Computational design of catalytic dyads and oxyanion holes for ester hydrolysis

Spiers Memorial Lecture: Engineering biocatalysts

Enzymes are being engineered to catalyze chemical reactions for many practical applications in chemistry and biotechnology. The approaches used are surveyed in this short review, emphasizing methods for accessing reactivities not expressed by native protein scaffolds. The successful generation of completely de novo enzymes that rival the rates and selectivities of their natural counterparts highlights the potential role that designer enzymes may play in the coming years in research, industry, and medicine. Some challenges that need to be addressed to realize this ambitious dream are considered together with possible solutions.

Computational design of serine hydrolases

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k cat /K m ) up to 3.8 x 103 M-1 s-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

Noncanonical Amino Acids in Biocatalysis

In recent years, powerful genetic code reprogramming methods have emerged that allow new functional components to be embedded into proteins as noncanonical amino acid (ncAA) side chains. In this review, we will illustrate how the availability of an expanded set of amino acid building blocks has opened a wealth of new opportunities in enzymology and biocatalysis research. Genetic code reprogramming has provided new insights into enzyme mechanisms by allowing introduction of new spectroscopic probes and the targeted replacement of individual atoms or functional groups. NcAAs have also been used to develop engineered biocatalysts with improved activity, selectivity, and stability, as well as enzymes with artificial regulatory elements that are responsive to external stimuli. Perhaps most ambitiously, the combination of genetic code reprogramming and laboratory evolution has given rise to new classes of enzymes that use ncAAs as key catalytic elements. With the framework for developing ncAA-containing biocatalysts now firmly established, we are optimistic that genetic code reprogramming will become a progressively more powerful tool in the armory of enzyme designers and engineers in the coming years.

Genetically encoded Nδ-vinyl histidine for the evolution of enzyme catalytic center

Genetic code expansion has emerged as a powerful tool for precisely introducing unnatural chemical structures into proteins to improve their catalytic functions. Given the high catalytic propensity of histidine in the enzyme pocket, increasing the chemical diversity of catalytic histidine could result in new characteristics of biocatalysts. Herein, we report the genetically encoded Nδ-Vinyl Histidine (δVin-H) and achieve the wild-type-like incorporation efficiency by the evolution of pyrrolysyl tRNA synthetase. As histidine usually acts as the nucleophile or the metal ligand in the catalytic center, we replace these two types of catalytic histidine to δVin-H to improve the performance of the histidine-involved catalytic center. Additionally, we further demonstrate the improvements of the hydrolysis activity of a previously reported organocatalytic esterase (the OE1.3 variant) in the acidic condition and myoglobin (Mb) catalyzed carbene transfer reactions under the aerobic condition. As histidine is one of the most frequently used residues in the enzyme catalytic center, the derivatization of the catalytic histidine by δVin-H holds a great potential to promote the performance of biocatalysts.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Cellular and Biophysical Applications of Genetic Code Expansion

Despite their diverse functions, proteins are inherently constructed from a limited set of building blocks. These compositional constraints pose significant challenges to protein research and its practical applications. Strategically manipulating the cellular protein synthesis system to incorporate novel building blocks has emerged as a critical approach for overcoming these constraints in protein research and application. In the past two decades, the field of genetic code expansion (GCE) has achieved significant advancements, enabling the integration of numerous novel functionalities into proteins across a variety of organisms. This technological evolution has paved the way for the extensive application of genetic code expansion across multiple domains, including protein imaging, the introduction of probes for protein research, analysis of protein-protein interactions, spatiotemporal control of protein function, exploration of proteome changes induced by external stimuli, and the synthesis of proteins endowed with novel functions. In this comprehensive Review, we aim to provide an overview of cellular and biophysical applications that have employed GCE technology over the past two decades.

A non-canonical nucleophile unlocks a new mechanistic pathway in a designed enzyme

Directed evolution of computationally designed enzymes has provided new insights into the emergence of sophisticated catalytic sites in proteins. In this regard, we have recently shown that a histidine nucleophile and a flexible arginine can work in synergy to accelerate the Morita-Baylis-Hillman (MBH) reaction with unrivalled efficiency. Here, we show that replacing the catalytic histidine with a non-canonical Nδ-methylhistidine (MeHis23) nucleophile leads to a substantially altered evolutionary outcome in which the catalytic Arg124 has been abandoned. Instead, Glu26 has emerged, which mediates a rate-limiting proton transfer step to deliver an enzyme (BHMeHis1.8) that is more than an order of magnitude more active than our earlier MBHase. Interestingly, although MeHis23 to His substitution in BHMeHis1.8 reduces activity by 4-fold, the resulting His containing variant is still a potent MBH biocatalyst. However, analysis of the BHMeHis1.8 evolutionary trajectory reveals that the MeHis nucleophile was crucial in the early stages of engineering to unlock the new mechanistic pathway. This study demonstrates how even subtle perturbations to key catalytic elements of designed enzymes can lead to vastly different evolutionary outcomes, resulting in new mechanistic solutions to complex chemical transformations.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Hydrolase mimic via second coordination sphere engineering in metal-organic frameworks for environmental remediation

Enzymes achieve high catalytic activity with their elaborate arrangements of amino acid residues in confined optimized spaces. Nevertheless, when exposed to complicated environmental implementation scenarios, including high acidity, organic solvent and high ionic strength, enzymes exhibit low operational stability and poor activity. Here, we report a metal-organic frameworks (MOFs)-based artificial enzyme system via second coordination sphere engineering to achieve high hydrolytic activity under mild conditions. Experiments and theoretical calculations reveal that amide cleavage catalyzed by MOFs follows two distinct catalytic mechanisms, Lewis acid- and hydrogen bonding-mediated hydrolytic processes. The hydrogen bond formed in the secondary coordination sphere exhibits 11-fold higher hydrolytic activity than the Lewis acidic zinc ions. The MOFs exhibit satisfactory degradation performance of toxins and high stability under extreme working conditions, including complicated fermentation broth and high ethanol environments, and display broad substrate specificity. These findings hold great promise for designing artificial enzymes for environmental remediation.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

Designing Efficient Enzymes: Eight Predicted Mutations Convert a Hydroxynitrile Lyase into an Efficient Esterase

Hydroxynitrile lyase from rubber tree (HbHNL) shares 45% identical amino acid residues with the homologous esterase from tobacco, SABP2, but the two enzymes catalyze different reactions. The x-ray structures reveal a serine-histidine-aspartate catalytic triad in both enzymes along with several differing amino acid residues within the active site. Previous exchange of three amino acid residues in the active site of HbHNL with the corresponding amino acid residue in SABP2 (T11G-E79H-K236M) created variant HNL3, which showed low esterase activity toward p-nitrophenyl acetate. Further structure comparison reveals additional differences surrounding the active site. HbHNL contains an improperly positioned oxyanion hole residue and differing solvation of the catalytic aspartate. We hypothesized that correcting these structural differences would impart good esterase activity on the corresponding HbHNL variant. To predict the amino acid substitutions needed to correct the structure, we calculated shortest path maps for both HbHNL and SABP2, which reveal correlated movements of amino acids in the two enzymes. Replacing four amino acid residues (C81L-N104T-V106F-G176S) whose movements are connected to the movements of the catalytic residues yielded variant HNL7TV (stabilizing substitution H103V was also added), which showed an esterase catalytic efficiency comparable to that of SABP2. The x-ray structure of an intermediate variant, HNL6V, showed an altered solvation of the catalytic aspartate and a partially corrected oxyanion hole. This dramatic increase in catalytic efficiency demonstrates the ability of shortest path maps to predict which residues outside the active site contribute to catalytic activity.

Multiscale Simulations of the Covalent Inhibition of the SARS-CoV-2 Main Protease: Four Compounds and Three Reaction Mechanisms

We report the results of computational modeling of the reactions of the SARS-CoV-2 main protease (MPro) with four potential covalent inhibitors. Two of them, carmofur and nirmatrelvir, have shown experimentally the ability to inhibit MPro. Two other compounds, X77A and X77C, were designed computationally in this work. They were derived from the structure of X77, a non-covalent inhibitor forming a tight surface complex with MPro. We modified the X77 structure by introducing warheads capable of reacting with the catalytic cysteine residue in the MPro active site. The reaction mechanisms of the four molecules with MPro were investigated by quantum mechanics/molecular mechanics (QM/MM) simulations. The results show that all four compounds form covalent adducts with the catalytic cysteine Cys 145 of MPro. From the chemical perspective, the reactions of these four molecules with MPro follow three distinct mechanisms. The reactions are initiated by a nucleophilic attack of the thiolate group of the deprotonated cysteine residue from the catalytic dyad Cys145-His41 of MPro. In the case of carmofur and X77A, the covalent binding of the thiolate to the ligand is accompanied by the formation of the fluoro-uracil leaving group. The reaction with X77C follows the nucleophilic aromatic substitution SNAr mechanism. The reaction of MPro with nirmatrelvir (which has a reactive nitrile group) leads to the formation of a covalent thioimidate adduct with the thiolate of the Cys145 residue in the enzyme active site. Our results contribute to the ongoing search for efficient inhibitors of the SARS-CoV-2 enzymes.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁-M8 were devised from scratch. More importantly, the designed 1a8uD₁-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

TMEM164 is an acyltransferase that forms ferroptotic C20:4 ether phospholipids

Ferroptosis is an iron-dependent form of cell death driven by oxidation of polyunsaturated fatty acid (PUFA) phospholipids. Large-scale genetic screens have uncovered a specialized role for PUFA ether phospholipids (ePLs) in promoting ferroptosis. Understanding of the enzymes involved in PUFA-ePL production, however, remains incomplete. Here we show, using a combination of pathway mining of genetic dependency maps, AlphaFold-guided structure predictions and targeted lipidomics, that the uncharacterized transmembrane protein TMEM164-the genetic ablation of which has been shown to protect cells from ferroptosis-is a cysteine active center enzyme that selectively transfers C20:4 acyl chains from phosphatidylcholine to lyso-ePLs to produce PUFA ePLs. Genetic deletion of TMEM164 across a set of ferroptosis-sensitive cancer cell lines caused selective reductions in C20:4 ePLs with minimal effects on C20:4 diacyl PLs, and this lipid profile produced a variable range of protection from ferroptosis, supportive of an important but contextualized role for C20:4 ePLs in this form of cell death.

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.

Minimalistic Artificial Catalysts with Esterase-Like Activity from Multivalent Nanofibers Formed by the Self-Assembly of Dipeptides

Imitating and incorporating the multiple key structural features observed in natural enzymes into a minimalistic molecule to develop an artificial catalyst with outstanding catalytic efficiency is an attractive topic for chemists. Herein, we designed and synthesized one class of minimalistic dipeptide molecules containing a terminal -SH group and a terminal His-Phe dipeptide head linked by a hydrophobic alkyl chain with different lengths, marked as HS-C n+1-His-Phe (n = 4, 7, 11, 15, and 17; n + 1 represents the carbon atom number of the alkyl chain). The His (-imidazole), Phe (-CO2 -) moieties, the terminal -SH group, and a long hydrophobic alkyl chain were found to have important contributions to achieve high binding ability leading to outstanding absolute catalytic efficiency (k cat/K M) toward the hydrolysis reactions of carboxylic ester substrates.

Esterase Sequence Composition Patterns for the Identification of Catalytic Triad Microenvironment Motifs

Ester hydrolysis is of wide biomedical interest, spanning from the green synthesis of pharmaceuticals to biomaterials' development. Existing peptide-based catalysts exhibit low catalytic efficiency compared to natural enzymes, due to the conformational heterogeneity of peptides. Moreover, there is lack of understanding of the correlation between the primary sequence and catalytic function. For this purpose, we statistically analyzed 22 EC 3.1 hydrolases with known catalytic triads, characterized by unique and well-defined mechanisms. The aim was to identify patterns at the sequence level that will better inform the creation of short peptides containing important information for catalysis, based on the catalytic triad, oxyanion holes and the triad residues microenvironments. Moreover, fragmentation schemes of the primary sequence of selected enzymes alongside the study of their amino acid frequencies, composition, and physicochemical properties are proposed. The results showed highly conserved catalytic sites with distinct positional patterns and chemical microenvironments that favor catalysis and revealed variations in catalytic site composition that could be useful for the design of minimalistic catalysts.

Engineering enzyme activity using an expanded amino acid alphabet

Enzyme design and engineering strategies are typically constrained by the limited size of nature's genetic alphabet, comprised of only 20 canonical amino acids. In recent years, site-selective incorporation of non-canonical amino acids (ncAAs) via an expanded genetic code has emerged as a powerful means of inserting new functional components into proteins, with hundreds of structurally diverse ncAAs now available. Here, we highlight how the emergence of an expanded repertoire of amino acids has opened new avenues in enzyme design and engineering. ncAAs have been used to probe complex biological mechanisms, augment enzyme function and, most ambitiously, embed new catalytic mechanisms into protein active sites that would be challenging to access within the constraints of nature's genetic code. We predict that the studies reviewed in this article, along with further advances in genetic code expansion technology, will establish ncAA incorporation as an increasingly important tool for biocatalysis in the coming years.

Design and Characterization of In-One Protease-Esterase PluriZyme

Proteases are abundant in prokaryotic genomes (~10 per genome), but their recovery encounters expression problems, as only 1% can be produced at high levels; this value differs from that of similarly abundant esterases (1-15 per genome), 50% of which can be expressed at good levels. Here, we design a catalytically efficient artificial protease that can be easily produced. The PluriZyme EH1AB1 with two active sites supporting the esterase activity was employed. A Leu24Cys mutation in EH1AB1, remodelled one of the esterase sites into a proteolytic one through the incorporation of a catalytic dyad (Cys24 and His214). The resulting artificial enzyme, EH1AB1C, efficiently hydrolysed (azo)casein at pH 6.5-8.0 and 60-70 °C. The presence of both esterase and protease activities in the same scaffold allowed the one-pot cascade synthesis (55.0 ± 0.6% conversion, 24 h) of L-histidine methyl ester from the dipeptide L-carnosine in the presence of methanol. This study demonstrates that active sites supporting proteolytic activity can be artificially introduced into an esterase scaffold to design easy-to-produce in-one protease-esterase PluriZymes for cascade reactions, namely, the synthesis of amino acid esters from dipeptides. It is also possible to design artificial proteases with good production yields, in contrast to natural proteases that are difficult to express.

Catalysis and Electron Transfer in De Novo Designed Metalloproteins

One of the hallmark advances in our understanding of metalloprotein function is showcased in our ability to design new, non-native, catalytically active protein scaffolds. This review highlights progress and milestone achievements in the field of de novo metalloprotein design focused on reports from the past decade with special emphasis on de novo designs couched within common subfields of bioinorganic study: heme binding proteins, monometal- and dimetal-containing catalytic sites, and metal-containing electron transfer sites. Within each subfield, we highlight several of what we have identified as significant and important contributions to either our understanding of that subfield or de novo metalloprotein design as a discipline. These reports are placed in context both historically and scientifically. General suggestions for future directions that we feel will be important to advance our understanding or accelerate discovery are discussed.

The road to fully programmable protein catalysis

The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.

Key dimer interface residues impact the catalytic activity of 3CLpro, the main protease of SARS-CoV-2

3C-like protease (3CLpro) is one of two proteases that process and liberate functional viral proteins essential for the maturation and infectivity of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19. It has been suggested that 3CLpro is catalytically active as a dimer, making the dimerization interface a target for antiviral development. Guided by structural analysis, here we introduced single amino acid substitutions at nine residues at three key sites of the dimer interface to assess their impact on dimerization and activity. We show that at site 1, alanine substitution of S1 or E166 increased by 2-fold or reduced relative activity, respectively. At site 2, alanine substitution of S10 or E14 eliminated activity, whereas K12A exhibited ∼60% relative activity. At site 3, alanine substitution of R4, E290, or Q299 eliminated activity, whereas S139A exhibited 46% relative activity. We further found the oligomerization states of the dimer interface mutants varied; the inactive mutants R4A, R4Q, S10A/C, E14A/D/Q/S, E290A, and Q299A/E were present as dimers, demonstrating that dimerization is not an indication of catalytically active 3CLpro. In addition, present mostly as monomers, K12A displayed residual activity, which could be attributed to the conspicuous amount of dimer present. Finally, differential scanning calorimetry did not reveal a direct relationship between the thermodynamic stability of mutants with oligomerization or catalytic activity. These results provide insights on two allosteric sites, R4/E290 and S10/E14, that may promote the design of antiviral compounds that target the dimer interface rather than the active site of SARS-CoV-2 3CLpro.

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.

Evolutionary Aspects of the Oxido-Reductive Network of Methylglyoxal

In the chemoautotrophic theory for the origin of life, offered as an alternative to broth theory, the archaic reductive citric acid cycle operating without enzymes is in the center. The non-enzymatic (methyl)glyoxalase pathway has been suggested to be the anaplerotic route for the reductive citric acid cycle. In the recent years, much has been learned about methylglyoxal, but its importance in the metabolic machinery is still uncovered. If methylglyoxal had been essential participant of the early stage of evolution, then it is a legitimate question whether it might have played a role in the early oxido-reduction network, too. Therefore, an oxido-reduction network of methylglyoxal that might have functioned under ancient circumstances without enzymes was constructed and analyzed by virtue of group contribution method. Taking methylglyoxal as input material, it turned out that the evolutionary value of reactions and biomolecules were not similar. Glycerol, glycerate, and tartonate, the output components, were conserved to different degrees. Although the tartonate route was similarly favorable from energetic point of view, its intermediates are almost not present in extant biochemistry. The presence of two carboxyl or aldehyde groups, or their combination in tricarbons of the constructed network seemed disadvantageous for selection, and the inductive effect, resulting in an asymmetry in electron cloud of chemicals, might have been important. The evolutionary role for cysteine, H₂S, and formaldehyde in the emergence of high-energy bonds in the form of thioesters and in Fe-S cluster formation as well as in imidazole synthesis was shown to bridge the gap between prebiotic chemistry and contemporary biochemistry. Overall, the ideas developed here represent an approach fitting to chemoautotrophic origin of life and implying to the role of methylglyoxal in triose formation. The proposed network is expected to have an impact upon how one may think of prebiological chemical processes on methylglyoxal, too. Finally, along the evolutionary time line, the network functioning without enzymes is situated between the formation of simple organic compounds and primeval cells, being closer to the former and well preceding the last common metabolic ancestor developed after primitive cells emerged.

Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling

Hydrolases are a critical component for modern chemical, pharmaceutical, and environmental sciences. Identifying mutations that enhance catalytic efficiency presents a roadblock to design and to discover new hydrolases for broad academic and industrial uses. Here, we report the statistical profiling for rate-perturbing mutant hydrolases with a single amino acid substitution. We constructed an integrated structure-kinetics database for hydrolases, IntEnzyDB, which contains 3907 k_cats, 4175 K_Ms, and 2715 Protein Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened data structure, enabling facile and efficient access to clean and tabulated data for machine learning uses. We conducted statistical analyses on how single amino acids mutations influence the turnover number (i.e., k_cat) and efficiency (i.e., k_cat/K_M), with a particular emphasis on profiling the features for rate-enhancing mutations. The results show that mutation to bulky nonpolar residues with a hydrocarbon chain involves a higher likelihood for rate acceleration than to other types of residues. Linear regression models reveal geometric descriptors of substrate and mutation residues that mediate rate-perturbing outcomes for hydrolases with bulky nonpolar mutations. On the basis of the analyses of the structure-kinetics relationship, we observe that the propensity for rate enhancement is independent of protein sizes. In addition, we observe that distal mutations (i.e., >10 Å from the active site) in hydrolases are significantly more prone to induce efficiency neutrality and avoid efficiency deletion but involve similar propensity for rate enhancement. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis.

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.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Recent Advances in Biocatalysis with Chemical Modification and Expanded Amino Acid Alphabet

The two main strategies for enzyme engineering, directed evolution and rational design, have found widespread applications in improving the intrinsic activities of proteins. Although numerous advances have been achieved using these ground-breaking methods, the limited chemical diversity of the biopolymers, restricted to the 20 canonical amino acids, hampers creation of novel enzymes that Nature has never made thus far. To address this, much research has been devoted to expanding the protein sequence space via chemical modifications and/or incorporation of noncanonical amino acids (ncAAs). This review provides a balanced discussion and critical evaluation of the applications, recent advances, and technical breakthroughs in biocatalysis for three approaches: (i) chemical modification of cAAs, (ii) incorporation of ncAAs, and (iii) chemical modification of incorporated ncAAs. Furthermore, the applications of these approaches and the result on the functional properties and mechanistic study of the enzymes are extensively reviewed. We also discuss the design of artificial enzymes and directed evolution strategies for enzymes with ncAAs incorporated. Finally, we discuss the current challenges and future perspectives for biocatalysis using the expanded amino acid alphabet.

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.

Light-mediated control of activity in a photosensitive foldamer that mimics an esterase

We report a catalytic foldamer in which a fumaramide chromophore links a Ser residue to a helical domain that contains within its sequence the residues His and Asp. Photoisomerization of the fumaramide chromophore (with E geometry) to the corresponding maleamide (with Z geometry) brings together a 'catalytic triad' of Ser, His, and Asp, triggering esterase activity that is absent in the fumaramide isomer. The fumaramide/maleamide linker thus acts as a light-sensitive switchable cofactor for activation of catalytic activity in short foldamers.

Asymmetric Catalysis Mediated by Synthetic Peptides, Version 2.0: Expansion of Scope and Mechanisms

Low molecular weight synthetic peptides have been demonstrated to be effective catalysts for an increasingly wide array of asymmetric transformations. In many cases, these peptide-based catalysts have enabled novel multifunctional substrate activation modes and unprecedented selectivity manifolds. These features, along with their ease of preparation, modular and tunable structures, and often biomimetic attributes make peptides well-suited as chiral catalysts and of broad interest. Many examples of peptide-catalyzed asymmetric reactions have appeared in the literature since the last survey of this broad field in Chemical Reviews (Chem. Rev. 2007, 107, 5759-5812). The overarching goal of this new Review is to provide a comprehensive account of the numerous advances in the field. As a corollary to this goal, we survey the many different types of catalytic reactions, ranging from acylation to C-C bond formation, in which peptides have been successfully employed. In so doing, we devote significant discussion to the structural and mechanistic aspects of these reactions that are perhaps specific to peptide-based catalysts and their interactions with substrates and/or reagents.

A De Novo Designed Esterase with p-Nitrophenyl Acetate Hydrolysis Activity

Esterases are a large family of enzymes with wide applications in the industry. However, all esterases originated from natural sources, limiting their use in harsh environments or newly- emerged reactions. In this study, we designed a new esterase to develop a new protocol to satisfy the needs for better biocatalysts. The ideal spatial conformation of the serine catalytic triad and the oxygen anion hole at the substrate-binding site was constructed by quantum mechanical calculation. The catalytic triad and oxygen anion holes were then embedded in the protein scaffold using the new enzyme protocol in Rosetta 3. The design results were subsequently evaluated, and optimized designs were used for expression and purification. The designed esterase had significant lytic activities towards p-nitrophenyl acetate, which was confirmed by point mutations. Thus, this study developed a new protocol to obtain novel enzymes that may be useful in unforgiving environments or novel reactions.

Ensemble-based enzyme design can recapitulate the effects of laboratory directed evolution in silico

The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (k_cat/K_M 146 M⁻¹s⁻¹). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (k_cat/K_M 103,000 M⁻¹s⁻¹) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data.

Kemp eliminases are artificial enzymes that catalyze the concerted deprotonation and ring-opening of benzisoxazoles. Here, the authors use room-temperature X-ray crystallography to investigate changes to the conformational ensemble of the Kemp eliminase HG3 along a directed evolutionary trajectory, and develop an experimentally guided, ensemble-based computational enzyme design procedure.

Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes

With the increase of the need to use more sustainable processes for the industry in our society, the modeling of enzymes has become crucial to fully comprehend their mechanism of action and use this knowledge to enhance and design their properties. A lot of methods to study enzymes computationally exist and they have been classified on sequence-based, structure-based, and the more new artificial intelligence-based ones. Albeit the abundance of methods to help predict the function of an enzyme, molecular modeling is crucial when trying to understand the enzyme mechanism, as they aim to correlate atomistic information with experimental data. Among them, methods that simulate the system dynamics at a molecular mechanics level of theory (classical force fields) have shown to offer a comprehensive study. In this book chapter, we will analyze these techniques, emphasizing the importance of precise modeling of enzyme-substrate interactions. In the end, a brief explanation of the transference of the information from research studies to the industry is given accompanied with two examples of family enzymes where their modeling has helped their exploitation.

An enumerative algorithm for de novo design of proteins with diverse pocket structures

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

Structural Basis of Specificity for Carboxyl-Terminated Acyl Donors in a Bacterial Acyltransferase

Macrolactins (MLNs) are a class of important antimacular degeneration and antitumor agents. Malonylated/succinylated MLNs are even more important due to their efficacy in overcoming multi-drug-resistant bacteria. However, which enzyme catalyzes this reaction remains enigmatic. Herein, we deciphered a β-lactamase homologue BmmI to be responsible for this step. BmmI could specifically attach C3-C5 alkyl acid thioesters onto 7-OH of MLN A and also exhibits substrate promiscuity toward acyl acceptors with different scaffolds. The crystal structure of BmmI covalently linked to the succinyl group and systematic mutagenesis highlighted the role of oxyanion holelike geometry in the recognition of carboxyl-terminated acyl donors. The engineering of this geometry expanded its substrate scope, with the R166A/G/Q variants recognizing up to C12 alkyl acid thioester. The structure of BmmI with acyl acceptor MLN A revealed the importance of Arg292 in the recognition of macrolide substrates. Moreover, the mechanism of the BmmI-catalyzed acyltransfer reaction was established, unmasking the deft role of Lys76 in governing acyl donors as well as catalysis. Our studies uncover the delicate mechanism underlying the substrate selectivity of acyltransferases, which would guide rational enzyme engineering for drug development.

On the Catalytic Activity of the Engineered Coiled-Coil Heptamer Mimicking the Hydrolase Enzymes: Insights from a Computational Study

Recently major advances were gained on the designed proteins aimed to generate biomolecular mimics of proteases. Although such enzyme-like catalysts must still suffer refinements for improving the catalytic activity, at the moment, they represent a good example of artificial enzymes to be tested in different fields. Herein, a de novo designed homo-heptameric peptide assembly (CC-Hept) where the esterase activity towards p-nitro-phenylacetate was obtained for introduction of the catalytic triad (Cys-His-Glu) into the hydrophobic matrix, is the object of the present combined molecular dynamics and quantum mechanics/molecular mechanics investigation. Constant pH Molecular Dynamics simulations on the apoform of CC-Hept suggested that the Cys residues are present in the protonated form. Molecular dynamics (MD) simulations of the enzyme-substrate complex evidenced the attitude of the enzyme-like system to retain water molecules, necessary in the hydrolytic reaction, in correspondence of the active site, represented by the Cys-His-Glu triad on each of the seven chains, without significant structural perturbations. A detailed reaction mechanism of esterase activity of CC-Hept-Cys-His-Glu was investigated on the basis of the quantum mechanics/molecular mechanics calculations employing a large quantum mechanical (QM) region of the active site. The proposed mechanism is consistent with available esterases kinetics and structural data. The roles of the active site residues were also evaluated. The deacylation phase emerged as the rate-determining step, in agreement with esterase activity of other natural proteases.

How the Local Environment of Functional Sites Regulates Protein Function

Proteins form complex biological machineries whose functions in the cell are highly regulated at both the cellular and molecular levels. Cellular regulation of protein functions involves differential gene expressions, post-translation modifications, and signaling cascades. Molecular regulation, on the other hand, involves tuning an optimal local protein environment for the functional site. Precisely how a protein achieves such an optimal environment around a given functional site is not well understood. Herein, by surveying the literature, we first summarize the various reported strategies used by certain proteins to ensure their correct functioning. We then formulate three key physicochemical factors for regulating a protein's functional site, namely, (i) its immediate interactions, (ii) its solvent accessibility, and (iii) its conformational flexibility. We illustrate how these factors are applied to regulate the functions of free/metal-bound Cys and Zn sites in proteins.

A multifunctional surfactant catalyst inspired by hydrolases

The remarkable power of enzymes to undertake catalysis frequently stems from their grouping of multiple, complementary chemical units within close proximity around the enzyme active site. Motivated by this, we report here a bioinspired surfactant catalyst that incorporates a variety of chemical functionalities common to hydrolytic enzymes. The textbook hydrolase active site, the catalytic triad, is modeled by positioning the three groups of the triad (-OH, -imidazole, and -CO₂H) on a single, trifunctional surfactant molecule. To support this, we recreate the hydrogen bond donating arrangement of the oxyanion hole by imparting surfactant functionality to a guanidinium headgroup. Self-assembly of these amphiphiles in solution drives the collection of functional headgroups into close proximity around a hydrophobic nano-environment, affording hydrolysis of a model ester at rates that challenge α-chymotrypsin. Structural assessment via NMR and XRD, paired with MD simulation and QM calculation, reveals marked similarities of the co-micelle catalyst to native enzymes.

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.

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.

Design and evolution of an enzyme with a non-canonical organocatalytic mechanism

The combination of computational design and laboratory evolution is a powerful and potentially versatile strategy for the development of enzymes with new functions^1-4. However, the limited functionality presented by the genetic code restricts the range of catalytic mechanisms that are accessible in designed active sites. Inspired by mechanistic strategies from small-molecule organocatalysis⁵, here we report the generation of a hydrolytic enzyme that uses N_δ-methylhistidine as a non-canonical catalytic nucleophile. Histidine methylation is essential for catalytic function because it prevents the formation of unreactive acyl-enzyme intermediates, which has been a long-standing challenge when using canonical nucleophiles in enzyme design^6-10. Enzyme performance was optimized using directed evolution protocols adapted to an expanded genetic code, affording a biocatalyst capable of accelerating ester hydrolysis with greater than 9,000-fold increased efficiency over free N_δ-methylhistidine in solution. Crystallographic snapshots along the evolutionary trajectory highlight the catalytic devices that are responsible for this increase in efficiency. N_δ-methylhistidine can be considered to be a genetically encodable surrogate of the widely employed nucleophilic catalyst dimethylaminopyridine¹¹, and its use will create opportunities to design and engineer enzymes for a wealth of valuable chemical transformations.