Computational discovery of biochemical routes to specialty chemicals

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.

enviRule: an end-to-end system for automatic extraction of reaction patterns from environmental contaminant biotransformation pathways

MOTIVATION

Transformation products (TPs) of man-made chemicals, formed through microbially mediated transformation in the environment, can have serious adverse environmental effects, yet the analytical identification of TPs is challenging. Rule-based prediction tools are successful in predicting TPs, especially in environmental chemistry applications that typically have to rely on small datasets, by imparting the existing knowledge on enzyme-mediated biotransformation reactions. However, the rules extracted from biotransformation reaction databases usually face the issue of being over/under-generalized and are not flexible to be updated with new reactions.

RESULTS

We developed an automatic rule extraction tool called enviRule. It clusters biotransformation reactions into different groups based on the similarities of reaction fingerprints, and then automatically extracts and generalizes rules for each reaction group in SMARTS format. It optimizes the genericity of automatic rules against the downstream TP prediction task. Models trained with automatic rules outperformed the models trained with manually curated rules by 30% in the area under curve (AUC) scores. Moreover, automatic rules can be easily updated with new reactions, highlighting enviRule's strengths for both automatic extraction of optimized reactions rules and automated updating thereof.

AVAILABILITY AND IMPLEMENTATION

enviRule code is freely available at https://github.com/zhangky12/enviRule.

Implementation of Synthetic Pathways to Foster Microbe-Based Production of Non-Naturally Occurring Carboxylic Acids and Derivatives

Microbially produced carboxylic acids (CAs) are considered key players in the implementation of more sustainable industrial processes due to their potential to replace a set of oil-derived commodity chemicals. Most CAs are intermediates of microbial central carbon metabolism, and therefore, a biochemical production pathway is described and can be transferred to a host of choice to enable/improve production at an industrial scale. However, for some CAs, the implementation of this approach is difficult, either because they do not occur naturally (as is the case for levulinic acid) or because the described production pathway cannot be easily ported (as it is the case for adipic, muconic or glucaric acids). Synthetic biology has been reshaping the range of molecules that can be produced by microbial cells by setting new-to-nature pathways that leverage on enzyme arrangements not observed in vivo, often in association with the use of substrates that are not enzymes’ natural ones. In this review, we provide an overview of how the establishment of synthetic pathways, assisted by computational tools for metabolic retrobiosynthesis, has been applied to the field of CA production. The translation of these efforts in bridging the gap between the synthesis of CAs and of their more interesting derivatives, often themselves non-naturally occurring molecules, is also reviewed using as case studies the production of methacrylic, methylmethacrylic and poly-lactic acids.

Learning graph representations of biochemical networks and its application to enzymatic link prediction

Motivation

The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, enzymatic link prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions cataloged in the KEGG database as a graph. ELP is innovative over prior works in using graph embedding to learn molecular representations that capture not only molecular and enzymatic attributes but also graph connectivity.

Results

We explore transductive (test nodes included in the training graph) and inductive (test nodes not part of the training graph) learning models. We show that ELP achieves high AUC when learning node embeddings using both graph connectivity and node attributes. Further, we show that graph embedding improves link prediction by 30% in area under curve over fingerprint-based similarity approaches and by 8% over support vector machines. We compare ELP against rule-based methods. We also evaluate ELP for predicting links in pathway maps and for reconstruction of edges in reaction networks of four common gut microbiota phyla: actinobacteria, bacteroidetes, firmicutes and proteobacteria. To emphasize the importance of graph embedding in the context of biochemical networks, we illustrate how graph embedding can guide visualization.

Availability and implementation

The code and datasets are available through https://github.com/HassounLab/ELP.

Stepping on the Gas to a Circular Economy: Accelerating Development of Carbon-Negative Chemical Production from Gas Fermentation

Owing to rising levels of greenhouse gases in our atmosphere and oceans, climate change poses significant environmental, economic, and social challenges globally. Technologies that enable carbon capture and conversion of greenhouse gases into useful products will help mitigate climate change by enabling a new circular carbon economy. Gas fermentation usingcarbon-fixing microorganisms offers an economically viable and scalable solution with unique feedstock and product flexibility that has been commercialized recently. We review the state of the art of gas fermentation and discuss opportunities to accelerate future development and rollout. We discuss the current commercial process for conversion of waste gases to ethanol, including the underlying biology, challenges in process scale-up, and progress on genetic tool development and metabolic engineering to expand the product spectrum. We emphasize key enabling technologies to accelerate strain development for acetogens and other nonmodel organisms.

Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity

Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Transforming organisms such as Escherichia coli into microbial factories is achieved via several engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruptions arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We demonstrate how enzyme promiscuity may aid cells in adapting to disruptions of essential metabolic functions. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropionic acid (3-HP) production. Using MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. As we demonstrate, MDFlow provides an innovative workflow to systematically identify incompatibilities between the native metabolism of the host and its engineered modifications due to enzyme promiscuity.

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Engineering modifications to cellular hosts result in undesirable byproducts.

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Metabolic Disruption: changes in engineered host due to enzyme promiscuity.

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Metabolic Disruption Workflow (MDFlow) uncovers metabolic disruption.

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MDFlow corroborates previously experimentally verified promiscuous interactions.

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MDFlow compares disruption due to heterologous pathways targeting 3-HP production.

Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. We developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.

MAGI: A Method for Metabolite Annotation and Gene Integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi .

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.

A review of parameters and heuristics for guiding metabolic pathfinding

Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.

Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas

Mono-ethylene glycol (MEG) is an important petrochemical with widespread use in numerous consumer products. The current industrial MEG-production process relies on non-renewable fossil fuel-based feedstocks, such as petroleum, natural gas, and naphtha; hence, it is useful to explore alternative routes of MEG-synthesis from gases as they might provide a greener and more sustainable alternative to the current production methods. Technologies of synthetic biology and metabolic engineering of microorganisms can be deployed for the expression of new biochemical pathways for MEG-synthesis from gases, provided that such promising alternative routes are first identified. We used the BNICE.ch algorithm to develop novel and previously unknown biological pathways to MEG from synthesis gas by leveraging the Wood-Ljungdahl pathway of carbon fixation of acetogenic bacteria. We developed a set of useful pathway pruning and analysis criteria to systematically assess thousands of pathways generated by BNICE.ch. Published genome-scale models of Moorella thermoacetica and Clostridium ljungdahlii were used to perform the pathway yield calculations and in-depth analyses of seven (7) newly developed biological MEG-producing pathways from gases, including CO₂, CO, and H₂. These analyses helped identify not only better candidate pathways, but also superior chassis organisms that can be used for metabolic engineering of the candidate pathways. The pathway generation, pruning, and detailed analysis procedures described in this study can also be used to develop biochemical pathways for other commodity chemicals from gaseous substrates.

Molecular thermodynamics of metabolism: quantum thermochemical calculations for key metabolites

The present work is the first of a series of papers aiming at a coherent and unified development of the thermodynamics of metabolism and the rationalization of feasibility analysis of metabolic pathways. The focus in this part is on high-level quantum chemical calculations of the thermochemical quantities of relatively heavy metabolites such as amino acids/oligopeptides, nucleosides, saccharides and their derivatives in the ideal gas state. The results of this study will be combined with the corresponding hydration/solvation results in subsequent parts of this work in order to derive the desired thermochemical quantities in aqueous solutions. The above metabolites exist in a vast conformational/isomerization space including rotational conformers, tautomers or anomers exhibiting often multiple or cooperative intramolecular hydrogen bonding. We examine the challenges posed by these features for the reliable estimation of thermochemical quantities. We discuss conformer search, conformer distribution and averaging processes. We further consider neutral metabolites as well as protonated and deprotonated metabolites. In addition to the traditional presentation of gas-phase acidities, basicities and proton affinities, we also examine heats and free energies of ionic species. We obtain simple linear relations between the thermochemical quantities of ions and the formation quantities of their neutral counterparts. Furthermore, we compare our calculations with reliable experimental measurements and predictive calculations from the literature, when available. Finally, we discuss the next steps and perspectives for this work.

Transition-State Theory, Dynamics, and Narrow Time Scale Separation in the Rate-Promoting Vibrations Model of Enzyme Catalysis

The power of transition-state theory (TST) for understanding enzymes is evidenced by its recent use in the design and synthesis of highly active de novo enzymes. However, dynamics can influence reaction kinetics, and some studies of rate-promoting vibrations even claim that dynamical theories instead of TST are needed to understand enzymatic reaction mechanisms. For the rate-promoting vibration (RPV) model of enzyme catalysis [Antoniou et al., J. Chem. Phys. 2004, 121, 6442], a reactive flux correlation function analysis shows that dynamical effects do slow the kinetics. However, the RPV model also shows extremely long-lived correlations because the RPV and the bath are not directly coupled. Additionally, earlier studies of the RPV model show a narrow time scale separation due to a small 5kT barrier. Thus earlier findings based on the RPV model may have little bearing on the properties of real enzymes. The intrinsic reaction coordinate (IRC) reveals that the RPV is an important component of the reaction coordinate at early and late stages of the pathway, but the RPV is not an important component of the reaction coordinate direction at the transition state. The unstable eigenmode from harmonic TST (which coincides with the IRC at the saddle point) gives a larger transmission coefficient than the coordinate used in the correlation functions of Antoniou et al. Thus while TST cannot predict the transmission coefficient, the RPV model suggests that TST can provide mechanistic insights on elementary steps in enzyme catalysis. Finally, we propose a method for using the transition-state ensemble as predicted from harmonic TST to distinguish promoting vibrations from other more mundane bath variables.

A directed-overflow and damage-control N-glycosidase in riboflavin biosynthesis

Plants and bacteria synthesize the essential human micronutrient riboflavin (vitamin B2) via the same multi-step pathway. The early intermediates of this pathway are notoriously reactive and may be overproduced in vivo because riboflavin biosynthesis enzymes lack feedback controls. In the present paper, we demonstrate disposal of riboflavin intermediates by COG3236 (DUF1768), a protein of previously unknown function that is fused to two different riboflavin pathway enzymes in plants and bacteria (RIBR and RibA respectively). We present cheminformatic, biochemical, genetic and genomic evidence to show that: (i) plant and bacterial COG3236 proteins cleave the N-glycosidic bond of the first two intermediates of riboflavin biosynthesis, yielding relatively innocuous products; (ii) certain COG3236 proteins are in a multi-enzyme riboflavin biosynthesis complex that gives them privileged access to riboflavin intermediates; and (iii) COG3236 action in Arabidopsis thaliana and Escherichia coli helps maintain flavin levels. COG3236 proteins thus illustrate two emerging principles in chemical biology: directed overflow metabolism, in which excess flux is diverted out of a pathway, and the pre-emption of damage from reactive metabolites.

Efficient searching and annotation of metabolic networks using chemical similarity

MOTIVATION

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks.

RESULTS

Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions.

AVAILABILITY AND IMPLEMENTATION

Python files will be available for download at http://tyolab.northwestern.edu/tools/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational evaluation of factors governing catalytic 2-keto acid decarboxylation

Recent advances in computational approaches for creating pathways for novel biochemical reactions has motivated the development of approaches for identifying enzyme-substrate pairs that are attractive candidates for effecting catalysis. We present an improved structural-based strategy to probe and study enzyme-substrate binding based on binding geometry, energy, and molecule characteristics, which allows for in silico screening of structural features that imbue higher catalytic potential with specific substrates. The strategy is demonstrated using 2-keto acid decarboxylation with various pairs of 2-keto acids and enzymes. We show that this approach fitted experimental values for a wide range of 2-keto acid decarboxylases for different 2-keto acid substrates. In addition, we show that the structure-based methods can be used to select specific enzymes that may be promising candidates to catalyze decarboxylation of certain 2-keto acids. The key features and principles of the candidate enzymes evaluated by the strategy can be used to design novel biosynthesis pathways, to guide enzymatic mutation or to guide biomimetic catalyst design.

Metabolic tinker: an online tool for guiding the design of synthetic metabolic pathways

One of the primary aims of synthetic biology is to (re)design metabolic pathways towards the production of desired chemicals. The fast pace of developments in molecular biology increasingly makes it possible to experimentally redesign existing pathways and implement de novo ones in microbes or using in vitro platforms. For such experimental studies, the bottleneck is shifting from implementation of pathways towards their initial design. Here, we present an online tool called ‘Metabolic Tinker’, which aims to guide the design of synthetic metabolic pathways between any two desired compounds. Given two user-defined ‘target’ and ‘source’ compounds, Metabolic Tinker searches for thermodynamically feasible paths in the entire known metabolic universe using a tailored heuristic search strategy. Compared with similar graph-based search tools, Metabolic Tinker returns a larger number of possible paths owing to its broad search base and fast heuristic, and provides for the first time thermodynamic feasibility information for the discovered paths. Metabolic Tinker is available as a web service at http://osslab.ex.ac.uk/tinker.aspx. The same website also provides the source code for Metabolic Tinker, allowing it to be developed further or run on personal machines for specific applications.

An algorithm for rapid computational construction of metabolic networks: a cholesterol biosynthesis example

Alternative pathways of metabolic networks represent the escape routes that can reduce drug efficacy and can cause severe adverse effects. In this paper we introduce a mathematical algorithm and a coding system for rapid computational construction of metabolic networks. The initial data for the algorithm are the source substrate code and the enzyme/metabolite interaction tables. The major strength of the algorithm is the adaptive coding system of the enzyme-substrate interactions. A reverse application of the algorithm is also possible, when optimisation algorithm is used to compute the enzyme/metabolite rules from the reference network structure. The coding system is user-defined and must be adapted to the studied problem. The algorithm is most effective for computation of networks that consist of metabolites with similar molecular structures. The computation of the cholesterol biosynthesis metabolic network suggests that 89 intermediates can theoretically be formed between lanosterol and cholesterol, only 20 are presently considered as cholesterol intermediates. Alternative metabolites may represent links with other metabolic networks both as precursors and metabolites of cholesterol. A possible cholesterol-by-pass pathway to bile acids metabolism through cholestanol is suggested.

Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis

Continued advances in metabolic engineering are increasing the number of small molecules being targeted for microbial production. Pathway yields and productivities, however, are often suboptimal, and strain improvement remains a persistent challenge given that the majority of small molecules are difficult to screen for and their biosynthesis does not improve host fitness. In this work, we have developed a generalized approach to screen or select for improved small-molecule biosynthesis using transcription factor-based biosensors. Using a tetracycline resistance gene 3' of a small-molecule inducible promoter, host antibiotic resistance, and hence growth rate, was coupled to either small-molecule concentration in the growth medium or a small-molecule production phenotype. Biosensors were constructed for two important chemical classes, dicarboxylic acids and alcohols, using transcription factor-promoter pairs derived from Pseudomonas putida, Thauera butanivorans, or E. coli. Transcription factors were selected for specific activation by either succinate, adipate, or 1-butanol, and we demonstrate product-dependent growth in E. coli using all three compounds. The 1-butanol biosensor was applied in a proof-of-principle liquid culture screen to optimize 1-butanol biosynthesis in engineered E. coli, identifying a pathway variant yielding a 35% increase in 1-butanol specific productivity through optimization of enzyme expression levels. Lastly, to demonstrate the capacity to select for enzymatic activity, the 1-butanol biosensor was applied as synthetic selection, coupling in vivo 1-butanol biosynthesis to E. coli fitness, and an 120-fold enrichment for a 1-butanol production phenotype was observed following a single round of positive selection.

Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.

An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds

Background

Prediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored.

Results

We formulated pathway prediction between a start compound and a goal compound as the shortest path search problem in terms of the number of enzyme reactions applied. We propose an efficient search method based on A* algorithm and heuristic techniques utilizing Linear Programming (LP) solution for estimation of the distance to the goal. First, a chemical compound is represented by a feature vector which counts frequencies of substructure occurrences in the structural formula. Second, an enzyme reaction is represented as an operator vector by detecting the structural changes to compounds before and after the reaction. By defining compound vectors as nodes and operator vectors as edges, prediction of the reaction pathway is reduced to the shortest path search problem in the vector space. In experiments on the DDT degradation pathway, we verify that the shortest paths predicted by our method are biologically correct pathways registered in the KEGG database. The results also demonstrate that the LP heuristics can achieve significant reduction in computation time. Furthermore, we apply our method to a secondary metabolite pathway of plant origin, and successfully find a novel biochemical pathway which cannot be predicted by the existing method. For the reconstruction of a known biochemical pathway, our method is over 40 times as fast as the existing method.

Conclusions

Our method enables fast and accurate de novo pathway predictions and novel pathway detection.

An in silico platform for the design of heterologous pathways in nonnative metabolite production

Background

Microorganisms are used as cell factories to produce valuable compounds in pharmaceuticals, biofuels, and other industrial processes. Incorporating heterologous metabolic pathways into well-characterized hosts is a major strategy for obtaining these target metabolites and improving productivity. However, selecting appropriate heterologous metabolic pathways for a host microorganism remains difficult owing to the complexity of metabolic networks. Hence, metabolic network design could benefit greatly from the availability of an in silico platform for heterologous pathway searching.

Results

We developed an algorithm for finding feasible heterologous pathways by which nonnative target metabolites are produced by host microorganisms, using Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae as templates. Using this algorithm, we screened heterologous pathways for the production of all possible nonnative target metabolites contained within databases. We then assessed the feasibility of the target productions using flux balance analysis, by which we could identify target metabolites associated with maximum cellular growth rate.

Conclusions

This in silico platform, designed for targeted searching of heterologous metabolic reactions, provides essential information for cell factory improvement.

Integrating computational methods to retrofit enzymes to synthetic pathways

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large-scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi-layer challenge, our strategy merges knowledge-based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate.

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.

Computational screening of novel thiamine-catalyzed decarboxylation reactions of 2-keto acids

A molecular modeling strategy to screen the capacity of known enzymes to catalyze the reactions of non-native substrates is presented. The binding of pyruvic acid and non-native ketoacids in the active site of pyruvate ferredoxin oxidoreductase was examined using docking analysis, and our results suggest that enzyme-non-native ketoacid-bound species are feasible. Quantum mechanics/molecular mechanics methods were then used to study the geometry of the covalent intermediate formed from the enzyme and the various ketoacids. Finally, quantum mechanical methods were used to study the decarboxylation reaction of 2-keto acids at the mechanistic level. This hierarchical screening ranked the substrates from those that cannot be accommodated by the enzyme (phenyl pyruvate) to those whose conversion rate would most closely approach that of the native substrate (2-ketobutanoic acid and 2-ketovaleric acid). Most notably, our investigation suggests that novel pathways generated using generalized enzyme actions may be screened using the hierarchical approach employed here.

Systems metabolic engineering: genome-scale models and beyond

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches--based on the data collected with high throughput technologies--to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.

Prediction of novel synthetic pathways for the production of desired chemicals

BACKGROUND

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism.

RESULTS

In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates.

CONCLUSIONS

It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

Background

Bioremediation offers a promising pollution treatment method in the reduction and elimination of man-made compounds in the environment. Computational tools to predict novel biodegradation pathways for pollutants allow one to explore the capabilities of microorganisms in cleaning up the environment. However, given the wealth of novel pathways obtained using these prediction methods, it is necessary to evaluate their relative feasibility, particularly within the context of the cellular environment.

Results

We have utilized a computational framework called BNICE to generate novel biodegradation routes for 1,2,4-trichlorobenzene (1,2,4-TCB) and incorporated the pathways into a metabolic model for Pseudomonas putida. We studied the cellular feasibility of the pathways by applying metabolic flux analysis (MFA) and thermodynamic constraints. We found that the novel pathways generated by BNICE enabled the cell to produce more biomass than the known pathway. Evaluation of the flux distribution profiles revealed that several properties influenced biomass production: 1) reducing power required, 2) reactions required to generate biomass precursors, 3) oxygen utilization, and 4) thermodynamic topology of the pathway. Based on pathway analysis, MFA, and thermodynamic properties, we identified several promising pathways that can be engineered into a host organism to accomplish bioremediation.

Conclusions

This work was aimed at understanding how novel biodegradation pathways influence the existing metabolism of a host organism. We have identified attractive targets for metabolic engineers interested in constructing a microorganism that can be used for bioremediation. Through this work, computational tools are shown to be useful in the design and evaluation of novel xenobiotic biodegradation pathways, identifying cellularly feasible degradation routes.

Computational framework for predictive biodegradation

As increasing amounts of anthropogenic chemicals are released into the environment, it is vital to human health and the preservation of ecosystems to evaluate the fate of these chemicals in the environment. It is useful to predict whether a particular compound is biodegradable and if alternate routes can be engineered for compounds already known to be biodegradable. In this work, we describe a computational framework (called BNICE) that can be used for the prediction of novel biodegradation pathways of xenobiotics. The framework was applied to 4-chlorobiphenyl, phenanthrene, gamma-hexachlorocyclohexane, and 1,2,4-trichlorobenzene, compounds representing various classes of xenobiotics with known biodegradation routes. BNICE reproduced the proposed biodegradation routes found experimentally, and in addition, it expanded the biodegradation reaction networks through the generation of novel compounds and reactions. The novel reactions involved in the biodegradation of 1,2,4-trichlorobenzene were studied in depth, where pathway and thermodynamic analyses were performed. This work demonstrates that BNICE can be applied to generate novel pathways to degrade xenobiotic compounds that are thermodynamically feasible alternatives to known biodegradation routes and attractive targets for metabolic engineering.

Assignment of EC numbers to enzymatic reactions with MOLMAP reaction descriptors and random forests

The MOLMAP descriptor relies on a Kohonen SOM that defines types of covalent bonds on the basis of their physicochemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants and numerically encodes the pattern of changes in bonds during a chemical reaction. In this study, a genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors and was explored for the assignment of the official EC number from the reaction equation with Random Forests as the machine learning algorithm. EC numbers were correctly assigned in 95%, 90%, and 85% (for independent test sets) at the class, subclass, and subsubclass EC number level, respectively, with training sets including one reaction from each available full EC number. Increasing differences between training and test sets were explored, leading to decreased percentages of correct assignments. The classification of reactions only from the main reactants and products was obtained at the class, subclass, and subsubclass level with accuracies of 78%, 74%, and 63%, respectively.

Metabolite and reaction inference based on enzyme specificities

Motivation: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra.

Results: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links.

Availability: Matlab and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/

Contact:d.deridder@tudelft.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

Towards the automated engineering of a synthetic genome

The development of the technology to synthesize new genomes and to introduce them into hosts with inactivated wild-type chromosome opens the door to new horizons in synthetic biology. Here it is of outmost importance to harness the ability of using computational design to predict and optimize a synthetic genome before attempting its synthesis. The methodology to computationally design a genome is based on an optimization that computationally mimics genome evolution. The biggest bottleneck lies on the use of an appropriate fitness function. This fitness function, usually cell growth, relies on the ability to quantitatively model the biochemical networks of the cell at the genome scale using parameters inferred from high-throughput data. Computational methods integrating such models in a common multilayer design platform can be used to automatically engineer synthetic genomes under physiological specifications. We describe the current state-of-the-art on automated methods for engineering or re-engineering synthetic genomes. We restrict ourselves to global models of metabolism, transcription and DNA structure. Although we are still far from the de novo computational genome design, it is important to collect all relevant work towards this goal. Finally, we discuss future perspectives about the practicability of an automated methodology for such computational design of synthetic genomes.

Synthetic metabolism: engineering biology at the protein and pathway scales

Biocatalysis has become a powerful tool for the synthesis of high-value compounds, particularly so in the case of highly functionalized and/or stereoactive products. Nature has supplied thousands of enzymes and assembled them into numerous metabolic pathways. Although these native pathways can be use to produce natural bioproducts, there are many valuable and useful compounds that have no known natural biochemical route. Consequently, there is a need for both unnatural metabolic pathways and novel enzymatic activities upon which these pathways can be built. Here, we review the theoretical and experimental strategies for engineering synthetic metabolic pathways at the protein and pathway scales, and highlight the challenges that this subfield of synthetic biology currently faces.