Computational Design of Synthetic Enzymes

Data-informed discovery of hydrolytic nanozymes

Nanozyme is a collection of nanomaterials with enzyme-like activity but higher environmental tolerance and long-term stability than their natural counterparts. Improving the catalytic activity and expanding the category of nanozymes are prerequisites to complement or even supersede enzymes. However, the development of hydrolytic nanozymes is still challenged by diverse hydrolytic substrates and following complicated mechanisms. Here, two strategies are informed by data to screen and predict catalytic active sites of MOF (metal-organic framework) based hydrolytic nanozymes: (1) to increase the intrinsic activity by finely tuned Lewis acidity of the metal clusters; (2) to improve the density of active sites by shortening the length of ligands. Finally, as-obtained Ce-FMA-MOF-based hydrolytic nanozyme is capable of cleaving phosphate bonds, amide bonds, glycosidic bonds, and even their mixture, biofilms. This work provides a rational methodology to design hydrolytic nanozyme, enriches the diversity of nanozymes, and potentially sheds light on future evolution of enzyme engineering.

From Acid Activation Mechanisms of Proton Conduction to Design of Inhibitors of the M2 Proton Channel of Influenza A Virus

With an estimated 1 billion people affected across the globe, influenza is one of the most serious health concerns worldwide. Therapeutic treatments have encompassed a number of key functional viral proteins, mainly focused on the M2 proton channel and neuraminidase. This review highlights the efforts spent in targeting the M2 proton channel, which mediates the proton transport toward the interior of the viral particle as a preliminary step leading to the release of the fusion peptide in hemagglutinin and the fusion of the viral and endosomal membranes. Besides the structural and mechanistic aspects of the M2 proton channel, attention is paid to the challenges posed by the development of efficient small molecule inhibitors and the evolution toward novel ligands and scaffolds motivated by the emergence of resistant strains.

Can electric fields drive chemistry for an aqueous microdroplet?

Reaction rates of common organic reactions have been reported to increase by one to six orders of magnitude in aqueous microdroplets compared to bulk solution, but the reasons for the rate acceleration are poorly understood. Using a coarse-grained electron model that describes structural organization and electron densities for water droplets without the expense of ab initio methods, we investigate the electric field distributions at the air-water interface to understand the origin of surface reactivity. We find that electric field alignments along free O-H bonds at the surface are ~16 MV/cm larger on average than that found for O-H bonds in the interior of the water droplet. Furthermore, electric field distributions can be an order of magnitude larger than the average due to non-linear coupling of intramolecular solvent polarization with intermolecular solvent modes which may contribute to even greater surface reactivity for weakening or breaking chemical bonds at the droplet surface.

Modern strategies for the diversification of the supply of natural compounds - the case of alkaloid painkillers

Plant-derived natural compounds are used for treating diseases since the beginning of humankind. The supply of many plant-derived natural compounds for medicinal purposes, such as thebaine, morphine, and codeine, is, nowadays, majorly dependent on opium poppy crop harvesting. This dependency puts an extra risk factor in ensuring the supply chain because crops are highly susceptible to environmental factors. Emerging technologies, such as biocatalysis, might help to solve this problem, by diversifying the sources of supply of these compounds. Here we review the first committed step in the production of alkaloid painkillers, the production of S-norcoclaurine, and the enzymes involved. The improvement of these enzymes can be carried out by experimental directed evolution and rational design strategies, supported by computational methods, to create variants that produce the S-norcoclaurine precursor for alkaloid painkillers in heterologous organisms, meeting the pharmaceutical industry standards and needs without depending on opium poppy crops.

Current and emerging tools of computational biology to improve the detoxification of mycotoxins

Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we will focus on mycotoxins-fungi-produced toxins that contaminate food and feed-and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

An electrostatic duel: subtle differences in the catalytic performance of monoamine oxidase A and B isoenzymes elucidated at the residue level using quantum computations

The origin of the immense catalytic power of enzymes remains one of the biggest unresolved questions in biochemistry, with electrostatics being one of the main contenders. Herein, we report results that not only confirm that electrostatics is the driving force behind enzyme catalysis, but also that it is capable of tuning subtle differences in the catalytic performance between structurally similar enzymes, as demonstrated using the example of isoenzymes, monoamine oxidases A and B. Using our own computationally efficient multiscale model [A. Prah, et al., ACS Catal., 2019, 9, 1231] we analyzed the rate-limiting step of the reaction between phenylethylamine and both isoenzymes and deduced that the electrostatic environment provided by isoenzyme B has a perceivably higher catalytic influence on all the considered parameters of the reaction (energy barrier, charge transfer, dipole moment, and HOMO-LUMO gap). This is in full agreement with the available experimental kinetic data and with our own simulations of the reaction in question. In-depth analysis of individual amino acid contributions of both isoenzymes to the barrier (based on the interaction between the electric field provided by the enzyme and the dipole moment of the reacting moiety) shows that the majority of the difference between the isoenzymes can be attributed to a small number of sizable differences between the aligned amino acid pairs, whereas in most of the pairs the difference in contribution to the barrier is vanishingly small. These results suggest that electrostatics largely controls the substrate selectivity of enzymes and validates our approach as being capable of discerning fine nuances in the selectivity of structurally related isoenzymes.

Chemical modification of enzymes to improve biocatalytic performance

Improvement in intrinsic enzymatic features is in many instances a prerequisite for the scalable applicability of many industrially important biocatalysts. To this end, various strategies of chemical modification of enzymes are maturing and now considered as a distinct way to improve biocatalytic properties. Traditional chemical modification methods utilize reactivities of amine, carboxylic, thiol and other side chains originating from canonical amino acids. On the other hand, noncanonical amino acid- mediated 'click' (bioorthogoal) chemistry and dehydroalanine (Dha)-mediated modifications have emerged as an alternate and promising ways to modify enzymes for functional enhancement. This review discusses the applications of various chemical modification tools that have been directed towards the improvement of functional properties and/or stability of diverse array of biocatalysts.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

Globally Optimized Molecular Embeddings for Dynamic Reaction Solvate Shell Optimization and Active Site Design

We demonstrate how a full QM/MM derivatization of the recently developed GOCAT model can be utilized in the global optimization of molecular embeddings. To this end, we provide two distinct examples: An $$\text {S}_\text {N}2$$ S N 2 reaction, and one enzymatic example of recent interest, the ketosteroid isomerase. These serve us to highlight the advantages of such an approach and sketch the roadmap for further improvements.

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.

Graph transformation for enzymatic mechanisms

MOTIVATION

The design of enzymes is as challenging as it is consequential for making chemical synthesis in medical and industrial applications more efficient, cost-effective and environmentally friendly. While several aspects of this complex problem are computationally assisted, the drafting of catalytic mechanisms, i.e. the specification of the chemical steps-and hence intermediate states-that the enzyme is meant to implement, is largely left to human expertise. The ability to capture specific chemistries of multistep catalysis in a fashion that enables its computational construction and design is therefore highly desirable and would equally impact the elucidation of existing enzymatic reactions whose mechanisms are unknown.

RESULTS

We use the mathematical framework of graph transformation to express the distinction between rules and reactions in chemistry. We derive about 1000 rules for amino acid side chain chemistry from the M-CSA database, a curated repository of enzymatic mechanisms. Using graph transformation, we are able to propose hundreds of hypothetical catalytic mechanisms for a large number of unrelated reactions in the Rhea database. We analyze these mechanisms to find that they combine in chemically sound fashion individual steps from a variety of known multistep mechanisms, showing that plausible novel mechanisms for catalysis can be constructed computationally.

AVAILABILITY AND IMPLEMENTATION

The source code of the initial prototype of our approach is available at https://github.com/Nojgaard/mechsearch.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-Level Production of Lysine in the Yeast Saccharomyces cerevisiae by Rational Design of Homocitrate Synthase

Homocitrate synthase (HCS) catalyzes the aldol condensation of 2-oxoglutarate (2-OG) and acetyl coenzyme A (AcCoA) to form homocitrate, which is the first enzyme of the lysine biosynthetic pathway in the yeast Saccharomyces cerevisiae. The HCS activity is tightly regulated via feedback inhibition by the end product lysine. Here, we designed a feedback inhibition-insensitive HCS of S. cerevisiae (ScLys20) for high-level production of lysine in yeast cells. In silico docking of the substrate 2-OG and the inhibitor lysine to ScLys20 predicted that the substitution of serine with glutamate at position 385 would be more suitable for desensitization of the lysine feedback inhibition than the substitution from serine to phenylalanine in the already known Ser385Phe variant. Enzymatic analysis revealed that the Ser385Glu variant is far more insensitive to feedback inhibition than the Ser385Phe variant. We also found that the lysine contents in yeast cells expressing the Ser385Glu variant were 4.62- and 1.47-fold higher than those of cells expressing the wild-type HCS and Ser385Phe variant, respectively, due to the extreme desensitization to feedback inhibition. In this study, we obtained highly feedback inhibition-insensitive HCS using in silico docking and enzymatic analysis. Our results indicate that the rational engineering of HCS for feedback inhibition desensitization by lysine could be useful for constructing new yeast strains with higher lysine productivity. IMPORTANCE A traditional method for screening toxic analogue-resistant mutants has been established for the breeding of microbes that produce high levels of amino acids, including lysine. However, another efficient strategy is required to further improve their productivity. Homocitrate synthase (HCS) catalyzes the first step of lysine biosynthesis in the yeast Saccharomyces cerevisiae, and its activity is subject to feedback inhibition by lysine. Here, in silico design of a key enzyme that regulates the biosynthesis of lysine was utilized to increase the productivity of lysine. We designed HCS for the high-level production of lysine in yeast cells by in silico docking simulation. The engineered HCS exhibited much less sensitivity to lysine and conferred higher production of lysine than the already known variant obtained by traditional breeding. The combination of in silico design and experimental analysis of a key enzyme will contribute to advances in metabolic engineering for the construction of industrial microorganisms.

Future directions of chemical theory and computation

Theoretical and computational chemistry aims to develop chemical theory and to apply numerical computation and simulation to reveal the mechanism behind complex chemical phenomena via quantum theory and statistical mechanics. Computation is the third pillar of scientific research together with theory and experiment. Computation enables scientists to test, discover, and build models/theories of the corresponding chemical phenomena. Theoretical and computational chemistry has been advanced to a new era due to the development of high-performance computational facilities and artificial intelligence approaches. The tendency to merge electronic structural theory with quantum chemical dynamics and statistical mechanics is of increasing interest because of the rapid development of on-the-fly dynamic simulations for complex systems plus low-scaling electronic structural theory. Another challenging issue lies in the transition from order to disorder, from thermodynamics to dynamics, and from equilibrium to non-equilibrium. Despite an increasingly rapid emergence of advances in computational power, detailed criteria for databases, effective data sharing strategies, and deep learning workflows have yet to be developed. Here, we outline some challenges and limitations of the current artificial intelligence approaches with an outlook on the potential future directions for chemistry in the big data era.

Quantification of the Electrostatic Effect on Redox Potential by Positive Charges in a Catalyst Microenvironment

Charged functional groups in the secondary coordination sphere (SCS) of a heterogeneous nanoparticle or homogeneous electrocatalyst are of growing interest due to enhancements in reactivity that derive from specific interactions that stabilize substrate binding or charged intermediates. At the same time, accurate benchmarking of electrocatalyst systems most often depends on the development of linear free-energy scaling relationships. However, the thermodynamic axis in those kinetic-thermodynamic correlations is most often obtained by a direct electrochemical measurement of the catalyst redox potential and might be influenced by electrostatic effects of a charged SCS. In this report, we systematically probe positive charges in a SCS and their electrostatic contributions to the electrocatalyst redox potential. A series of 11 iron carbonyl clusters modified with charged and uncharged ligands was probed, and a linear correlation between the ν_CO absorption band energy and electrochemical redox potentials is observed except where the SCS is positively charged.

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.

Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites

Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).

Recent advances in artificial enzyme cascades for the production of value-added chemicals

Enzyme cascades are efficient tools to perform multi-step synthesis in one-pot in a green and sustainable manner, enabling non-natural synthesis of valuable chemicals from easily available substrates by artificially combining two or more enzymes. Bioproduction of many high-value chemicals such as chiral and highly functionalised molecules have been achieved by developing new enzyme cascades. This review summarizes recent advances on engineering and application of enzyme cascades to produce high-value chemicals (alcohols, aldehydes, ketones, amines, carboxylic acids, etc) from simple starting materials. While 2-step enzyme cascades are developed for versatile enantioselective synthesis, multi-step enzyme cascades are engineered to functionalise basic chemicals, such as styrenes, cyclic alkanes, and aromatic compounds. New cascade reactions have also been developed for producing valuable chemicals from bio-based substrates, such as ʟ-phenylalanine, and renewable feedstocks such as glucose and glycerol. The challenges in current process and future outlooks in the development of enzyme cascades are also addressed.

Substrate promiscuity of a de novo designed peroxidase

The design and construction of de novo enzymes offer potentially facile routes to exploiting powerful chemistries in robust, expressible and customisable protein frameworks, while providing insight into natural enzyme function. To this end, we have recently demonstrated extensive catalytic promiscuity in a heme-containing de novo protein, C45. The diverse transformations that C45 catalyses include substrate oxidation, dehalogenation and carbon‑carbon bond formation. Here we explore the substrate promiscuity of C45's peroxidase activity, screening the de novo enzyme against a panel of peroxidase and dehaloperoxidase substrates. Consistent with the function of natural peroxidases, C45 exhibits a broad spectrum of substrate activities with selectivity dictated primarily by the redox potential of the substrate, and by extension, the active oxidising species in peroxidase chemistry, compounds I and II. Though the comparison of these redox potentials provides a threshold for determining activity for a given substrate, substrate:protein interactions are also likely to play a significant role in determining electron transfer rates from substrate to heme, affecting the kinetic parameters of the enzyme. We also used biomolecular simulation to screen substrates against a computational model of C45 to identify potential interactions and binding sites. Several sites of interest in close proximity to the heme cofactor were discovered, providing insight into the catalytic workings of C45.

Designing better enzymes: Insights from directed evolution

De novo enzymes can be created by computational design and directed evolution. Here, we review recent insights into the origins of catalytic power in evolved designer enzymes to pinpoint opportunities for next-generation designs: Evolution precisely organizes active sites, introduces catalytic H-bonding networks, invokes electrostatic catalysis, and creates dynamical networks embedding the active site in a reactive protein scaffold. Such insights foster our fundamental knowledge of enzyme catalysis and fuel the future design of tailor-made enzymes.

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.

Large-scale production of enzymes for biotechnology uses

Enzymes are biocatalysts that speed up the chemical reaction to obtain the final valuable product/s. Biotechnology has revolutionized the use of traditional enzymes to be applicable in industries such as food, beverage, personal and household care, agriculture, bioenergy, pharmaceutical, and various other segments. With respect to the exponential growth of enzymes in biotech industries, it becomes important to highlight the advancements and impact of enzyme technology over recent years. In this review article, we discuss the existing and emerging production approaches, applications, developments, and global need for enzymes. Special emphasis is given to the predominantly utilized hydrolytic microbial enzymes in industrial bioprocesses.

An isolated water droplet in the aqueous solution of a supramolecular tetrahedral cage

Water under nanoconfinement at ambient conditions has exhibited low-dimensional ice formation and liquid-solid phase transitions, but with structural and dynamical signatures that map onto known regions of water's phase diagram. Using terahertz (THz) absorption spectroscopy and ab initio molecular dynamics, we have investigated the ambient water confined in a supramolecular tetrahedral assembly, and determined that a dynamically distinct network of 9 ± 1 water molecules is present within the nanocavity of the host. The low-frequency absorption spectrum and theoretical analysis of the water in the Ga₄L₆ ^12- host demonstrate that the structure and dynamics of the encapsulated droplet is distinct from any known phase of water. A further inference is that the release of the highly unusual encapsulated water droplet creates a strong thermodynamic driver for the high-affinity binding of guests in aqueous solution for the Ga₄L₆ ^12- supramolecular construct.

External electric field reduces the explosive sensitivity: a theoretical investigation into the hydrogen transference kinetics of the NH2NO2∙∙∙H2O complex

Controlling the selectivity of detonation initiation reaction to reduce the explosive sensitivity has been a Holy Grail in the field of energetic materials. The effects of the external electric fields on the homolysis of the N-NO₂ bond and initiation reaction dynamics of NH₂NO₂∙∙∙H₂O (i.e., intermolecular and 1,3-intramolecular hydrogen transfers) were investigated at the MP2/6-311++G(2d,p) and CCSD/6-311++G(2d,p)//MP2/6-311++G(2d,p) levels. The results show that the N-NO₂ bond is not the "trigger linkage." The notable transiliences of the activation energy of the intermolecular hydrogen transfer are found with the field strength of - 0.012 a.u. along the -x-direction, leading to the conversion of the main reaction between the intermolecular and 1,3-intramolecular hydrogen transference. The activation energies of two kinds of the hydrogen transferences are increased under the external electric fields along the -y-direction. In particular, due to the conversion of the main reaction, the activation energies of the overall reaction are increased significantly along the -x-direction, leading to the significant reduced explosive sensitivities. Therefore, by controlling the field strengths and orientations between the "reaction axis" and external electric field along the y- and x-directions, the selectivity of the initiation reaction could be controlled and the explosive sensitivity could be reduced. Employing AIM (atoms in molecules) and surface electrostatic potentials, the origin of the control of reaction selectivity and the reduction of sensitivity is revealed. This work is of great significance to the improvement of the technology that the external electric fields are added safely into the energetic material system to enhance the explosive performance. Graphical abstract.

Artificial Enzymes for Diels-Alder Reactions

The Diels-Alder (DA) reaction is a cycloaddition of a conjugated diene and an alkene (dienophile) leading to the formation of a cyclohexene derivative through a concerted mechanism. As DA reactions generally proceed with a high degree of regio- and stereoselectivity, they are widely used in synthetic organic chemistry. Considering eco-conscious public and governmental movements, efforts are now directed towards the development of synthetic processes that meet environmental concerns. Artificial enzymes, which can be developed to catalyze abiotic reactions, appear to be important synthetic tools in the synthetic biology field. This review describes the different strategies used to develop protein-based artificial enzymes for DA reactions, including for in cellulo approaches.

Biomaterials based on noncovalent interactions of small molecules

Unlike conventional materials that covalent bonds connecting atoms as the major force to hold the materials together, supramolecular biomaterials rely on noncovalent intermolecular interactions to assemble. The reversibility and biocompatibility of supramolecular biomaterials render them with diverse range of functions and lead to rapid development in the past two decades. This review focuses on the noncovalent and enzymatic control of supramolecular biomaterials, with the introduction to various triggering mechanism to initiate self-assembly. Representative applications of supramolecular biomaterials are highlighted in four categories: tissue engineering, cancer therapy, drug delivery, and molecular imaging. By introducing various applications, we intend to show enzymatic control and noncovalent interactions as a powerful tool for achieving spatiotemporal control of biomaterials both invitro and in vivo for biomedicine.

Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach

The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

Enzymatic Noncovalent Synthesis

Enzymatic reactions and noncovalent (i.e., supramolecular) interactions are two fundamental nongenetic attributes of life. Enzymatic noncovalent synthesis (ENS) refers to a process where enzymatic reactions control intermolecular noncovalent interactions for spatial organization of higher-order molecular assemblies that exhibit emergent properties and functions. Like enzymatic covalent synthesis (ECS), in which an enzyme catalyzes the formation of covalent bonds to generate individual molecules, ENS is a unifying theme for understanding the functions, morphologies, and locations of molecular ensembles in cellular environments. This review intends to provide a summary of the works of ENS within the past decade and emphasize ENS for functions. After comparing ECS and ENS, we describe a few representative examples where nature uses ENS, as a rule of life, to create the ensembles of biomacromolecules for emergent properties/functions in a myriad of cellular processes. Then, we focus on ENS of man-made (synthetic) molecules in cell-free conditions, classified by the types of enzymes. After that, we introduce the exploration of ENS of man-made molecules in the context of cells by discussing intercellular, peri/intracellular, and subcellular ENS for cell morphogenesis, molecular imaging, cancer therapy, and other applications. Finally, we provide a perspective on the promises of ENS for developing molecular assemblies/processes for functions. This review aims to be an updated introduction for researchers who are interested in exploring noncovalent synthesis for developing molecular science and technologies to address societal needs.

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.

Electric-Field Mediated Chemistry: Uncovering and Exploiting the Potential of (Oriented) Electric Fields to Exert Chemical Catalysis and Reaction Control

This Perspective discusses oriented external-electric-fields (OEEF), and other electric-field types, as "smart reagents", which enable in principle control over wide-ranging aspects of reactivity and structure. We discuss the potential of OEEFs to control nonredox reactions and impart rate-enhancement and selectivity. An OEEF along the "reaction axis", which is the direction whereby electronic reorganization converts reactants' to products' bonding, will accelerate reactions, control regioselectivity, induce spin-state selectivity, and elicit mechanistic crossovers. Simply flipping the direction of the OEEF will lead to inhibition. Orienting the OEEF off the reaction axis enables control over stereoselectivity, enantioselectivity, and product selectivity. For polar/polarizable reactants, the OEEF itself will act as tweezers, which orient the reactants and drive their reaction. OEEFs also affect bond-dissociation energies and dissociation modes (covalent vs ionic), as well as alteration of molecular geometries and supramolecular aggregation. The "key" to gaining access to this toolbox provided by OEEFs is microscopic control over the alignment between the molecule and the applied field. We discuss the elegant experimental methods which have been used to verify the theoretical predictions and describe various alternative EEF sources and prospects for upscaling OEEF catalysis in solvents. We also demonstrate the numerous ways in which the OEEF effects can be mimicked by use of (designed) local-electric fields (LEFs), i.e., by embedding charges or dipoles into molecules. LEFs and OEEFs are shown to be equivalent and to obey the same ground rules. Outcomes are exemplified for Diels-Alder cycloadditions, oxidative addition of bonds by transition-metal complexes, H-abstractions by oxo-metal species, ionic cleavage of halogen bonds, methane activation, etc.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (∼2 × 104 M-1 s-1 and ∼102 s-1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ∼2 kcal mol-1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

Manipulation of Diatomic Molecules with Oriented External Electric Fields: Linear Correlations in Atomic Properties Lead to Nonlinear Molecular Responses

Oriented external electric fields (OEEFs) have been shown to have great potential in being able to provide unprecedented control of chemical reactions, catalysis, and selectivity with applications ranging from H₂ storage to molecular machines. We report a theoretical study of the atomic origins of molecular changes because of OEEFs since understanding the characteristics of OEEF-induced couplings between atomic and molecular properties is an important step toward comprehensive understanding of the effects of strong external fields on the molecular structure, stability, and reactivity. We focus on the atomic and molecular (bond) properties of a set of homo- (H₂, N₂, O₂, F₂, and Cl₂) and heterodiatomic (HF, HCl, CO, and NO) molecules under intense external electric fields in the context of quantum theory of atoms in molecules (QTAIM). It is shown that the atomic properties (atomic charges, energies, and localization indices) correlate linearly with the field strengths, but molecular properties (bond length, electron density at the bond critical point, and electron delocalization index) exhibit nonlinear responses to the imposed fields. In particular, the changes in the electron density distribution alter the shapes and locations of the zero-flux surfaces, atomic volumes, atomic electron population, and localization/delocalization indices. The topography and topology of the molecular electrostatic potential undergo dramatic changes. External fields also perturb the covalent-polar-ionic characteristic of the studied chemical bonds, hallmarking the impact of electric fields on the stability and reactivity of chemical compounds. The findings are well-rationalized within the framework of the QTAIM and form a coherent conceptual understanding of these effects in prototypical diatomic molecules.

A protein sequence fitness function for identifying natural and nonnatural proteins

The infinitesimally small sequence space naturally scouted in the millions of years of evolution suggests that the natural proteins are constrained by some functional prerequisites and should differ from randomly generated sequences. We have developed a protein sequence fitness scoring function that implements sequence and corresponding secondary structural information at tripeptide levels to differentiate natural and nonnatural proteins. The proposed fitness function is extensively validated on a dataset of about 210 000 natural and nonnatural protein sequences and benchmarked with existing methods for differentiating natural and nonnatural proteins. The high sensitivity, specificity, and percentage accuracy (0.81%, 0.95%, and 91% respectively) of the fitness function demonstrates its potential application for sampling the protein sequences with higher probability of mimicking natural proteins. Moreover, the four major classes of proteins (α proteins, β proteins, α/β proteins, and α + β proteins) are separately analyzed and β proteins are found to score slightly lower as compared to other classes. Further, an analysis of about 250 designed proteins (adopted from previously reported cases) helped to define the boundaries for sampling the ideal protein sequences. The protein sequence characterization aided by the proposed fitness function could facilitate the exploration of new perspectives in the design of novel functional proteins.

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.

Enabling protein-hosted organocatalytic transformations

In this review, the development of organocatalytic artificial enzymes will be discussed. This area of protein engineering research has underlying importance, as it enhances the biocompatibility of organocatalysis for applications in chemical and synthetic biology research whilst expanding the catalytic repertoire of enzymes. The approaches towards the preparation of organocatalytic artificial enzymes, techniques used to improve their performance (selectivity and reactivity) as well as examples of their applications are presented. Challenges and opportunities are also discussed.

A Critical Evaluation of Vibrational Stark Effect (VSE) Probes with the Local Vibrational Mode Theory

Over the past two decades, the vibrational Stark effect has become an important tool to measure and analyze the in situ electric field strength in various chemical environments with infrared spectroscopy. The underlying assumption of this effect is that the normal stretching mode of a target bond such as CO or CN of a reporter molecule (termed vibrational Stark effect probe) is localized and free from mass-coupling from other internal coordinates, so that its frequency shift directly reflects the influence of the vicinal electric field. However, the validity of this essential assumption has never been assessed. Given the fact that normal modes are generally delocalized because of mass-coupling, this analysis was overdue. Therefore, we carried out a comprehensive evaluation of 68 vibrational Stark effect probes and candidates to quantify the degree to which their target normal vibration of probe bond stretching is decoupled from local vibrations driven by other internal coordinates. The unique tool we used is the local mode analysis originally introduced by Konkoli and Cremer, in particular the decomposition of normal modes into local mode contributions. Based on our results, we recommend 31 polyatomic molecules with localized target bonds as ideal vibrational Stark effect probe candidates.

Enzymes to unravel bioproducts architecture

Enzymes are essential and ubiquitous biocatalysts involved in various metabolic pathways and used in many industrial processes. Here, we reframe enzymes not just as biocatalysts transforming bioproducts but also as sensitive probes for exploring the structure and composition of complex bioproducts, like meat tissue, dairy products and plant materials, in both food and non-food bioprocesses. This review details the global strategy and presents the most recent investigations to prepare and use enzymes as relevant probes, with a focus on glycoside-hydrolases involved in plant deconstruction and proteases and lipases involved in food digestion. First, to expand the enzyme repertoire to fit bioproduct complexity, novel enzymes are mined from biodiversity and can be artificially engineered. Enzymes are further characterized by exploring sequence/structure/dynamics/function relationships together with the environmental factors influencing enzyme interactions with their substrates. Then, the most advanced experimental and theoretical approaches developed for exploring bioproducts at various scales (from nanometer to millimeter) using active and inactive enzymes as probes are illustrated. Overall, combining multimodal and multiscale approaches brings a better understanding of native-form or transformed bioproduct architecture and composition, and paves the way to mainstream the use of enzymes as probes.

Globally optimal catalytic fields for a Diels-Alder reaction

In a previous paper [M. Dittner and B. Hartke, J. Chem. Theory Comput. 14, 3547 (2018)], we introduced a preliminary version of our GOCAT (globally optimal catalyst) concept in which electrostatic catalysts are designed for arbitrary reactions by global optimization of distributed point charges that surround the reaction. In this first version, a pre-defined reaction path was kept fixed. This unrealistic assumption allowed for only small catalytic effects. In the present work, we extend our GOCAT framework by a sophisticated and robust on-the-fly reaction path optimization, plus further concomitant algorithm adaptions. This allows smaller and larger excursions from a pre-defined reaction path under the influence of the GOCAT point-charge surrounding, all the way to drastic mechanistic changes. In contrast to the restricted first GOCAT version, this new version is able to address real-life catalysis. We demonstrate this by applying it to the electrostatic catalysis of a prototypical Diels-Alder reaction. Without using any prior information, this procedure re-discovers theoretically and experimentally established features of electrostatic catalysis of this very reaction, including a field-dependent transition from the synchronous, concerted textbook mechanism to a zwitterionic two-step mechanism, and diastereomeric discrimination by suitable electric field components.