Computational design gains momentum in enzyme catalysis engineering

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.

Rational Design of Anti-Angiogenic Peptides to Inhibit VEGF/VEGFR2 Interactions for Cancer Therapeutics

BACKGROUND

Angiogenesis is a critical physiological process that plays a key role in tumor progression, metastatic dissemination, and invasion. In the last two decades, the vascular endothelial growth factor (VEGF) signaling pathway has been the area of extensive researches. VEGF executes its special effects by binding to vascular endothelial growth factor receptors (VEGFRs), particularly VEGFR-2.

OBJECTIVE

The inhibition of VEGF/VEGFR2 interaction is known as an effective cancer therapy strategy. The current study pointed to design and model an anti-VEGF peptide based on VEGFR2 binding regions.

METHOD

The large-scale peptide mutation screening was used to achieve a potent peptide with high binding affinity to VEGF for possible application in inhibition of VEGF/VEGFR2 interaction. The AntiCP and Peptide Ranker servers were used to generate the possible peptides library with anticancer activities and prediction of peptides bioactivity. Then, the interaction of VEGF and all library peptides were analyzed using Hex 8.0.0 and ClusPro tools. A number of six peptides with favorable docking scores were achieved. All of the best docking scores of peptides in complexes with VEGF were evaluated to confirm their stability, using molecular dynamics simulation (MD) with the help of the GROMACS software package.

RESULTS

As a result, two antiangiogenic peptides with 13 residues of PepA (NGIDFNRDFFLGL) and PepC (NGIDFNRDKFLFL) were achieved and introduced to inhibit VEGF/VEGFR2 interactions.

CONCLUSIONS

In summary, this study provided new insights into peptide-based therapeutics development for targeting VEGF signaling pathway in tumor cells. PepA and PepC are recommended as potentially promising anticancer agents for further experimental evaluations.

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.

Computational reverse engineering of the lipase from Pseudomonas aeruginosa PAO1: α-helices

Lipases are important enzymes in many biochemical industries, thus making them attractive targets for protein engineering to improve enzymatic properties. In this work, a ''reverse engineering'' approach was explored: disrupt secondary structures to determine their contribution to enzyme stability and activity. All the α-helices of the lipase from Pseudomonas aeruginosa PAO1 (PAL) were systematically disrupted using computational proline mutagenesis and molecular dynamics (MD) simulations. This method identified the α3 mutant (R89P), located within the vicinity of the active site, to be significantly important for stability and activity. In addition, the α6 system (L159P), part of the ''cap'' domain that regulates substrate entry into the active site, was found to be critical for activity as it pushed the lipase to adopt a completely closed conformation. The perturbation introduced by the proline mutations resulted in increased backbone flexibility that significantly decreased protein stability. Moreover, mutations within the cap domain helices - α4 (A115P), α5 (S132P, G139P), α6 (L159P), and α7 (R169P) - resulted in increased flexibility of the N-terminal region of the α5 helix, the mobile ''lid'' helix, that pushes the gorge into a partially closed conformation. The α6 mutation (L159P) further increased the flexibility of the helix-loop region at the C-terminal end of the α5 helix to push the lid into the fully closed state. Therefore, the α3 and α6 helices could be ''hot spots'' for stabilizing mutations that could improve the overall enzyme stability and activity this lipase. The insights obtained in this work may be validated experimentally in future works.

Comparative bioinformatic and structural analyses of pepsin and renin

Pepsin, the archetypal pepsin-like aspartic protease, is irreversibly denatured when exposed to neutral pH conditions whereas renin, a structural homologue of pepsin, is fully stable and optimally active in the same conditions despite sharing highly similar enzyme architecture. To gain insight into the structural determinants of differential aspartic protease pH stability, the present study used comparative bioinformatic and structural analyses. In pepsin, an abundance of polar and aspartic acid residues were identified, a common trait with other acid-stable enzymes. Conversely, renin was shown to have increased levels of basic amino acids. In both pepsin and renin, the solvent exposure of these charged groups was high. Having similar overall acidic residue content, the solvent-exposed basic residues may allow for extensive salt bridge formation in renin, whereas in pepsin, these residues are protonated and serve to form stabilizing hydrogen bonds at low pH. Relative differences in structure and sequence in the turn and joint regions of the β-barrel and ψ-loop in both the N- and C-terminal lobes were identified as regions of interest in defining divergent pH stability. Compared to the structural rigidity of renin, pepsin has more instability associated with the N-terminus, specifically the B/C connector. By contrast, renin exhibits greater C-terminal instability in turn and connector regions. Overall, flexibility differences in connector regions, and amino acid composition, particularly in turn and joint regions of the β-barrel and ψ-loops, likely play defining roles in determining pH stability for renin and pepsin.

Bottom-Up Nonempirical Approach To Reducing Search Space in Enzyme Design Guided by Catalytic Fields

Currently developed protocols of theozyme design still lead to biocatalysts with much lower catalytic activity than enzymes existing in nature, and, so far, the only avenue of improvement was the in vitro laboratory-directed evolution (LDE) experiments. In this paper, we propose a different strategy based on "reversed" methodology of mutation prediction. Instead of common "top-down" approach, requiring numerous assumptions and vast computational effort, we argue for a "bottom-up" approach that is based on the catalytic fields derived directly from transition state and reactant complex wave functions. This enables direct one-step determination of the general quantitative angular characteristics of optimal catalytic site and simultaneously encompasses both the transition-state stabilization (TSS) and ground-state destabilization (GSD) effects. We further extend the static catalytic field approach by introducing a library of atomic multipoles for amino acid side-chain rotamers, which, together with the catalytic field, allow one to determine the optimal side-chain orientations of charged amino acids constituting the elusive structure of a preorganized catalytic environment. Obtained qualitative agreement with experimental LDE data for Kemp eliminase KE07 mutants validates the proposed procedure, yielding, in addition, a detailed insight into possible dynamic and epistatic effects.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

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.

Computational design of Bacillus licheniformis RN-01 levansucrase for control of the chain length of levan-type fructooligosaccharides

Levansucrase (LS) from Gram-positive bacteria generally produces a large quantity of levan polymer, a polyfructose with glucose at the end (GF_n) but a small quantity of levan-type fructooligosaccharides (LFOs). The properties of levan and LFOs depend on their chain lengths, thereby determining their potential applications in food and pharmaceutical industries such as prebiotics and anti-tumor agents. Therefore, an ability to redesign and engineer the active site of levansucrase for synthesis of products with desired degree of polymerization (DP) is very beneficial. We employed computational protein design, docking and molecular dynamics to redesign and engineer the active site of Bacillus licheniformis RN-01 levansucrase for production of LFOs with DP up to five (GF₄), using two approaches: 1) blocking oligosaccharide binding track of GF₃-LS complex with large aromatic residues and 2) eliminating hydrogen bond interactions between terminal glucose of GF₄ and side chains of binding residues of GF₄-LS complex. The designed enzymes and their product patterns from these two approaches were experimentally characterized. The experimental results show that the first approach was successful in creating N251W and N251W/K372Y mutants that synthesized LFOs with DP up to five. This work illustrates how computer-aided approaches can offer novel opportunities to engineer enzymes for desired products.

Bioinformatic analysis of the fold type I PLP-dependent enzymes reveals determinants of reaction specificity in l-threonine aldolase from Aeromonas jandaei

Understanding the role of specific amino acid residues in the molecular mechanism of a protein's function is one of the most challenging problems in modern biology. A systematic bioinformatic analysis of protein families and superfamilies can help in the study of structure–function relationships and in the design of improved variants of enzymes/proteins, but represents a methodological challenge. The pyridoxal‐5′‐phosphate (PLP)‐dependent enzymes are catalytically diverse and include the aspartate aminotransferase superfamily which implements a common structural framework known as type fold I. In this work, the recently developed bioinformatic online methods Mustguseal and Zebra were used to collect and study a large representative set of the aspartate aminotransferase superfamily with high structural, but low sequence similarity to l‐threonine aldolase from Aeromonas jandaei (LTAaj), in order to identify conserved positions that provide general properties in the superfamily, and to reveal family‐specific positions (FSPs) responsible for functional diversity. The roles of the identified residues in the catalytic mechanism and reaction specificity of LTAaj were then studied by experimental site‐directed mutagenesis and molecular modelling. It was shown that FSPs determine reaction specificity by coordinating the PLP cofactor in the enzyme's active centre, thus influencing its activation and the tautomeric equilibrium of the intermediates, which can be used as hotspots to modulate the protein's functional properties. Mutagenesis at the selected FSPs in LTAaj led to a reduction in a native catalytic activity and increased the rate of promiscuous reactions. The results provide insight into the structural basis of catalytic promiscuity of the PLP‐dependent enzymes and demonstrate the potential of bioinformatic analysis in studying structure–function relationship in protein superfamilies.

Rational and Semirational Protein Design

This mini review gives an overview over different design approaches and methodologies applied in rational and semirational enzyme engineering. The underlying principles for engineering novel activities, enantioselectivity, substrate specificity, stability, and pH optimum are summarized.

A Computational Library Design Protocol for Rapid Improvement of Protein Stability: FRESCO

The ability to stabilize enzymes and other proteins has wide-ranging applications. Most protocols for enhancing enzyme stability require multiple rounds of high-throughput screening of mutant libraries and provide only modest improvements of stability. Here, we describe a computational library design protocol that can increase enzyme stability by 20-35 °C with little experimental screening, typically fewer than 200 variants. This protocol, termed FRESCO, scans the entire protein structure to identify stabilizing disulfide bonds and point mutations, explores their effect by molecular dynamics simulations, and provides mutant libraries with variants that have a good chance (>10%) to exhibit enhanced stability. After experimental verification, the most effective mutations are combined to produce highly robust enzymes.

Exploring the Development of Ground-State Destabilization and Transition-State Stabilization in Two Directed Evolution Paths of Kemp Eliminases

Computer-aided enzyme design presents a major challenge since in most cases it has not resulted in an impressive catalytic power. The reasons for the problems with computational design include the use of nonquantitative approaches, but they may also reflect other difficulties that are not completely obvious. Thus, it is very useful to try to learn from the trend in directed evolution experiments. Here we explore the nature of the refinement of Kemp eliminases by directed evolution, trying to gain an understanding of related requirements from computational design. The observed trend in the directed evolution refinement of KE07 and HG3 are reproduced, showing that in the case of KE07 the directed evolution leads to ground-state destabilization, whereas in the case of HG3 the directed evolution leads to transition-state stabilization. The nature of the different paths of the directed evolution is examined and discussed. The present study seems to indicate that computer-aided enzyme design may require more than calculations of the effect of single mutations and should be extended to calculations of the effect of simultaneous multiple mutations (that make a few residues preorganized effectively). However, the analysis of two known evolution paths can still be accomplished using the relevant sequences and structures. Thus, by comparing two directed evolution paths of Kemp eliminases we reached the important conclusion that the more effective path leads to transition-state stabilization.

Extending enzyme molecular recognition with an expanded amino acid alphabet

Natural enzymes are constructed from the 20 proteogenic amino acids, which may then require posttranslational modification or the recruitment of coenzymes or metal ions to achieve catalytic function. Here, we demonstrate that expansion of the alphabet of amino acids can also enable the properties of enzymes to be extended. A chemical mutagenesis strategy allowed a wide range of noncanonical amino acids to be systematically incorporated throughout an active site to alter enzymic substrate specificity. Specifically, 13 different noncanonical side chains were incorporated at 12 different positions within the active site of N-acetylneuraminic acid lyase (NAL), and the resulting chemically modified enzymes were screened for activity with a range of aldehyde substrates. A modified enzyme containing a 2,3-dihydroxypropyl cysteine at position 190 was identified that had significantly increased activity for the aldol reaction of erythrose with pyruvate compared with the wild-type enzyme. Kinetic investigation of a saturation library of the canonical amino acids at the same position showed that this increased activity was not achievable with any of the 20 proteogenic amino acids. Structural and modeling studies revealed that the unique shape and functionality of the noncanonical side chain enabled the active site to be remodeled to enable more efficient stabilization of the transition state of the reaction. The ability to exploit an expanded amino acid alphabet can thus heighten the ambitions of protein engineers wishing to develop enzymes with new catalytic properties.

The Framework of Computational Protein Design

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein-protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Minimalist Design of Allosterically Regulated Protein Catalysts

Nature facilitates chemical transformations with exceptional selectivity and efficiency. Despite a tremendous progress in understanding and predicting protein function, the overall problem of designing a protein catalyst for a given chemical transformation is far from solved. Over the years, many design techniques with various degrees of complexity and rational input have been developed. Minimalist approach to protein design that focuses on the bare minimum requirements to achieve activity presents several important advantages. By focusing on basic physicochemical properties and strategic placing of only few highly active residues one can feasibly evaluate in silico a very large variety of possible catalysts. In more general terms minimalist approach looks for the mere possibility of catalysis, rather than trying to identify the most active catalyst possible. Even very basic designs that utilize a single residue introduced into nonenzymatic proteins or peptide bundles are surprisingly active. Because of the inherent simplicity of the minimalist approach computational tools greatly enhance its efficiency. No complex calculations need to be set up and even a beginner can master this technique in a very short time. Here, we present a step-by-step protocol for minimalist design of functional proteins using basic, easily available, and free computational tools.

Design of Catalytic Peptides and Proteins Through Rational and Combinatorial Approaches

This review focuses on recent progress in noncomputational methods to introduce catalytic function into proteins, peptides, and peptide assemblies. We discuss various approaches to creating catalytic activity and classification of noncomputational methods into rational and combinatorial classes. The section on rational design covers recent progress in the development of short peptides and oligomeric peptide assemblies for various natural and unnatural reactions. The section on combinatorial design describes recent advances in the discovery of catalytic peptides. We present the future prospects of these and other new approaches in a broader context, including implications for functional material design.

New Tricks for Old Proteins: Single Mutations in a Nonenzymatic Protein Give Rise to Various Enzymatic Activities

Design of a new catalytic function in proteins, apart from its inherent practical value, is important for fundamental understanding of enzymatic activity. Using a computationally inexpensive, minimalistic approach that focuses on introducing a single highly reactive residue into proteins to achieve catalysis we converted a 74-residue-long C-terminal domain of calmodulin into an efficient esterase. The catalytic efficiency of the resulting stereoselective, allosterically regulated catalyst, nicknamed AlleyCatE, is higher than that of any previously reported de novo designed esterases. The simplicity of our design protocol should complement and expand the capabilities of current state-of-art approaches to protein design. These results show that even a small nonenzymatic protein can efficiently attain catalytic activities in various reactions (Kemp elimination, ester hydrolysis, retroaldol reaction) as a result of a single mutation. In other words, proteins can be just one mutation away from becoming entry points for subsequent evolution.

Computational strategies for the design of new enzymatic functions

In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and Retro-Aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

On the challenge of exploring the evolutionary trajectory from phosphotriesterase to arylesterase using computer simulations

The ability to design effective enzymes presents a fundamental challenge in biotechnology and also in biochemistry. Unfortunately, most of the progress on this field has been accomplished by bringing the reactants to a reasonable orientation relative to each other, rather than by rational optimization of the polar preorganization of the environment, which is the most important catalytic factor. True computer based enzyme design would require the ability to evaluate the catalytic power of designed active sites. This work considers the evolution from a phosphotriesterase (with the paraoxon substrate) to arylesterase (with the 2-naphthylhexanoate (2NH) substrate) catalysis. Both the original and the evolved enzymes involve two zinc ions and their ligands, making it hard to obtain a reliable quantum mechanical description and then to obtain an effective free energy sampling. Furthermore, the options for the reaction path are quite complicated. To progress in this direction we started with DFT calculations of the energetics of different mechanistic options of cluster models and then used the results to calibrate empirical valence bond (EVB) models and to generate properly sampled free energy surfaces for different mechanisms in the enzyme. Interestingly, it is found that the catalytic effect depends on the Zn-Zn distance making the mechanistic analysis somewhat complicated. Comparing the activation barriers of paraoxon and the 2NH ester at the beginning and end of the evolutionary path reproduced the observed evolutionary trend. However, although our findings provide an advance in exploring the nature of promiscuous enzymes, they also indicate that modeling the reaction mechanism in the case of enzymes with a binuclear zinc center is far from trivial and presents a challenge for computer-aided enzyme design.

Design of allosterically regulated protein catalysts

Activity of allosteric protein catalysts is regulated by an external stimulus, such as protein or small molecule binding, light activation, pH change, etc., at a location away from the active site of the enzyme. Since its original introduction in 1961, the concept of allosteric regulation has undergone substantial expansion, and many, if not most, enzymes have been shown to possess some degree of allosteric regulation. The ability to create new catalysts that can be turned on and off using allosteric interactions would greatly expand the chemical biology toolbox and will allow for detection of environmental pollutants and disease biomarkers and facilitate studies of cellular processes and metal homeostasis. Thus, design of allosterically regulated protein catalysts represents an actively growing area of research. In this paper, we describe various approaches to achieving regulation of catalysis.

Enantioselective enzymes by computational design and in silico screening

Computational enzyme design holds great promise for providing new biocatalysts for synthetic chemistry. A strategy to design small mutant libraries of complementary enantioselective epoxide hydrolase variants for the production of highly enantioenriched (S,S)-diols and (R,R)-diols is developed. Key features of this strategy (CASCO, catalytic selectivity by computational design) are the design of mutations that favor binding of the substrate in a predefined orientation, the introduction of steric hindrance to prevent unwanted substrate binding modes, and ranking of designs by high-throughput molecular dynamics simulations. Using this strategy we obtained highly stereoselective mutants of limonene epoxide hydrolase after experimental screening of only 37 variants. The results indicate that computational methods can replace a substantial amount of laboratory work when developing enantioselective enzymes.

De novo design of stereochemically-bent sixteen-residue β-hairpin as a hydrolase mimic

Stepwise design of sixteen-residue β-hairpin as a hydrolase mimic involving fold design by stereochemical mutation followed by inverse-design of sequence.

Synthetic biology outside the cell: linking computational tools to cell-free systems

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

Computer aided enzyme design and catalytic concepts

Gaining a deeper understanding of enzyme catalysis is of great practical and fundamental importance. Over the years it has become clear that despite advances made in experimental mutational studies, a quantitative understanding of enzyme catalysis will not be possible without the use of computer modeling approaches. While we believe that electrostatic preorganization is by far the most important catalytic factor, convincing the wider scientific community of this may require the demonstration of effective rational enzyme design. Here we make the point that the main current advances in enzyme design are basically advances in directed evolution and that computer aided enzyme design must involve approaches that can reproduce catalysis in well-defined test cases. Such an approach is provided by the empirical valence bond method.

Computational library design for increasing haloalkane dehalogenase stability

We explored the use of a computational design framework for the stabilization of the haloalkane dehalogenase LinB. Energy calculations, disulfide bond design, molecular dynamics simulations, and rational inspection of mutant structures predicted many stabilizing mutations. Screening of these in small mutant libraries led to the discovery of seventeen point mutations and one disulfide bond that enhanced thermostability. Mutations located in or contacting flexible regions of the protein had a larger stabilizing effect than mutations outside such regions. The combined introduction of twelve stabilizing mutations resulted in a LinB mutant with a 23 °C increase in apparent melting temperature (Tm,app , 72.5 °C) and an over 200-fold longer half-life at 60 °C. The most stable LinB variants also displayed increased compatibility with co-solvents, thus allowing substrate conversion and kinetic resolution at much higher concentrations than with the wild-type enzyme.

Computationally designed libraries for rapid enzyme stabilization

The ability to engineer enzymes and other proteins to any desired stability would have wide-ranging applications. Here, we demonstrate that computational design of a library with chemically diverse stabilizing mutations allows the engineering of drastically stabilized and fully functional variants of the mesostable enzyme limonene epoxide hydrolase. First, point mutations were selected if they significantly improved the predicted free energy of protein folding. Disulfide bonds were designed using sampling of backbone conformational space, which tripled the number of experimentally stabilizing disulfide bridges. Next, orthogonal in silico screening steps were used to remove chemically unreasonable mutations and mutations that are predicted to increase protein flexibility. The resulting library of 64 variants was experimentally screened, which revealed 21 (pairs of) stabilizing mutations located both in relatively rigid and in flexible areas of the enzyme. Finally, combining 10-12 of these confirmed mutations resulted in multi-site mutants with an increase in apparent melting temperature from 50 to 85°C, enhanced catalytic activity, preserved regioselectivity and a >250-fold longer half-life. The developed Framework for Rapid Enzyme Stabilization by Computational libraries (FRESCO) requires far less screening than conventional directed evolution.

Computational tools for designing and engineering enzymes

Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution.