Computational Design of a Biologically Active Enzyme

Cyclization mechanism of monoterpenes catalyzed by monoterpene synthases in dipterocarpaceae

Monoterpenoids are typically present in the secretory tissues of higher plants, and their biosynthesis is catalyzed by the action of monoterpene synthases (MTSs). However, the knowledge about these enzymes is restricted in a few plant species. MTSs are responsible for the complex cyclization of monoterpene precursors, resulting in the production of diverse monoterpene products. These enzymatic reactions are considered exceptionally complex in nature. Therefore, it is crucial to understand the catalytic mechanism of MTSs to elucidate their ability to produce diverse or specific monoterpenoid products. In our study, we analyzed thirteen genomes of Dipterocarpaceae and identified 38 MTSs that generate a variety of monoterpene products. By focusing on four MTSs with different product spectra and analyzing the formation mechanism of acyclic, monocyclic and bicyclic products in MTSs, we observed that even a single amino acid mutation can change the specificity and diversity of MTS products, which is due to the synergistic effect between the shape of the active cavity and the stabilization of carbon-positive intermediates that the mutation changing. Notably, residues N340, I448, and phosphoric acid groups were found to be significant contributors to the stabilization of intermediate terpinyl and pinene cations. Alterations in these residues, either directly or indirectly, can impact the synthesis of single monoterpenes or their mixtures. By revealing the role of key residues in the catalytic process and establishing the interaction model between specific residues and complex monoterpenes in MTSs, it will be possible to reasonably design and engineer different catalytic activities into existing MTSs, laying a foundation for the artificial design and industrial application of MTSs.

Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes

In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.

Variable and Conserved Regions of Secondary Structure in the β-Trefoil Fold: Structure Versus Function

β-trefoil proteins exhibit an approximate C₃ rotational symmetry. An analysis of the secondary structure for members of this diverse superfamily of proteins indicates that it is comprised of remarkably conserved β-strands and highly-divergent turn regions. A fundamental “minimal” architecture can be identified that is devoid of heterogenous and extended turn regions, and is conserved among all family members. Conversely, the different functional families of β-trefoils can potentially be identified by their unique turn patterns (or turn “signature”). Such analyses provide clues as to the evolution of the β-trefoil family, suggesting a folding/stability role for the β-strands and a functional role for turn regions. This viewpoint can also guide de novo protein design of β-trefoil proteins having novel functionality.

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.

Atom Transfer Radical Polymerization for Biorelated Hybrid Materials

Proteins, nucleic acids, lipid vesicles, and carbohydrates are the major classes of biomacromolecules that function to sustain life. Biology also uses post-translation modification to increase the diversity and functionality of these materials, which has inspired attaching various other types of polymers to biomacromolecules. These polymers can be naturally (carbohydrates and biomimetic polymers) or synthetically derived and have unique properties with tunable architectures. Polymers are either grafted-to or grown-from the biomacromolecule's surface, and characteristics including polymer molar mass, grafting density, and degree of branching can be controlled by changing reaction stoichiometries. The resultant conjugated products display a chimerism of properties such as polymer-induced enhancement in stability with maintained bioactivity, and while polymers are most often conjugated to proteins, they are starting to be attached to nucleic acids and lipid membranes (cells) as well. The fundamental studies with protein-polymer conjugates have improved our synthetic approaches, characterization techniques, and understanding of structure-function relationships that will lay the groundwork for creating new conjugated biomacromolecular products which could lead to breakthroughs in genetic and tissue engineering.

Optimization of the Turnover in Artificial Enzymes via Directed Evolution Results in the Coupling of Protein Dynamics to Chemistry

The design of artificial enzymes is an emerging field of research. Although progress has been made, the catalytic proficiency of many designed enzymes is low compared to natural enzymes. Nevertheless, recently Hilvert et al. ( Nat. Chem. 2017, 9, 50-56) created a series of five artificial retro-aldolase enzymes via directed evolution, with the final variant exhibiting a rate comparable to the naturally occurring enzyme fructose 1,6 bisphosphate aldolase. We present a study of this system in atomistic detail that elucidates the effects of mutational changes on the chemical step. Transition path sampling is used to create ensembles of reactive trajectories, and committor analysis is used to identify the stochastic separatrix of each ensemble. The application of committor distribution analysis to constrained trajectories allows the identification of changes in important protein motions coupled to reaction across the generated series of the artificial retro-aldolases. We observed two different reaction mechanisms and analyzed the role of the residues participating in the reaction coordinate of each enzyme. However, only in the most evolved variant we identified a fast motion that promotes catalysis, suggesting that this rate promoting vibration was introduced during directed evolution. This study provides further evidence that protein dynamics must be taken into account in designing efficient artificial enzymes.

Incorporating Fast Protein Dynamics into Enzyme Design: A Proposed Mutant Aromatic Amine Dehydrogenase

In recent years, there has been encouraging progress in the engineering of enzymes that are designed to catalyze reactions not accelerated by natural enzymes. We tested the possibility of reengineering an existing enzyme by introducing a fast protein motion that couples to the reaction. Aromatic amine dehydrogenase is a system that has been shown to use a fast substrate motion as part of the reaction mechanism. We identified a mutation that preserves this fast motion but also introduces a favorable fast motion near the active site that did not exist in the native enzyme. Transition path sampling was used for the analysis of the atomic details of the mechanism.

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.

Probik: Protein Backbone Motion by Inverse Kinematics

To investigate the parameters of the protein design problem that we are exploring in collaboration with biochemists, we have developed a tool that uses inverse kinematics to support moving small fragments of protein backbone, while respecting biochemists' desires to “remain in favorable regions of the Ramachandran plot” and “preserve ideal geometry”. By presenting estimates of derivatives in response to motion, we are able to refine these qualitative desires as we work with our collaborators. We then explore low-dimensional bases to parametrize the space of backbone motions.

PELE: Protein Energy Landscape Exploration. A Novel Monte Carlo Based Technique

Combining protein structure prediction algorithms and Metropolis Monte Carlo techniques, we provide a novel method to explore all-atom energy landscapes. The core of the technique is based on a steered localized perturbation followed by side-chain sampling as well as minimization cycles. The algorithm and its application to ligand diffusion are presented here. Ligand exit pathways are successfully modeled for different systems containing ligands of various sizes:  carbon monoxide in myoglobin, camphor in cytochrome P450cam, and palmitic acid in the intestinal fatty-acid-binding protein. These initial applications reveal the potential of this new technique in mapping millisecond-time-scale processes. The computational cost associated with the exploration is significantly less than that of conventional MD simulations.

Flat-Bottom Strategy for Improved Accuracy in Protein Side-Chain Placements

We present a new strategy for protein side-chain placement that uses flat-bottom potentials for rotamer scoring. The extent of the flat bottom depends on the coarseness of the rotamer library and is optimized for libraries ranging from diversities of 0.2 Å to 5.0 Å. The parameters reported here were optimized for forcefields using Lennard-Jones 12-6 van der Waals potential with DREIDING parameters but are expected to be similar for AMBER, CHARMM, and other forcefields. This Side-Chain Rotamer Excitation Analysis Method is implemented in the SCREAM software package. Similar scoring function strategies should be useful for ligand docking, virtual ligand screening, and protein folding applications.

Designing functional metalloproteins: from structural to catalytic metal sites

Metalloenzymes efficiently catalyze some of the most important and difficult reactions in nature. For many years, coordination chemists have effectively used small molecule models to understand these systems. More recently, protein design has been shown to be an effective approach for mimicking metal coordination environments. Since the first designed proteins were reported, much success has been seen for incorporating metal sites into proteins and attaining the desired coordination environment but until recently, this has been with a lack of significant catalytic activity. Now there are examples of designed metalloproteins that, although not yet reaching the activity of native enzymes, are considerably closer. In this review, we highlight work leading up to the design of a small metalloprotein containing two metal sites, one for structural stability (HgS3) and the other a separate catalytic zinc site to mimic carbonic anhydrase activity (ZnN3O). The first section will describe previous studies that allowed for a high affinity thiolate site that binds heavy metals in a way that stabilizes three-stranded coiled coils. The second section will examine ways of preparing histidine rich environments that lead to metal based hydrolytic catalysts. We will also discuss other recent examples of the design of structural metal sites and functional metalloenzymes. Our work demonstrates that attaining the proper first coordination geometry of a metal site can lead to a significant fraction of catalytic activity, apparently independent of the type of secondary structure of the surrounding protein environment. We are now in a position to begin to meet the challenge of building a metalloenzyme systematically from the bottom-up by engineering and analyzing interactions directly around the metal site and beyond.

Expanded explorations into the optimization of an energy function for protein design

Nature possesses a secret formula for the energy as a function of the structure of a protein. In protein design, approximations are made to both the structural representation of the molecule and to the form of the energy equation, such that the existence of a general energy function for proteins is by no means guaranteed. Here, we present new insights toward the application of machine learning to the problem of finding a general energy function for protein design. Machine learning requires the definition of an objective function, which carries with it the implied definition of success in protein design. We explored four functions, consisting of two functional forms, each with two criteria for success. Optimization was carried out by a Monte Carlo search through the space of all variable parameters. Cross-validation of the optimized energy function against a test set gave significantly different results depending on the choice of objective function, pointing to relative correctness of the built-in assumptions. Novel energy cross terms correct for the observed nonadditivity of energy terms and an imbalance in the distribution of predicted amino acids. This paper expands on the work presented at the 2012 ACM-BCB.

Endowing non-cellulolytic microorganisms with cellulolytic activity aiming for consolidated bioprocessing

With the exhaustion of fossil fuels and with the environmental issues they pose, utilization of abundant lignocellulosic biomass as a feedstock for biofuels and bio-based chemicals has recently become an attractive option. Lignocellulosic biomass is primarily composed of cellulose, hemicellulose, and lignin and has a very rigid and complex structure. It is accordingly much more expensive to process than starchy grains because of the need for extensive pretreatment and relatively large amounts of cellulases for efficient hydrolysis. Efficient and cost-effective methods for the production of biofuels and chemicals from lignocellulose are required. A consolidated bioprocess (CBP), which integrates all biological steps consisting of enzyme production, saccharification, and fermentation, is considered a promising strategy for reducing production costs. Establishing an efficient CBP using lignocellulosic biomass requires both lignocellulose degradation into glucose and efficient production of biofuels or chemicals from glucose. With this aim, many researchers are attempting to endow selected microorganisms with lignocellulose-assimilating ability. In this review, we focus on studies aimed at conferring lignocellulose-assimilating ability not only to yeast strains but also to bacterial strains by recombinant technology. Recent developments in improvement of enzyme productivity by microorganisms and in improvement of the specific activity of cellulase are emphasized.

Energy functions in de novo protein design: current challenges and future prospects

In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.

Computational Enzyme Design: Advances, hurdles and possible ways forward

This mini review addresses recent developments in computational enzyme design. Successful protocols as well as known issues and limitations are discussed from an energetic perspective. It will be argued that improved results can be obtained by including a dynamic treatment in the design protocol. Finally, a molecular dynamics-based approach for evaluating and refining computational designs is presented.

A measure of the promiscuity of proteins and characteristics of residues in the vicinity of the catalytic site that regulate promiscuity

Promiscuity, the basis for the evolution of new functions through 'tinkering' of residues in the vicinity of the catalytic site, is yet to be quantitatively defined. We present a computational method Promiscuity Indices Estimator (PROMISE)--based on signatures derived from the spatial and electrostatic properties of the catalytic residues, to estimate the promiscuity (PromIndex) of proteins with known active site residues and 3D structure. PromIndex reflects the number of different active site signatures that have congruent matches in close proximity of its native catalytic site, the quality of the matches and difference in the enzymatic activity. Promiscuity in proteins is observed to follow a lognormal distribution (μ = 0.28, σ = 1.1 reduced chi-square = 3.0E-5). The PROMISE predicted promiscuous functions in any protein can serve as the starting point for directed evolution experiments. PROMISE ranks carboxypeptidase A and ribonuclease A amongst the more promiscuous proteins. We have also investigated the properties of the residues in the vicinity of the catalytic site that regulates its promiscuity. Linear regression establishes a weak correlation (R(2)∼0.1) between certain properties of the residues (charge, polar, etc) in the neighborhood of the catalytic residues and PromIndex. A stronger relationship states that most proteins with high promiscuity have high percentages of charged and polar residues within a radius of 3 Å of the catalytic site, which is validated using one-tailed hypothesis tests (P-values∼0.05). Since it is known that these characteristics are key factors in catalysis, their relationship with the promiscuity index cross validates the methodology of PROMISE.

Empirical and computational design of iron-sulfur cluster proteins

Here, we compare two approaches of protein design. A computational approach was used in the design of the coiled-coil iron-sulfur protein, CCIS, as a four helix bundle binding an iron-sulfur cluster within its hydrophobic core. An empirical approach was used for designing the redox-chain maquette, RCM as a four-helix bundle assembling iron-sulfur clusters within loops and one heme in the middle of its hydrophobic core. We demonstrate that both ways of design yielded the desired proteins in terms of secondary structure and cofactors assembly. Both approaches, however, still have much to improve in predicting conformational changes in the presence of bound cofactors, controlling oligomerization tendency and stabilizing the bound iron-sulfur clusters in the reduced state. Lessons from both ways of design and future directions of development are discussed. This article is part of a Special Issue entitled: Photosynthesis Research for Sustainability: from Natural to Artificial.

Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives

The development of characterization techniques, advanced synthesis methods, as well as molecular modeling has transformed the study of systems in a well-established research field. The current research challenges in biocatalysis and biotransformation evolve around enzyme discovery, design, and optimization. How can we find or create enzymes that catalyze important synthetic reactions, even reactions that may not exist in nature? What is the source of enzyme catalytic power? To answer these and other related questions, the standard strategies have evolved from trial-and-error methodologies based on chemical knowledge, accumulated experience, and common sense into a clearly multidisciplinary science that allows one to reach the molecular design of tailor-made enzyme catalysts. This is even more so when one refers to enzyme catalysts, for which the detailed structure and composition are known and can be manipulated to introduce well-defined residues which can be implicated in the chemical rearrangements taking place in the active site. The methods and techniques of theoretical and computational chemistry are becoming more and more important in both understanding the fundamental biological roles of enzymes and facilitating their utilization in biotechnology. Improvement of the catalytic function of enzymes is important from scientific and industrial viewpoints, and to put this fact in the actual perspective as well as the potentialities, we recommend the very recent report of Sanderson [Sanderson, K. (2011). Chemistry: enzyme expertise. Nature 471, 397.]. Great fundamental advances have been made toward the ab initio design of enzyme catalysts based on molecular modeling. This has been based on the molecular mechanistic knowledge of the reactions to be catalyzed, together with the development of advanced synthesis and characterization techniques. The corresponding molecular mechanism can be studied by means of powerful quantum chemical calculations. The catalytic active site can be optimized to improve the transition state analogues (TSA) and to enhance the catalytic activity, even improve the active site to favor a desired direction of some promiscuous enzymes. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are described. The interplay between enzyme design, molecular simulations, and experiments will be presented to emphasize the interdisciplinary nature of this research field. This text highlights the recent advances and examples selected from our laboratory are shown, of how the applications of these tools are a first attempt to de novo design of protein active sites. Identification of neutral/advantageous/deleterious mutation platforms can be exploited to penetrate some of Nature's closely guarded secrets of chemical reactivity. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. The first part describes briefly how the molecular modeling is carried out. Then, we discuss the requirements of hybrid quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) simulations, analyzing what are the basis of these theoretical methodologies, how we can use them with a view to its application in the study of enzyme catalysis, and what are the best methodologies for assessing its catalytic potential. In the second part, we focus on some selected examples, taking as a common guide the chorismate to prephenate rearrangement, studying the corresponding molecular mechanism in vacuo, in solution and in an enzyme environment. In addition, examples involving catalytic antibodies (CAs) and promiscuous enzymes will be presented. Finally, a special emphasis is made to provide some hints about the logical evolution that can be anticipated in this research field. Moreover, it helps in understanding the open directions in this area of knowledge and highlights the importance of computational approaches in discovering specific drugs and the impact on the rational design of tailor-made enzymes.

Methyl jasmonate-induced defense responses are associated with elevation of 1-aminocyclopropane-1-carboxylate oxidase in Lycopersicon esculentum fruit

It has been known that methyl jasmonate (MeJA) interacts with ethylene to elicit resistance. In green mature tomato fruits (Lycopersicon esculentum cv. Lichun), 0.02mM MeJA increased the activity of 1-aminocyclopropane-1-carboxylate oxidase (ACO), and consequently influenced the last step of ethylene biosynthesis. Fruits treated with a combination of 0.02 MeJA and 0.02 α-aminoisobutyric acid (AIB, a competitive inhibitor of ACO) exhibited a lower ethylene production comparing to that by 0.02mM MeJA alone. The increased activities of defense enzymes and subsequent control of disease incidence caused by Botrytis cinerea with 0.2mM MeJA treatment was impaired by AIB as well. A close relationship (P<0.05) was found between the activity alterations of ACO and that of chitinase (CHI) and β-1,3-glucanase (GLU). In addition, this study further detected the changes of gene expressions and enzyme kinetics of ACO to different concentrations of MeJA. LeACO1 was found the principal member from the ACO gene family to respond to MeJA. Accumulation of LeACO1/3/4 transcripts followed the concentration pattern of MeJA treatments, where the largest elevations were reached by 0.2mM. For kinetic analysis, K(m) values of ACO stepped up during the experiment and reached the maximums at 0.2mM MeJA with ascending concentrations of treatments. V(max) exhibited a gradual increase from 3h to 24h, and the largest induction appeared with 1.0mM MeJA. The results suggested that ACO is involved in MeJA-induced resistance in tomato, and the concentration influence of MeJA on ACO was attributable to the variation of gene transcripts and enzymatic properties.

Computational design of a thermostable mutant of cocaine esterase via molecular dynamics simulations

Cocaine esterase (CocE) has been known as the most efficient native enzyme for metabolizing naturally occurring cocaine. A major obstacle to the clinical application of CocE is the thermoinstability of native CocE with a half-life of only ∼11 min at physiological temperature (37 °C). It is highly desirable to develop a thermostable mutant of CocE for therapeutic treatment of cocaine overdose and addiction. To establish a structure-thermostability relationship, we carried out molecular dynamics (MD) simulations at 400 K on wild-type CocE and previously known thermostable mutants, demonstrating that the thermostability of the active form of the enzyme correlates with the fluctuation (characterized as the root-mean square deviation and root-mean square fluctuation of atomic positions) of the catalytic residues (Y44, S117, Y118, H287, and D259) in the simulated enzyme. In light of the structure-thermostability correlation, further computational modelling including MD simulations at 400 K predicted that the active site structure of the L169K mutant should be more thermostable. The prediction has been confirmed by wet experimental tests showing that the active form of the L169K mutant had a half-life of 570 min at 37 °C, which is significantly longer than those of the wild-type and previously known thermostable mutants. The encouraging outcome suggests that the high-temperature MD simulations and the structure-thermostability relationship may be considered as a valuable tool for the computational design of thermostable mutants of an enzyme.

Incorporating peptides in the outer-coordination sphere of bioinspired electrocatalysts for hydrogen production

Four new cyclic 1,5-diaza-3,7-diphosphacyclooctane ligands have been prepared and used to synthesize [Ni(P(Ph)(2)N(R)(2))(2)](2+) complexes in which R is a mono- or dipeptide. These complexes represent a first step in the development of an outer-coordination sphere for this class of complexes that can mimic the outer-coordination sphere of the active sites of hydrogenase enzymes. Importantly, these complexes retain the electrocatalytic activity of the parent [Ni(P(Ph)(2)N(Ph)(2))(2)](2+) complex in an acetonitrile solution with turnover frequencies for hydrogen production ranging from 14 to 25 s(-1) in the presence of p-cyanoanilinium trifluoromethanesulfonate and from 135 to 1000 s(-1) in the presence of protonated dimethylformamide, with moderately low overpotentials, ∼0.3 V. The addition of small amounts of water results in rate increases of 2-7 times. Unlike the parent complex, these complexes demonstrate dynamic structural transformations in solution. These results establish a building block from which larger peptide scaffolding can be added to allow the [Ni(P(R)(2)N(R')(2))(2)](2+) molecular catalytic core to begin to mimic the multifunctional outer-coordination sphere of enzymes.

Programmable ligand detection system in plants through a synthetic signal transduction pathway

Background

There is an unmet need to monitor human and natural environments for substances that are intentionally or unintentionally introduced. A long-sought goal is to adapt plants to sense and respond to specific substances for use as environmental monitors. Computationally re-designed periplasmic binding proteins (PBPs) provide a means to design highly sensitive and specific ligand sensing capabilities in receptors. Input from these proteins can be linked to gene expression through histidine kinase (HK) mediated signaling. Components of HK signaling systems are evolutionarily conserved between bacteria and plants. We previously reported that in response to cytokinin-mediated HK activation in plants, the bacterial response regulator PhoB translocates to the nucleus and activates transcription. Also, we previously described a plant visual response system, the de-greening circuit, a threshold sensitive reporter system that produces a visual response which is remotely detectable and quantifiable.

Methodology/Principal Findings

We describe assembly and function of a complete synthetic signal transduction pathway in plants that links input from computationally re-designed PBPs to a visual response. To sense extracellular ligands, we targeted the computational re-designed PBPs to the apoplast. PBPs bind the ligand and develop affinity for the extracellular domain of a chemotactic protein, Trg. We experimentally developed Trg fusions proteins, which bind the ligand-PBP complex, and activate intracellular PhoR, the HK cognate of PhoB. We then adapted Trg-PhoR fusions for function in plants showing that in the presence of an external ligand PhoB translocates to the nucleus and activates transcription. We linked this input to the de-greening circuit creating a detector plant.

Conclusions/Significance

Our system is modular and PBPs can theoretically be designed to bind most small molecules. Hence our system, with improvements, may allow plants to serve as a simple and inexpensive means to monitor human surroundings for substances such as pollutants, explosives, or chemical agents.

Developing a synthetic signal transduction system in plants

One area of focus in the emerging field of plant synthetic biology is the manipulation of systems involved in sensing and response to environmental signals. Sensing and responding to signals, including ligands, typically involves biological signal transduction. Plants use a wide variety of signaling systems to sense and respond to their environment. One of these systems, a histidine kinase (HK) based signaling system, lends itself to manipulation using the tools of synthetic biology. Both plants and bacteria use HKs to relay signals, which in bacteria can involve as few as two proteins (two-component systems or TCS). HK proteins are evolutionarily conserved between plants and bacteria and plant HK components have been shown to be functional in bacteria. We found that this conservation also applies to bacterial HK components which can function in plants. This conservation of function led us to hypothesize that synthetic HK signaling components can be designed and rapidly tested in bacteria. These novel HK signaling components form the foundation for a synthetic signaling system in plants, but typically require modifications such as codon optimization and proper targeting to allow optimal function. We describe the process and methodology of producing a synthetic signal transduction system in plants. We discovered that the bacterial response regulator (RR) PhoB shows HK-dependent nuclear translocation in planta. Using this discovery, we engineered a partial synthetic pathway in which a synthetic promoter (PlantPho) is activated using a plant-adapted PhoB (PhoB-VP64) and the endogenous HK-based cytokinin signaling pathway. Building on this work, we adapted an input or sensing system based on bacterial chemotactic binding proteins and HKs, resulting in a complete eukaryotic signal transduction system. Input to our eukaryotic signal transduction system is provided by a periplasmic binding protein (PBP), ribose-binding protein (RBP). RBP interacts with the membrane-localized chemotactic receptor Trg. PBPs like RBP have been computationally redesigned to bind small ligands, such as the explosive 2,4,6-trinitrotoluene (TNT). A fusion between the chemotactic receptor Trg and the HK, PhoR, enables signal transduction via PhoB, which undergoes nuclear translocation in response to phosphorylation, resulting in transcriptional activation of an output gene under control of a synthetic plant promoter. Collectively, these components produce a novel ligand-responsive signal transduction system in plants and provide a means to engineer a eukaryotic synthetic signaling system.

Enhancement of the latent 3-isopropylmalate dehydrogenase activity of promiscuous homoisocitrate dehydrogenase by directed evolution

HICDH (homoisocitrate dehydrogenase), which is involved in lysine biosynthesis through α-aminoadipate, is a paralogue of IPMDH [3-IPM (3-isopropylmalate) dehydrogenase], which is involved in leucine biosynthesis. TtHICDH (Thermus thermophilus HICDH) can recognize isocitrate, as well as homoisocitrate, as the substrate, and also shows IPMDH activity, although at a considerably decreased rate. In the present study, the promiscuous TtHICDH was evolved into an enzyme showing distinct IPMDH activity by directed evolution using a DNA-shuffling technique. Through five repeats of DNA shuffling/screening, variants that allowed Escherichia coli C600 (leuB−) to grow on a minimal medium in 2 days were obtained. One of the variants LR5–1, with eight amino acid replacements, was found to possess a 65-fold increased kcat/Km value for 3-IPM, compared with TtHICDH. Introduction of a single back-replacement H15Y change caused a further increase in the kcat/Km value and a partial recovery of the decreased thermotolerance of LR5–1. Site-directed mutagenesis revealed that most of the amino acid replacements found in LR5–1 effectively increased IPMDH activity; replacements around the substrate-binding site contributed to the improved recognition for 3-IPM, and other replacements at sites away from the substrate-binding site enhanced the turnover number for the IPMDH reaction. The crystal structure of LR5–1 was determined at 2.4 Å resolution and revealed that helix α4 was displaced in a manner suitable for recognition of the hydrophobic γ-moiety of 3-IPM. On the basis of the crystal structure, possible reasons for enhancement of the turnover number are discussed.

Improving computational protein design by using structure-derived sequence profile

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main-chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five-residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino-acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign-SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild-type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi-blast. More importantly, the sequences designed by RosettaDesign-SR have 2-3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild-type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign-SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions.

Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition

BACKGROUND

Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases.

METHODOLOGY/PRINCIPAL FINDINGS

WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed.

CONCLUSIONS/SIGNIFICANCE

For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

Mechanisms of protein evolution and their application to protein engineering

Protein engineering holds great promise for the development of new biosensors, diagnostics, therapeutics, and agents for bioremediation. Despite some remarkable successes in experimental and computational protein design, engineered proteins rarely achieve the efficiency or specificity of natural enzymes. Current protein design methods utilize evolutionary concepts, including mutation, recombination, and selection, but the inability to fully recapitulate the success of natural evolution suggests that some evolutionary principles have not been fully exploited. One aspect of protein engineering that has received little attention is how to select the most promising proteins to serve as templates, or scaffolds, for engineering. Two evolutionary concepts that could provide a rational basis for template selection are the conservation of catalytic mechanisms and functional promiscuity. Knowledge of the catalytic motifs responsible for conserved aspects of catalysis in mechanistically diverse superfamilies could be used to identify promising templates for protein engineering. Second, protein evolution often proceeds through promiscuous intermediates, suggesting that templates which are naturally promiscuous for a target reaction could enhance protein engineering strategies. This review explores these ideas and alternative hypotheses concerning protein evolution and engineering. Future research will determine if application of these principles will lead to a protein engineering methodology governed by predictable rules for designing efficient, novel catalysts.

De Novo metabolic engineering and the promise of synthetic DNA

The uncertain price and tight supply of crude oil and the ever-increasing demand for clean energy have prompted heightened attention to the development of sustainable fuel technologies that ensure continued economic development while maintaining stewardship of the environment. In the face of these enormous challenges, biomass has emerged as a viable alternative to petroleum for the production of energy, chemicals, and materials owing to its abundance, inexpensiveness, and carbon-neutrality. Moreover, the immense ease and efficiency of biological systems at converting biomass-derived feedstocks into fuels, chemicals, and materials has generated renewed interest in biotechnology as a replacement for traditional chemical processes. Aided by the ever-expanding repertoire of microbial genetics and plant biotechnology, improved understanding of gene regulation and cellular metabolism, and incessantly accumulating gene and protein data, scientists are now contemplating engineering microbial cell factories to produce fuels, chemical feedstocks, polymers and pharmaceuticals in an economically and environmentally sustainable way. This goal resonates with that of metabolic engineering - the improvement of cellular properties through the intelligent design, rational modification, or directed evolution of biochemical pathways, and arguably, metabolic engineering seems best positioned to achieve the concomittant goals of environmental stewardship and economic prolificity.Improving a host organism's cellular traits and the potential design of new phenotypes is strongly dependent on the ability to effectively control the organism's genetic machinery. In fact, finely-tuned gene expression is imperative for achieving an optimal balance between pathway expression and cell viability, while avoiding cytotoxicity due to accumulation of certain gene products or metabolites. Early attempts to engineer a cell's metabolism almost exclusively relied on merely deleting or over-expressing single or multiple genes using recombinant DNA, and intervention targets were predominantly selected based on knowledge of the stoichiometry, kinetics, and regulation of the pathway of interest. However, the distributive nature of metabolic control, as opposed to the existence of a single rate-limiting step, predicates the controlled expression of multiple enzymes in several coordinated pathways to achieve the desired flux, and, as such, simple strategies involving either deleting or over-expressing genes are greatly limited in this context. On the other hand, the use of synthetic or modified promoters, riboswitches, tunable intergenic regions, and translation modulators such as internal ribosome entry sequences, upstream open reading frames, optimized mRNA secondary structures, and RNA silencing have been shown to be enormously conducive to achieving the fine-tuning of gene expression. These modifications to the genetic machinery of the host organism can be best achieved via the use of synthetic DNA technology, and the constant improvement in the affordability and quality of oligonucleotide synthesis suggests that these might well become the mainstay of the metabolic engineering toolbox in the years to come. The possibilities that arise with the use of synthetic oligonucleotides will be delineated herein.

Computational protein design as a tool for fold recognition

Computationally designed protein sequences have been proposed as a basis to perform fold recognition and homology searching. To investigate this possibility, an automated procedure is used to completely redesign 24 SH3 proteins and 22 SH2 proteins. We use the experimental backbone coordinates as fixed templates in the folded state and a molecular mechanics model to compute the pairwise interaction energies between all sidechain types and conformations. Energy calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is then used to scan the sequence and conformational space for optimal solutions. We produced 200,000-450,000 sequences for each backbone template. The designed sequences ressemble moderately-distant, natural homologues of the initial templates, according to their identity scores and their similarity with respect to the Pfam sets of SH2 and SH3 domains. Standard homology detection tools document their native-like character: the Conserved Domain Database recognizes 61% (52%) of our low-energy sequences as SH3 (SH2) domains; the SUPERFAMILY, Hidden-Markov Model library recognizes 81% (84%). Conversely, position specific scoring matrices (PSSMs) derived from our designed sequences can be used to detect natural homologues in sequence databases. Within SwissProt, a set of natural SH3 PSSMs detects 772 SH3 domains, for example; our designed PSSMs detect 67% of these, plus one additional sequence and two false positives. If six amino acids involved in substrate binding (a selective pressure not accounted for in our design) are reset to their experimental types, then 77% of the experimental SH3 domains are detected. Results for the SH2 domains are similar. Several directions to improve the method further are discussed.

Small-angle neutron scattering study of protein unfolding and refolding

Small-angle neutron scattering has been used to study protein unfolding and refolding in protein bovine serum albumin (BSA) due to perturbation in its native structure as induced by three different protein denaturating agents: urea, surfactant, and pressure. The BSA protein unfolds for urea concentrations greater than 4 M and is observed to be independent of the protein concentration. The addition of surfactant unfolds the protein by the formation of micellelike aggregates of surfactants along the unfolded polypeptide chains of the protein and depends on the ratio of surfactant to protein concentration. We make use of the dilution method to show the refolding of unfolded proteins in the presence of urea and surfactant. BSA does not show any protein unfolding up to the pressure of 450 MPa. The presence of urea and surfactant (for concentrations prior to inducing their own unfolding) has been used to examine pressure-induced unfolding of the protein at lower pressures. The protein unfolds at 200 MPa pressure in the presence of urea; however, no unfolding is observed with surfactant. The protein unfolding is shown to be reversible in all the above denaturating methods.

Thermostable variants of cocaine esterase for long-time protection against cocaine toxicity

Enhancing cocaine metabolism by administration of cocaine esterase (CocE) has been recognized as a promising treatment strategy for cocaine overdose and addiction, because CocE is the most efficient native enzyme for metabolizing the naturally occurring cocaine yet identified. A major obstacle to the clinical application of CocE is the thermoinstability of native CocE with a half-life of only a few minutes at physiological temperature (37 degrees C). Here we report thermostable variants of CocE developed through rational design using a novel computational approach followed by in vitro and in vivo studies. This integrated computational-experimental effort has yielded a CocE variant with a approximately 30-fold increase in plasma half-life both in vitro and in vivo. The novel design strategy can be used to develop thermostable mutants of any protein.

Organic ligand binding by a hydrophobic cavity in a designed tetrameric coiled-coil protein

The design and characterization of a hydrophobic cavity in de novo designed proteins provides a wide range of information about the functions of de novo proteins. We designed a de novo tetrameric coiled-coil protein with a hydrophobic pocketlike cavity. Tetrameric coiled coils with hydrophobic cavities have previously been reported. By replacing one Leu residue at the a position with Ala, hydrophobic cavities that did not flatten out due to loose peptide chains were reliably created. To perform a detailed examination of the ligand-binding characteristics of the cavities, we originally designed two other coiled-coil proteins: AM2, with eight Ala substitutions at the adjacent a and d positions at the center of a bundled structure, and AM2W, with one Trp and seven Ala substitutions at the same positions. To increase the association of the helical peptides, each helical peptide was connected with flexible linkers, which resulted in a single peptide chain. These proteins exhibited CD spectra corresponding to superhelical structures, despite weakened hydrophobic packing. AM2W exhibited binding affinity for size-complementary organic compounds. The dissociation constants, K(d), of AM2W were 220 nM for adamantane, 81 microM for 1-adamantanol, and 294 microM for 1-adamantaneacetic acid, as measured by fluorescence titration analyses. Although it was contrary to expectations, AM2 did not exhibit any binding affinity, probably due to structural defects around the designed hydrophobic cavity. Interestingly, AM2W exhibited incremental structure stability through ligand binding. Plugging of structural defects with organic ligands would be expected to facilitate protein folding.