo Design of Protein Catalysts

The field of protein design has grown enormously in the past few decades. In this review we discuss the minimalist approach to design of artificial enzymes, in which protein sequences are created with the minimum number of elements for folding and function. This method relies on identifying starting points in catalytically inert scaffolds for active site installation. The progress of the field from the original helical assemblies of the 1980s to the more complex structures of the present day is discussed, highlighting the variety of catalytic reactions which have been achieved using these methods. We outline the strengths and weaknesses of the minimalist approaches, describe representative design cases and put it in the general context of the de novo design of proteins.

Kemp Eliminases of the AlleyCat Family Possess High Substrate Promiscuity

Minimalist enzymes designed to catalyze model reactions provide useful starting points for creating catalysts for practically important chemical transformations. We have shown that Kemp eliminases of the AlleyCat family facilitate conversion of leflunomide (an immunosupressor pro-drug) to its active form teriflunomide with outstanding rate enhancement (nearly four orders of magnitude) and catalytic proficiency (more than seven orders of magnitude) without any additional optimization. This remarkable activity is achieved by properly positioning the substrate in close proximity to the catalytic glutamate with very high pKa.

Principles Governing Catalytic Activity of Self-Assembled Short Peptides

Molecular self-assembly provides a chemical strategy for the synthesis of nanostructures by using the principles of nature, and peptides serve as the promising building blocks to construct adaptable molecular architectures. Recently, a series of heptapeptides with alternative hydrophobic and hydrophilic residues were reported to form amyloid-like structures, which are capable of catalyzing acyl ester hydrolysis with remarkable efficiency. However, information remains elusive about the atomic structures of the fibrils. What is the origin of the sequence-dependent catalytic activity? How is the ester hydrolysis catalyzed by the fibrils? In this work, the atomic structures of the aggregates were determined by using molecular modeling and further validated by solid-state NMR experiments, where the fibril with high activity adopts twisted parallel configuration within each layer, and the one with low activity is in flat antiparallel configuration. The polymorphism originates from the interactions between different regions of the building block peptides, where the delicate balance between rigidity and flexibility plays an important role. We further show that the p-nitrophenylacetate ( pNPA) hydrolysis reactions catalyzed by two different fibrils follow a similar mechanism, and the difference in microenvironment at the active site between the natural enzyme and the present self-assembled fibrils should account for the discrepancy in catalytic activities. The present work provides understanding of the structure and function of self-assembled fibrils formed with short peptides at an atomic level and thus sheds new insight on designing aggregates with better functions.

Comparison of Alicyclobacillus acidocaldarius o-Succinylbenzoate Synthase to Its Promiscuous N-Succinylamino Acid Racemase/ o-Succinylbenzoate Synthase Relatives

Studying the evolution of catalytically promiscuous enzymes like those from the N-succinylamino acid racemase/ o-succinylbenzoate synthase (NSAR/OSBS) subfamily can reveal mechanisms by which new functions evolve. Some enzymes in this subfamily have only OSBS activity, while others catalyze OSBS and NSAR reactions. We characterized several NSAR/OSBS subfamily enzymes as a step toward determining the structural basis for evolving NSAR activity. Three enzymes were promiscuous, like most other characterized NSAR/OSBS subfamily enzymes. However, Alicyclobacillus acidocaldarius OSBS (AaOSBS) efficiently catalyzes OSBS activity but lacks detectable NSAR activity. Competitive inhibition and molecular modeling show that AaOSBS binds N-succinylphenylglycine with moderate affinity in a site that overlaps its normal substrate. On the basis of possible steric conflicts identified by molecular modeling and sequence conservation within the NSAR/OSBS subfamily, we identified one mutation, Y299I, that increased NSAR activity from undetectable to 1.2 × 10² M^-1 s^-1 without affecting OSBS activity. This mutation does not appear to affect binding affinity but instead affects k_cat, by reorienting the substrate or modifying conformational changes to allow both catalytic lysines to access the proton that is moved during the reaction. This is the first site known to affect reaction specificity in the NSAR/OSBS subfamily. However, this gain of activity was obliterated by a second mutation, M18F. Epistatic interference by M18F was unexpected because a phenylalanine at this position is important in another NSAR/OSBS enzyme. Together, modest NSAR activity of Y299I AaOSBS and epistasis between sites 18 and 299 indicate that additional sites influenced the evolution of NSAR reaction specificity in the NSAR/OSBS subfamily.

Catalytic peptide assemblies

Self-assembly of molecules often results in new emerging properties. Even very short peptides can self-assemble into structures with a variety of physical and structural characteristics. Remarkably, many peptide assemblies show high catalytic activity in model reactions reaching efficiencies comparable to those found in natural enzymes by weight. In this review, we discuss different strategies used to rationally develop self-assembled peptide catalysts with natural and unnatural backbones as well as with metal-containing cofactors.

Evolution of cyclohexadienyl dehydratase from an ancestral solute-binding protein

The emergence of enzymes through the neofunctionalization of noncatalytic proteins is ultimately responsible for the extraordinary range of biological catalysts observed in nature. Although the evolution of some enzymes from binding proteins can be inferred by homology, we have a limited understanding of the nature of the biochemical and biophysical adaptations along these evolutionary trajectories and the sequence in which they occurred. Here we reconstructed and characterized evolutionary intermediate states linking an ancestral solute-binding protein to the extant enzyme cyclohexadienyl dehydratase. We show how the intrinsic reactivity of a desolvated general acid was harnessed by a series of mutations radiating from the active site, which optimized enzyme-substrate complementarity and transition-state stabilization and minimized sampling of noncatalytic conformations. Our work reveals the molecular evolutionary processes that underlie the emergence of enzymes de novo, which are notably mirrored by recent examples of computational enzyme design and directed evolution.

Biotechnological and protein-engineering implications of ancestral protein resurrection

Approximations to the sequences of ancestral proteins can be derived from the sequences of their modern descendants. Proteins encoded by such reconstructed sequences can be prepared in the laboratory and subjected to experimental scrutiny. These 'resurrected' ancestral proteins often display remarkable properties, reflecting ancestral adaptations to intra-cellular and extra-cellular environments that differed from the environments hosting modern/extant proteins. Recent experimental and computational work has specifically discussed high stability, substrate and catalytic promiscuity, conformational flexibility/diversity and altered patterns of interaction with other sub-cellular components. In this review, we discuss these remarkable properties as well as recent attempts to explore their biotechnological and protein-engineering potential.