The kinetic model of the shikimate pathway as a tool to optimize enzyme assays for high-throughput screening

Applications of Molecular Simulation in the Discovery of Antituberculosis Drugs: A Review

After decades of efforts, tuberculosis has been well controlled in most places. The existing drugs are no longer sufficient for the treatment of drug-resistant Mycobacterium tuberculosis due to significant toxicity and selective pressure, especially for XDR-TB. In order to accelerate the development of high-efficiency, low-toxic antituberculosis drugs, it is particularly important to use Computer Aided Drug Design (CADD) for rational drug design. Here, we systematically reviewed the specific role of molecular simulation in the discovery of new antituberculosis drugs. The purpose of this review is to overview current applications of molecular simulation methods in the discovery of antituberculosis drugs. Furthermore, the unique advantages of molecular simulation was discussed in revealing the mechanism of drug resistance. The comprehensive use of different molecular simulation methods will help reveal the mechanism of drug resistance and improve the efficiency of rational drug design. With the help of molecular simulation methods such as QM/MM method, the mechanisms of biochemical reactions catalyzed by enzymes at atomic level in Mycobacterium tuberculosis has been deeply analyzed. QSAR and virtual screening both accelerate the development of highefficiency, low-toxic potential antituberculosis drugs. Improving the accuracy of existing algorithms and developing more efficient new methods for CADD will always be a hot topic in the future. It is of great value to utilize molecular dynamics simulation to investigate complex systems that cannot be studied in experiments, especially for drug resistance of Mycobacterium tuberculosis.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Mechanistic enzymology in drug discovery: a fresh perspective

Given the therapeutic and commercial success of small-molecule enzyme inhibitors, as exemplified by kinase inhibitors in oncology, a major focus of current drug-discovery and development efforts is on enzyme targets. Understanding the course of an enzyme-catalysed reaction can help to conceptualize different types of inhibitor and to inform the design of screens to identify desired mechanisms. Exploiting this information allows the thorough evaluation of diverse compounds, providing the knowledge required to efficiently optimize leads towards differentiated candidate drugs. This review highlights the rationale for conducting high-quality mechanistic enzymology studies and considers the added value in combining such studies with orthogonal biophysical methods.

Identification of polyketide inhibitors targeting 3-dehydroquinate dehydratase in the shikimate pathway of Enterococcus faecalis

Due to the emergence of resistance toward current antibiotics, there is a pressing need to develop the next generation of antibiotics as therapeutics against infectious and opportunistic diseases of microbial origins. The shikimate pathway is exclusive to microbes, plants and fungi, and hence is an attractive and logical target for development of antimicrobial therapeutics. The Gram-positive commensal microbe, Enterococcus faecalis, is a major human pathogen associated with nosocomial infections and resistance to vancomycin, the “drug of last resort”. Here, we report the identification of several polyketide-based inhibitors against the E. faecalis shikimate pathway enzyme, 3-dehydroquinate dehydratase (DHQase). In particular, marein, a flavonoid polyketide, both inhibited DHQase and retarded the growth of Enterococcus faecalis. The purification, crystallization and structural resolution of recombinant DHQase from E. faecalis (at 2.2 Å resolution) are also reported. This study provides a route in the development of polyketide-based antimicrobial inhibitors targeting the shikimate pathway of the human pathogen E. faecalis.

Genome-wide identification of genes essential for the survival of Streptococcus pneumoniae in human saliva

Since Streptococcus pneumoniae transmits through droplet spread, this respiratory tract pathogen may be able to survive in saliva. Here, we show that saliva supports survival of clinically relevant S. pneumoniae strains for more than 24 h in a capsule-independent manner. Moreover, saliva induced growth of S. pneumoniae in growth-permissive conditions, suggesting that S. pneumoniae is well adapted for uptake of nutrients from this bodily fluid. By using Tn-seq, a method for genome-wide negative selection screening, we identified 147 genes potentially required for growth and survival of S. pneumoniae in saliva, among which genes predicted to be involved in cell envelope biosynthesis, cell transport, amino acid metabolism, and stress response predominated. The Tn-seq findings were validated by testing a panel of directed gene deletion mutants for their ability to survive in saliva under two testing conditions: at room temperature without CO₂, representing transmission, and at 37°C with CO₂, representing in-host carriage. These validation experiments confirmed that the plsX gene and the amiACDEF and aroDEBC operons, involved in respectively fatty acid metabolism, oligopeptide transport, and biosynthesis of aromatic amino acids play an important role in the growth and survival of S. pneumoniae in saliva at 37°C. In conclusion, this study shows that S. pneumoniae is well-adapted for growth and survival in human saliva and provides a genome-wide list of genes potentially involved in adaptation. This notion supports earlier evidence that S. pneumoniae can use human saliva as a vector for transmission.

Reassessing the type I dehydroquinate dehydratase catalytic triad: kinetic and structural studies of Glu86 mutants

Dehydroquinate dehydratase (DHQD) catalyzes the third reaction in the biosynthetic shikimate pathway. Type I DHQDs are members of the greater aldolase superfamily, a group of enzymes that contain an active site lysine that forms a Schiff base intermediate. Three residues (Glu86, His143, and Lys170 in the Salmonella enterica DHQD) have previously been proposed to form a triad vital for catalysis. While the roles of Lys170 and His143 are well defined-Lys170 forms the Schiff base with the substrate and His143 shuttles protons in multiple steps in the reaction-the role of Glu86 remains poorly characterized. To probe Glu86's role, Glu86 mutants were generated and subjected to biochemical and structural study. The studies presented here demonstrate that mutant enzymes retain catalytic proficiency, calling into question the previously attributed role of Glu86 in catalysis and suggesting that His143 and Lys170 function as a catalytic dyad. Structures of the Glu86Ala (E86A) mutant in complex with covalently bound reaction intermediate reveal a conformational change of the His143 side chain. This indicates a predominant steric role for Glu86, to maintain the His143 side chain in position consistent with catalysis. The structures also explain why the E86A mutant is optimally active at more acidic conditions than the wild-type enzyme. In addition, a complex with the reaction product reveals a novel, likely nonproductive, binding mode that suggests a mechanism of competitive product inhibition and a potential strategy for the design of therapeutics.

A conserved surface loop in type I dehydroquinate dehydratases positions an active site arginine and functions in substrate binding

Dehydroquinate dehydratase (DHQD) catalyzes the third step in the biosynthetic shikimate pathway. We present three crystal structures of the Salmonella enterica type I DHQD that address the functionality of a surface loop that is observed to close over the active site following substrate binding. Two wild-type structures with differing loop conformations and kinetic and structural studies of a mutant provide evidence of both direct and indirect mechanisms of involvement of the loop in substrate binding. In addition to allowing amino acid side chains to establish a direct interaction with the substrate, closure of the loop necessitates a conformational change of a key active site arginine, which in turn positions the substrate productively. The absence of DHQD in humans and its essentiality in many pathogenic bacteria make the enzyme a target for the development of nontoxic antimicrobials. The structures and ligand binding insights presented here may inform the design of novel type I DHQD inhibiting molecules.

Insights into the mechanism of type I dehydroquinate dehydratases from structures of reaction intermediates

The biosynthetic shikimate pathway consists of seven enzymes that catalyze sequential reactions to generate chorismate, a critical branch point in the synthesis of the aromatic amino acids. The third enzyme in the pathway, dehydroquinate dehydratase (DHQD), catalyzes the dehydration of 3-dehydroquinate to 3-dehydroshikimate. We present three crystal structures of the type I DHQD from the intestinal pathogens Clostridium difficile and Salmonella enterica. Structures of the enzyme with substrate and covalent pre- and post-dehydration reaction intermediates provide snapshots of successive steps along the type I DHQD-catalyzed reaction coordinate. These structures reveal that the position of the substrate within the active site does not appreciably change upon Schiff base formation. The intermediate state structures reveal a reaction state-dependent behavior of His-143 in which the residue adopts a conformation proximal to the site of catalytic dehydration only when the leaving group is present. We speculate that His-143 is likely to assume differing catalytic roles in each of its observed conformations. One conformation of His-143 positions the residue for the formation/hydrolysis of the covalent Schiff base intermediates, whereas the other conformation positions the residue for a role in the catalytic dehydration event. The fact that the shikimate pathway is absent from humans makes the enzymes of the pathway potential targets for the development of non-toxic antimicrobials. The structures and mechanistic insight presented here may inform the design of type I DHQD enzyme inhibitors.

DBSolve Optimum: a software package for kinetic modeling which allows dynamic visualization of simulation results

Background

Systems biology research and applications require creation, validation, extensive usage of mathematical models and visualization of simulation results by end-users. Our goal is to develop novel method for visualization of simulation results and implement it in simulation software package equipped with the sophisticated mathematical and computational techniques for model development, verification and parameter fitting.

Results

We present mathematical simulation workbench DBSolve Optimum which is significantly improved and extended successor of well known simulation software DBSolve5. Concept of "dynamic visualization" of simulation results has been developed and implemented in DBSolve Optimum. In framework of the concept graphical objects representing metabolite concentrations and reactions change their volume and shape in accordance to simulation results. This technique is applied to visualize both kinetic response of the model and dependence of its steady state on parameter. The use of the dynamic visualization is illustrated with kinetic model of the Krebs cycle.

Conclusion

DBSolve Optimum is a user friendly simulation software package that enables to simplify the construction, verification, analysis and visualization of kinetic models. Dynamic visualization tool implemented in the software allows user to animate simulation results and, thereby, present them in more comprehensible mode. DBSolve Optimum and built-in dynamic visualization module is free for both academic and commercial use. It can be downloaded directly from http://www.insysbio.ru.

Kinetic modeling as a tool to integrate multilevel dynamic experimental data

The metabolic networks are the most well-studied biochemical systems, with an abundance of in vitro and in vivo data available for quantitative estimation of its kinetic parameters. In this chapter, we present our approach to developing mathematical description of metabolic pathways. The model-based integration of reaction kinetics and the utilization of different types of experimental data including temporal dependencies have been described in detail. Software package DBSolve7 which allows us to develop kinetic model of the biochemical system and integrate experimental data has been presented.

Structure-based inhibitor discovery of Helicobacter pylori dehydroquinate synthase

Dehydroquinate synthase (DHQS) is a nicotinamide adenine dinucleotide (NAD)-dependent enzyme that converts 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) into 3-dehydroquinate (DHQ). Since it catalyzes the second key step in the shikimate pathway, which is crucial for the aromatic amino acid metabolism in bacteria, fungi, and plants, but not in mammals, DHQS is a potential target for new antimicrobial agents, anti-parasitic agents and herbicides. The crystal structure of Helicobacter pylori DHQS (HpDHQS) complexed with NAD has been determined at 2.4-A resolution and was found to possess an N-terminal Rossmann-fold domain and a C-terminal alpha-helical domain. Structural comparison reveals that the binary complex adopts an open-state conformation and shares conserved residues in the binding pocket. Virtual docking of compounds into the active site of the HpDHQS structure using the GOLD docking program led to the identification of several inhibitors. The most active compound had an IC(50) value of 61 microM, which may serve as a lead for potent inhibitors.

Metabolomics: current state and evolving methodologies and tools

In recent years, metabolomics developed to an accepted and valuable tool in life sciences. Substantial improvements of analytical hardware allow metabolomics to run routinely now. Data are successfully used to investigate genotype-phenotype relations of strains and mutants. Metabolomics facilitates metabolic engineering to optimise mircoorganisms for white biotechnology and spreads to the investigation of biotransformations and cell culture. Metabolomics serves not only as a source of qualitative but also quantitative data of intra-cellular metabolites essential for the model-based description of the metabolic network operating under in vivo conditions. To collect reliable metabolome data sets, culture and sampling conditions, as well as the cells' metabolic state, are crucial. Hence, application of biochemical engineering principles and method standardisation efforts become important. Together with the other more established omics technologies, metabolomics will strengthen its claim to contribute to the detailed understanding of the in vivo function of gene products, biochemical and regulatory networks and, even more ambitious, the mathematical description and simulation of the whole cell in the systems biology approach. This knowledge will allow the construction of designer organisms for process application using biotransformation and fermentative approaches making effective use of single enzymes, whole microbial and even higher cells.