Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC)

Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search

Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search. The HybridMLP predicts precursors that will produce the target NPs, while the AND-OR tree generates the iterative multi-step biosynthetic pathways. The one-step bioretrosynthesis prediction experiments are conducted on MetaNetX dataset by using HybridMLP, which achieves 46.5%, 74.6%, 81.6% in terms of the top-1, top-5, top-10 accuracies. The great performance demonstrates the effectiveness of HybridMLP in one-step bioretrosynthesis. Besides, the evaluation of two benchmark datasets reveals that BioRetro can significantly improve the speed and success rate in predicting biosynthesis pathways. In addition, the BioRetro is further shown to find the synthetic pathway of compounds, such as ginsenoside F1 with the same substrates as reported but different enzymes, which may be the novel potential enzyme to have better catalytic performance.

PyMiner: A method for metabolic pathway design based on the uniform similarity of substrate-product pairs and conditional search

Metabolic pathway design is an essential step in the course of constructing an efficient microbial cell factory to produce high value-added chemicals. Meanwhile, the computational design of biologically meaningful metabolic pathways has been attracting much attention to produce natural and non-natural products. However, there has been a lack of effective methods to perform metabolic network reduction automatically. In addition, comprehensive evaluation indexes for metabolic pathway are still relatively scarce. Here, we define a novel uniform similarity to calculate the main substrate-product pairs of known biochemical reactions, and develop further an efficient metabolic pathway design tool named PyMiner. As a result, the redundant information of general metabolic network (GMN) is eliminated, and the number of substrate-product pairs is shown to decrease by 81.62% on average. Considering that the nodes in the extracted metabolic network (EMN) constructed in this work is large in scale but imbalanced in distribution, we establish a conditional search strategy (CSS) that cuts search time in 90.6% cases. Compared with state-of-the-art methods, PyMiner shows obvious advantages and demonstrates equivalent or better performance on 95% cases of experimentally verified pathways. Consequently, PyMiner is a practical and effective tool for metabolic pathway design.

Cell-Free Biosynthesis System: Methodology and Perspective of in Vitro Efficient Platform for Pyruvate Biosynthesis and Transformation

The modification of intracellular metabolic pathways by metabolic engineering has generated many engineered strains with relatively high yields of various target products in the past few decades. However, the unpredictable accumulation of toxic products, the cell membrane barrier, and competition between the carbon flux of cell growth and product synthesis have severely retarded progress toward the industrial-scale production of many essential chemicals. On the basis of an in-depth understanding of intracellular metabolic pathways, scientists intend to explore more sustainable methods and construct a cell-free biosynthesis system in vitro. In this review, the synthesis and application of pyruvate as a platform compound is used as an example to introduce cell-free biosynthesis systems. We systematically summarize a proposed methodology workflow of cell-free biosynthesis systems, including pathway design, enzyme mining, enzyme modification, multienzyme assembly, and pathway optimization. Some new methods, such as machine learning, are also mentioned in this review.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Data-driven rational biosynthesis design: from molecules to cell factories

A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.

MAPPS: A Web-Based Tool for Metabolic Pathway Prediction and Network Analysis in the Postgenomic Era

Comparative and evolutionary analyses of metabolic networks have a wide range of applications, ranging from research into metabolic evolution through to practical applications in drug development, synthetic biology, and biodegradation. We present MAPPS: Metabolic network Analysis and Pathway Prediction Server (https://mapps.lums.edu.pk), a web-based tool to study functions and evolution of metabolic networks using traditional and 'omics data sets. MAPPS provides diverse functionalities including an interactive interface, graphical visualization of results, pathway prediction and network comparison, identification of potential drug targets, in silico metabolic engineering, host-microbe interactions, and ancestral network building. Importantly, MAPPS also allows users to upload custom data, thus enabling metabolic analyses on draft and custom genomes, and has an 'omics pipeline to filter pathway results, making it relevant in today's postgenomic era.

CaMeRe: A Novel Tool for Inference of Cancer Metabolic Reprogramming

Metabolic reprogramming is prevalent in cancer, largely due to its altered chemical environments such as the distinct intracellular concentrations of O₂, H₂O₂ and H⁺, compared to those in normal tissue cells. The reprogrammed metabolisms are believed to play essential roles in cancer formation and progression. However, it is highly challenging to elucidate how individual normal metabolisms are altered in a cancer-promoting environment; hence for many metabolisms, our knowledge about how they are changed is limited. We present a novel method, CaMeRe (CAncer MEtabolic REprogramming), for identifying metabolic pathways in cancer tissues. Based on the specified starting and ending compounds, along with gene expression data of given cancer tissue samples, CaMeRe identifies metabolic pathways connecting the two compounds via collection of compatible enzymes, which are most consistent with the provided gene-expression data. In addition, cancer-specific knowledge, such as the expression level of bottleneck enzymes in the pathways, is incorporated into the search process, to enable accurate inference of cancer-specific metabolic pathways. We have applied this tool to predict the altered sugar-energy metabolism in cancer, referred to as the Warburg effect, and found the prediction result is highly accurate by checking the appearance and ranking of those key pathways in the results of CaMeRe. Computational evaluation indicates that the tool is fast and capable of handling large metabolic network inference in cancer tissues. Hence, we believe that CaMeRe offers a powerful tool to cancer researchers for their discovery of reprogrammed metabolisms in cancer. The URL of CaMeRe is http://csbl.bmb.uga.edu/CaMeRe/.

ASAPP: Architectural Similarity-Based Automated Pathway Prediction System and Its Application in Host-Pathogen Interactions

The significance of metabolic pathway prediction is to envision the viable unknown transformations that can occur provided the appropriate enzymes are present. It can facilitate the prediction of the consequences of host-pathogen interactions. In this article, we have proposed a new algorithm Architectural Similarity-based Automated Pathway Prediction (ASAPP) to predict metabolic pathways based on the structural similarity among the metabolites. ASAPP takes two-dimensional structure and molecular weight of metabolites as input, and generates a list of probable transformations without the knowledge of any externally established reactions, with an accuracy of 85.09 percent. ASAPP has also been applied to predict the outcome of pathogen liberated toxins on the carbohydrate and lipid pathways of the hosts. We have analyzed the disruption of host pathways in the presence of toxins, and have found that some metabolites in Glycolysis and the TCA cycle have a high chance of being the breakpoints in the pathway. The tool is available at http://asapp.droppages.com/.

Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

Background

The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways.

Results

A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest.

Conclusions

This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results

Motivation

Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint.

Results

MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy (HE). MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked α-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of HE, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases.

Availability and implementation

Method is implemented in the MetExplore server and is available at www.metexplore.fr. A tutorial is available at https://metexplore.toulouse.inra.fr/com/tutorials/MetaboRank/2017-MetaboRank.pdf.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolic pathways synthesis based on ant colony optimization

One of the current challenges in bioinformatics is to discover new ways to transform a set of compounds into specific products. The usual approach is finding the reactions to synthesize a particular product, from a given substrate, by means of classical searching algorithms. However, they have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. We present here a novel bio-inspired algorithm for synthesizing linear and branched metabolic pathways. It allows relating several compounds simultaneously, ensuring the availability of substrates for every reaction in the solution. Comparisons with classical searching algorithms and other recent metaheuristic approaches show clear advantages of this proposal, fully recovering well-known pathways. Furthermore, solutions found can be analyzed in a simple way through graphical representations on the web.

A pathway for every product? Tools to discover and design plant metabolism

The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.

Pathway design using de novo steps through uncharted biochemical spaces

Existing retrosynthesis tools generally traverse production routes from a source to a sink metabolite using known enzymes or de novo steps. Generally, important considerations such as blending known transformations with putative steps, complexity of pathway topology, mass conservation, cofactor balance, thermodynamic feasibility, microbial chassis selection, and cost are largely dealt with in a posteriori fashion. The computational procedure we present here designs bioconversion routes while simultaneously considering any combination of the aforementioned design criteria. First, we track and codify as rules all reaction centers using a prime factorization-based encoding technique (rePrime). Reaction rules and known biotransformations are then simultaneously used by the pathway design algorithm (novoStoic) to trace both metabolites and molecular moieties through balanced bio-conversion strategies. We demonstrate the use of novoStoic in bypassing steps in existing pathways through putative transformations, assembling complex pathways blending both known and putative steps toward pharmaceuticals, and postulating ways to biodegrade xenobiotics.

Identification of metabolic pathways using pathfinding approaches: a systematic review

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.

EcoSynther: A Customized Platform To Explore the Biosynthetic Potential in E. coli

Developing computational tools for a chassis-centered biosynthetic pathway design is very important for a productive heterologous biosynthesis system by considering enormous foreign biosynthetic reactions. For many cases, a pathway to produce a target molecule consists of both native and heterologous reactions when utilizing a microbial organism as the host organism. Due to tens of thousands of biosynthetic reactions existing in nature, it is not trivial to identify which could be served as heterologous ones to produce the target molecule in a specific organism. In the present work, we integrate more than 10,000 E. coli non-native reactions and utilize a probability-based algorithm to search pathways. Moreover, we built a user-friendly Web server named EcoSynther. It is able to explore the precursors and heterologous reactions needed to produce a target molecule in Escherichia coli K12 MG1655 and then applies flux balance analysis to calculate theoretical yields of each candidate pathway. Compared with other chassis-centered biosynthetic pathway design tools, EcoSynther has two unique features: (1) allow for automatic search without knowing a precursor in E. coli and (2) evaluate the candidate pathways under constraints from E. coli physiological states and growth conditions. EcoSynther is available at http://www.rxnfinder.org/ecosynther/ .

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Systems Metabolic Engineering of Escherichia coli

Systems metabolic engineering, which recently emerged as metabolic engineering integrated with systems biology, synthetic biology, and evolutionary engineering, allows engineering of microorganisms on a systemic level for the production of valuable chemicals far beyond its native capabilities. Here, we review the strategies for systems metabolic engineering and particularly its applications in Escherichia coli. First, we cover the various tools developed for genetic manipulation in E. coli to increase the production titers of desired chemicals. Next, we detail the strategies for systems metabolic engineering in E. coli, covering the engineering of the native metabolism, the expansion of metabolism with synthetic pathways, and the process engineering aspects undertaken to achieve higher production titers of desired chemicals. Finally, we examine a couple of notable products as case studies produced in E. coli strains developed by systems metabolic engineering. The large portfolio of chemical products successfully produced by engineered E. coli listed here demonstrates the sheer capacity of what can be envisioned and achieved with respect to microbial production of chemicals. Systems metabolic engineering is no longer in its infancy; it is now widely employed and is also positioned to further embrace next-generation interdisciplinary principles and innovation for its upgrade. Systems metabolic engineering will play increasingly important roles in developing industrial strains including E. coli that are capable of efficiently producing natural and nonnatural chemicals and materials from renewable nonfood biomass.

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.

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.

MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind

To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. We developed a novel web server, called MRE, which, for a given pair of starting and desired compounds in a given chassis organism, ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. These unique, chassis-centered features distinguish MRE from existing pathway design tools and allow synthetic biologists to evaluate the design of their biosynthesis systems from a different angle. By using biosynthesis of a range of high-value natural products as a case study, we show that MRE is an effective tool to guide the design and optimization of heterologous biosynthesis pathways. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/.

Evolutionary algorithm for metabolic pathways synthesis

Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear sequences of reactions linking two compounds. However, an important limitation of these methods is the exponential increase of search trees when a large number of compounds and reactions is considered. Besides, such models do not take into account all substrates for each reaction during the search, leading to solutions that lack biological feasibility in many cases. This work proposes a new evolutionary algorithm that allows searching not only linear, but also branched metabolic pathways, formed by feasible reactions that relate multiple compounds simultaneously. Tests performed using several sets of reactions show that this algorithm is able to find feasible linear and branched metabolic pathways.

An overview of bioinformatics methods for modeling biological pathways in yeast

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed.

Computational methods to identify metabolic sub-networks based on metabolomic profiles

Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction

MOTIVATION

A fundamental computational problem in the area of metabolic engineering is finding metabolic pathways between a pair of source and target metabolites efficiently. We present an approach, namely FogLight, for searching metabolic networks utilizing Boolean (AND-OR) operations represented in matrix notation to efficiently reduce the search space. This enables the enumeration of all pathways between metabolites that are too distant for the application of brute-force methods.

RESULTS

Benchmarking tests run with FogLight show that it can reduce the search space by up to 98%, after which the accelerated search for high accurate results is guaranteed. Using FogLight, several pathways between eight given pairs of metabolites are found of which the pathways from CO2 to ethanol are specifically discussed. Additionally, in comparison with three path-finding tools, namely PHT, FMM and RouteSearch, FogLight can find shorter and more pathways for attempted source-target metabolite pairs.

CONTACT

szamani@aut.ac.ir, gholamreza.bidkhori@vtt.fi

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

EvoMS: An evolutionary tool to find de novo metabolic pathways

The evolutionary metabolic synthesizer (EvoMS) is an evolutionary tool capable of finding novel metabolic pathways linking several compounds through feasible reactions. It allows system biologists to explore different alternatives for relating specific metabolites, offering the possibility of indicating the initial compound or allowing the algorithm to automatically select it. Searching process can be followed graphically through several plots of the evolutionary process. Metabolic pathways found are displayed in a web browser as directed graphs. In all cases, solutions are networks of reactions that produce linear or branched metabolic pathways which are feasible from the specified set of available compounds. Source code of EvoMS is available at http://sourceforge.net/projects/sourcesinc/files/evoms/. Subsets of reactions are provided, as well as four examples for searching metabolic pathways among several compounds. Available as a web service at http://fich.unl.edu.ar/sinc/web-demo/evoms/.

EC-BLAST: a tool to automatically search and compare enzyme reactions

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering.

Elucidation of metabolic pathways from enzyme classification data

The IUBMB Enzyme List is widely used by other databases as a source for avoiding ambiguity in the recognition of enzymes as catalytic entities. However, it was not designed for metabolic pathway tracing, which has become increasingly important in systems biology. A Reactions Database has been created from the material in the Enzyme List to allow reactions to be searched by substrate/product, and pathways to be traced from any selected starting/seed substrate. An extensive synonym glossary allows searches by many of the alternative names, including accepted abbreviations, by which a chemical compound may be known. This database was necessary for the development of the application Reaction Explorer ( http://www.reaction-explorer.org ), which was written in Real Studio ( http://www.realsoftware.com/realstudio/ ) to search the Reactions Database and draw metabolic pathways from reactions selected by the user. Having input the name of the starting compound (the "seed"), the user is presented with a list of all reactions containing that compound and then selects the product of interest as the next point on the ensuing graph. The pathway diagram is then generated as the process iterates. A contextual menu is provided, which allows the user: (1) to remove a compound from the graph, along with all associated links; (2) to search the reactions database again for additional reactions involving the compound; (3) to search for the compound within the Enzyme List.

DRUG TARGET PRIORITIZATION IN PLASMODIUM FALCIPARUM THROUGH METABOLIC NETWORK ANALYSIS, AND INHIBITOR DESIGNING USING VIRTUAL SCREENING AND DOCKING APPROACH

The genome sequence of Plasmodium falciparum reveals that many metabolic pathways are unique as compared to its human host. Metabolic Network Analysis was carried out to find the essential enzymes critical for the survival of the pathogen. In the present study, choke point and load point analysis was used to locate putative targets. The identified targets were further checked to confirm that no alternate pathway or human homolog exists. Among the top 15 enzymes obtained from this analysis, we have selected P. falciparum orotidine-5'-monophosphate decarboxylase (PfODCase) enzyme as it is sequentially and structurally different from that of humans, for searching novel inhibitors. A five-point 3D pharmacophore was generated for the crystal structure of PfODCase complexes with uridine-5'-monophosphate (U5P). The binding site environment shows three H-bond acceptors, one H-bond donor and one negative ionizable feature. This pharmacophore model was used as a 3D query to perform virtual screening experiments against 2,664,779 standard lead compounds obtained from the freely available ZINC database. Top 10 hits obtained from virtual screening were selected for molecular docking experiments against PfODCase in order to verify their results and to have a better insight into their binding modes. Here, docking of U5P with PfODCase is used as a control. We have identified six compounds, among them, few are U5P analogs and others are novel ones with diverse scaffolds. The key residues: Lys42, Asp20, Lys72, Ser127, Ala184, Gln185 and Arg203 at the main binding pocket of PfODCase are responsible for better stability of diverse ligands. These compounds according to their free energy of binding could serve as potent leads for designing novel inhibitors against malarial ODCase enzyme.

CMPF: class-switching minimized pathfinding in metabolic networks

Background

The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell.

Results

We propose a path weighting method CMPF (Class-switching Minimized Pathfinder) to search for routes in a metabolic network which minimizes pathway switching. In biological terms, a pathway is a series of chemical reactions which define a specific function (e.g. glycolysis). We conjecture that routes that cross many pathways are inefficient since different pathways define different metabolic functions. In addition, native routes are also well characterized within pathways, suggesting that reasonable paths should not involve too many pathway switches. Our method can be generalized when reactions participate in a class set (e.g., pathways, species or cellular localization) so that the paths predicted have minimal class crossings.

Conclusions

We show that our method generates k-paths that involve the least number of class switching. In addition, we also show that native paths are recoverable and alternative paths deviates less from native paths compared to other methods. This suggests that paths ranked by our method could be a way to predict paths that are likely to occur in biological systems.

SAGPAR: structural grammar-based automated pathway reconstruction

In-silico metabolic engineering is a very useful branch of systems biology for modeling, analysis and prediction of various outcomes of metabolic pathways. It can also be used for detecting interactions and dynamics within a network. Various protocols have been proposed for modeling a pathway. But most of these protocols have various disadvantages and shortcomings with respect to automated pathway modeling and analysis. In the present article, we have proposed a novel algorithm for automated pathway reconstruction. We have also made a comparative study of our algorithm with other standard protocols and discussed its advantages over others. We present StructurAl Grammar-based automated PAthway Reconstruction (SAGPAR), a fast and robust algorithm that generates any metabolic pathway using some given structural representations of metabolites. Users can model any pathway based on some pre-required features that are asked as an input by the algorithm. The algorithm also takes into considerations various thermodynamic thresholds and structural properties while modeling a pathway. The given algorithm has been tested on the standard pathway datasets of 25 pathways of Mycoplasma pneumoniae M129 and 24 pathways of Homo sapiens. The dataset is taken from KEGG and PubChem Compound data repositories. SAGPAR performs much better than some already present metabolic pathway analysis tools like Copasi, PHT, Gepasi, Jarnac and Path-A.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.

Genomic analysis of the hydrocarbon-producing, cellulolytic, endophytic fungus Ascocoryne sarcoides

The microbial conversion of solid cellulosic biomass to liquid biofuels may provide a renewable energy source for transportation fuels. Endophytes represent a promising group of organisms, as they are a mostly untapped reservoir of metabolic diversity. They are often able to degrade cellulose, and they can produce an extraordinary diversity of metabolites. The filamentous fungal endophyte Ascocoryne sarcoides was shown to produce potential-biofuel metabolites when grown on a cellulose-based medium; however, the genetic pathways needed for this production are unknown and the lack of genetic tools makes traditional reverse genetics difficult. We present the genomic characterization of A. sarcoides and use transcriptomic and metabolomic data to describe the genes involved in cellulose degradation and to provide hypotheses for the biofuel production pathways. In total, almost 80 biosynthetic clusters were identified, including several previously found only in plants. Additionally, many transcriptionally active regions outside of genes showed condition-specific expression, offering more evidence for the role of long non-coding RNA in gene regulation. This is one of the highest quality fungal genomes and, to our knowledge, the only thoroughly annotated and transcriptionally profiled fungal endophyte genome currently available. The analyses and datasets contribute to the study of cellulose degradation and biofuel production and provide the genomic foundation for the study of a model endophyte system.

A renewable source of energy is a pressing global need. The biological conversion of lignocellulose to biofuels by microorganisms presents a promising avenue, but few organisms have been studied thoroughly enough to develop the genetic tools necessary for rigorous experimentation. The filamentous-fungal endophyte A. sarcoides produces metabolites when grown on a cellulose-based medium that include eight-carbon volatile organic compounds, which are potential biofuel targets. Here we use broadly applicable methods including genomics, transcriptomics, and metabolomics to explore the biofuel production of A. sarcoides. These data were used to assemble the genome into 16 scaffolds, to thoroughly annotate the cellulose-degradation machinery, and to make predictions for the production pathway for the eight-carbon volatiles. Extremely high expression of the gene swollenin when grown on cellulose highlights the importance of accessory proteins in addition to the enzymes that catalyze the breakdown of the polymers. Correlation of the production of the eight-carbon biofuel-like metabolites with the expression of lipoxygenase pathway genes suggests the catabolism of linoleic acid as the mechanism of eight-carbon compound production. This is the first fungal genome to be sequenced in the family Helotiaceae, and A. sarcoides was isolated as an endophyte, making this work also potentially useful in fungal systematics and the study of plant–fungus relationships.

Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms

Background

We consider the possibility of engineering metabolic pathways in a chassis organism in order to synthesize novel target compounds that are heterologous to the chassis. For this purpose, we model metabolic networks through hypergraphs where reactions are represented by hyperarcs. Each hyperarc represents an enzyme-catalyzed reaction that transforms set of substrates compounds into product compounds. We follow a retrosynthetic approach in order to search in the metabolic space (hypergraphs) for pathways (hyperpaths) linking the target compounds to a source set of compounds.

Results

To select the best pathways to engineer, we have developed an objective function that computes the cost of inserting a heterologous pathway in a given chassis organism. In order to find minimum-cost pathways, we propose in this paper two methods based on steady state analysis and network topology that are to the best of our knowledge, the first to enumerate all possible heterologous pathways linking a target compounds to a source set of compounds. In the context of metabolic engineering, the source set is composed of all naturally produced chassis compounds (endogenuous chassis metabolites) and the target set can be any compound of the chemical space. We also provide an algorithm for identifying precursors which can be supplied to the growth media in order to increase the number of ways to synthesize specific target compounds.

Conclusions

We find the topological approach to be faster by several orders of magnitude than the steady state approach. Yet both methods are generally scalable in time with the number of pathways in the metabolic network. Therefore this work provides a powerful tool for pathway enumeration with direct application to biosynthetic pathway design.

From metabolic reactions to networks and pathways

Enzymatic reactions form a hypergraph structure and their translation into a graph structure accompanies an information loss. This chapter introduces well-known topological transformations from metabolic reactions to a graph, and discusses their advantages and disadvantages. Also discussed is the legitimacy of defining cofactors or currency metabolites, and suitable application area of each representation.

An algorithm for efficient identification of branched metabolic pathways

This article presents a new graph-based algorithm for identifying branched metabolic pathways in multi-genome scale metabolic data. The term branched is used to refer to metabolic pathways between compounds that consist of multiple pathways that interact biochemically. A branched pathway may produce a target compound through a combination of linear pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While branched metabolic pathways predominate in metabolic networks, most previous work has focused on identifying linear metabolic pathways. The ability to automatically identify branched pathways is important in applications that require a deeper understanding of metabolism, such as metabolic engineering and drug target identification. The algorithm presented in this article utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on several well-characterized metabolic pathways that demonstrate that the new merging approach can efficiently find biologically relevant branched metabolic pathways.

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

BACKGROUND

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 in situ 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.

RESULTS

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.

CONCLUSIONS

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.

Engineering antibiotic production and overcoming bacterial resistance

Progress in DNA technology, analytical methods and computational tools is leading to new developments in synthetic biology and metabolic engineering, enabling new ways to produce molecules of industrial and therapeutic interest. Here, we review recent progress in both antibiotic production and strategies to counteract bacterial resistance to antibiotics. Advances in sequencing and cloning are increasingly enabling the characterization of antibiotic biosynthesis pathways, and new systematic methods for de novo biosynthetic pathway prediction are allowing the exploration of the metabolic chemical space beyond metabolic engineering. Moreover, we survey the computer-assisted design of modular assembly lines in polyketide synthases and non-ribosomal peptide synthases for the development of tailor-made antibiotics. Nowadays, production of novel antibiotic can be tranferred into any chosen chassis by optimizing a host factory through specific strain modifications. These advances in metabolic engineering and synthetic biology are leading to novel strategies for engineering antimicrobial agents with desired specificities.

Prediction of metabolic pathways from genome-scale metabolic networks

The analysis of a variety of data sets (transcriptome arrays, phylogenetic profiles, etc.) yields groups of functionally related genes. In order to determine their biological function, associated gene groups are often projected onto known pathways or tested for enrichment of known functions. However, these approaches are not flexible enough to deal with variations or novel pathways. During the last decade, we developed and refined an approach that predicts metabolic pathways from a global metabolic network encompassing all known reactions and their substrates/products, by extracting a subgraph connecting at best a set of seed nodes (compounds, reactions, enzymes or enzyme-coding genes). In this review, we summarize this work, while discussing the problems and pitfalls but also the advantages and applications of network-based metabolic pathway prediction.

Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks.

The strength of chemical linkage as a criterion for pruning metabolic graphs

MOTIVATION

A metabolic graph represents the connectivity patterns of a metabolic system, and provides a powerful framework within which the organization of metabolic reactions can be analyzed and elucidated. A common practice is to prune (i.e. remove nodes and edges) the metabolic graph prior to any analysis in order to eliminate confounding signals from the representation. Currently, this pruning process is carried out in an ad hoc fashion, resulting in discrepancies and ambiguities across studies.

RESULTS

We propose a biochemically informative criterion, the strength of chemical linkage (SCL), for a systematic pruning of metabolic graphs. By analyzing the metabolic graph of Escherichia coli, we show that thresholding SCL is powerful in selecting the conventional pathways' connectivity out of the raw network connectivity when the network is restricted to the reactions collected from these pathways. Further, we argue that the root of ambiguity in pruning metabolic graphs is in the continuity of the amount of chemical content that can be conserved in reaction transformation patterns. Finally, we demonstrate how biochemical pathways can be inferred efficiently if the search procedure is guided by SCL.

MRSD: a web server for metabolic route search and design

SUMMARY

We present a tool called MRSD (Metabolic Route Search and Design) to search and design routes based on the weighted compound transform diagraph. The search submodule returns routes between a source and product compound within seconds in the network of one or multiple organisms based on data from KEGG. The design submodule designs a route from an appointed compound in an interactive mode. The two complementary functions, Metabolic Route Search and Design, can be broadly used in biosynthesis, bio-pharmaceuticals and the other related fields.

AVAILABILITY

bioinfo.ustc.edu.cn/softwares/MRSD/.

Bioinformatic-driven search for metabolic biomarkers in disease

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.