A retrosynthetic biology approach to metabolic pathway design for therapeutic production

<p>Abstract</p> <p>Background</p> <p>Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an <it>in situ </it>drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.</p> <p>Results</p> <p>In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.</p> <p>Conclusions</p> <p>We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.</p

novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model

To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/

Computational Studies and Biosynthesis of Natural Products with Promising Anticancer Properties

We present an overview of computational approaches for the prediction of metabolic pathways by which plants biosynthesise compounds, with a focus on selected very promising anticancer secondary metabolites from floral sources. We also provide an overview of databases for the retrieval of useful genomic data, discussing the strengths and limitations of selected prediction software and the main computational tools (and methods), which could be employed for the investigation of the uncharted routes towards the biosynthesis of some of the identified anticancer metabolites from plant sources, eventually using specific examples to address some knowledge gaps when using these approaches

Synthesis of a Potentially Insulin-Mimetic, Lipid-Linked Inositol Glycan

Inositol glycans (IGs) are naturally occurring oligosaccharides that can stimulate insulin sensitive cells. Several synthetic IG analogues have been shown to activate the insulin-signaling pathway, including the stimulation of the enzyme pyruvate dehydrogenase (PDH) phosphatase that can further stimulate aerobic metabolism in cells. Cancer cells shift to anaerobic metabolism in order to escape intrinsic apoptosis (Warburg Effect). IG\u27s ability to stimulate aerobic metabolism might provide a method to reverse the Warburg Effect and thereby induce apoptosis in the cancer cells. One specific palmitoylated IG analogue has been shown to selectively kill cancer cells while having no adverse effect on normal cells. However, this analogue is unstable under physiological conditions due to ester hydrolysis and acyl group migration. This thesis describes the work on the synthesis of an IG analogue in which the ester linkage has been replaced by ether. Since ethers are comparatively more stable than esters, the resulting IG analogue should be more stable than the parent analogue. Biological activity of this IG analogue will be reported elsewhe

In-depth characterization of genome-scale network reconstructions for the in vitro synthesis in cell-free systems

Cell‐free systems containing multiple enzymes are becoming an increasingly interesting tool for one‐pot syntheses of biochemical compounds. To extensively explore the enormous wealth of enzymes in the biological space, we present methods for assembling and curing data from databases to apply them for the prediction of pathway candidates for directed enzymatic synthesis. We use Kyoto Encyclopedia of Genes and Genomes to establish single organism models and a pan‐organism model that is combining the available data from all organisms listed there. We introduce a filtering scheme to remove data that are not suitable, for example, generic metabolites and general reactions. In addition, a valid stoichiometry of reactions is required for acceptance. The networks created are analyzed by graph theoretical methods to identify a set of metabolites that are potentially reachable from a defined set of starting metabolites. Thus, metabolites not connected to such starting metabolites cannot be produced unless new starting metabolites or reactions are introduced. The network models also comprise stoichiometric and thermodynamic data that allow the definition of constraints to identify potential pathways. The resulting data can be directly applied using existing or future pathway finding tools

Complexity reduction and opportunities in the design, integration and intensification of biocatalytic processes for metabolite synthesis

This is the final version. Available on open access from Frontiers Media via the DOI in this recordThe biosynthesis of metabolites from available starting materials is becoming an ever important area due to the increasing demands within the life science research area. Access to metabolites is making essential contributions to analytical, diagnostic, therapeutic and different industrial applications. These molecules can be synthesized by the enzymes of biological systems under sustainable process conditions. The facile synthetic access to the metabolite and metabolite-like molecular space is of fundamental importance. The increasing knowledge within molecular biology, enzyme discovery and production together with their biochemical and structural properties offers excellent opportunities for using modular cell-free biocatalytic systems. This reduces the complexity of synthesizing metabolites using biological whole-cell approaches or by classical chemical synthesis. A systems biocatalysis approach can provide a wealth of optimized enzymes for the biosynthesis of already identified and new metabolite molecules

Retrobiosynthesis of D-glucaric acid in a metabolically engineered strain of Escherichia coli

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 173-181).Synthetic biology is an evolving field that emphasizes synthesis more than observation which has been and is the scientific method for traditional biology. With powerful synthetic tools, synthetic biologists seek to reproduce natural behaviors (and eventually to create artificial life) from unnatural molecules or try to construct unnatural systems from interchangeable parts. Accompanied with this recent movement, metabolic engineers started to utilize these interchangeable parts (enzymes in this case) to create novel pathways. In addition, engineering biological parts including enzymes, promoters, and protein-protein interaction domains has led to productivity improvement. However, understanding behaviors of a synthetic pathway in an engineered chassis is still a daunting task, requiring global optimization. As the first step to understand pathway design rules and behaviors of synthetic pathways, a synthetic pathway for the production of D-glucaric acid has been designed and constructed in E. coli. To this end, three disparate enzymes were recruited from three different organisms, and the system perturbed by this introduction of heterologous genes was analyzed. Limiting flux through the pathway is the second recombinant step, catalyzed by myo-inositol oxygenase (MIOX), whose activity is strongly influenced by the concentration of the myo-inositol substrate. To increase the effective concentration of myo-inositol, synthetic scaffold devices were built from protein-protein interaction domains to co-recruit all three pathway enzymes in a designable complex.(cont.) This colocalization led to enhancement of MIOX activity with concomitant productivity improvement, achieving 2.7 g/L of D-glucaric acid production from 10 g/L of D-glucose input. Secondly, retrobiosynthetic approach, a product-targeted design of biological pathways, has been proposed as an alternative strategy to exploit the diversity of enzymecatalyzed reactions. The first step in a glucaric acid pathway designed retrosynthetically involves oxidation of the C-6 hydroxyl group on glucose, but no glucose oxidase in nature has been found to act on this hydroxyl group on glucose. To create glucose 6- oxidase, a computational design and experimental selection was combined with the help of DNA synthesis technology. To this end, the sequence space of candidate mutations was computationally searched, the selected sequences were combinatorially assembled, and the created library was experimentally screened and characterized. Furthermore, the structure-activity relationship of the newly created glucose oxidases was elucidated, and the kinetic model mechanism for these mutants was proposed and analyzed. Collectively, parts, devices, and chassis engineering were applied to a synthetic pathway for the production of D-glucaric acid, and this synthetic biology approach was proven to be effective for new pathway design and improvement.by Tae Seok Moon.Ph.D

Computational Studies on Cellular Metabolism:From Biochemical Pathways to Complex Metabolic Networks

Biotechnology promises the biologically and ecologically sustainable production of commodity chemicals, biofuels, pharmaceuticals and other high-value products using industrial platform microorganisms. Metabolic engineering plays a key role in this process, providing the tools for targeted modifications of microbial metabolism to create efficient microbial cell factories that convert low value substrates to value-added chemicals. Engineering microbes for the bioproduction of chemicals has been practiced through three different approaches: (i) optimization of native pathways of a host organism; (ii) incorporation of heterologous pathways in an amenable organism; and finally (iii) design and introduction of synthetic pathways in an organism. So far, the progress that has been made in the biosynthesis of chemicals was mostly achieved using the first two approaches. Nevertheless, many novel biosynthetic pathways for the production of native and non-native compounds that have potential to provide near-theoretical yields and high specific production rates of chemicals remain yet to be discovered. Therefore, the third approach is crucial for the advancement of bio-based production of value-added chemicals. We need to fully comprehend and analyze the existing knowledge of metabolism in order to generate new hypotheses and design de novo pathways. In this thesis, through development and application of efficient computational methods, we took the research path to expand our understanding of cell metabolism with the aim to discover novel knowledge about metabolic networks. We analyze different aspects of metabolism through five distinct studies. In the first study, we begin with a holistic view of the enzymatic reactions across all the species, and we propose a computational approach for identifying all the theoretically possible enzymatic reactions based on the known biochemistry. We organize our results in a web-based database called âAtlas of biochemistryâ. In the second study, we focus on one of the most structurally diverse and ubiquitous constituents of metabolism, the lipid metabolism. Here we propose a computational framework for integrating lipid species with unknown metabolic/catabolic pathways into metabolic networks. In our next study, we investigate the full metabolic capacity of E. coli. We explore computationally all enzymatic potentials of this organism, and we introduce the âSuper E. coliâ, a new and advanced chassis for metabolic engineering studies. Our next contribution concentrates on the development of a new method for the atom-level description of metabolic networks. We demonstrate the significance of our approach through the reconstruction of atom-level map of the E. coli central metabolism. In the last study, we turn our focus on studying the thermodynamics of metabolism and we present our original approach for estimating the thermodynamic properties of an important class of metabolites. So far, the available thermodynamic properties either from experiments or the computational methods are estimated with respect to the standard conditions, which are different from typical biological conditions. Our workflow paves the way for reliable computing of thermochemical properties of biomolecules at biological conditions of temperature and pressure. Finally, in the conclusion chapter, we discuss the outlook of this work and the potential further applications of the computational methods that were developed in this thesis