Prediction of novel synthetic pathways for the production of desired chemicals

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

A Novel Multi-Scale Graph Neural Network for Metabolic Pathway Prediction

Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgraph encoder, a feature encoder and a global feature processor, and a graph augmentation strategy is adopted. The subgraph encoder is responsible for extracting the local structural features of the compound, the feature encoder learns the characteristics of the atoms, and the global feature processor processes the information from the pre-training model and the two molecular fingerprints, while the graph augmentation strategy is to expand the train set through a scientific and reasonable method. The experiment result illustrates that the accuracy, precision, recall and F1 metrics of MSGNN reach 98.17%, 94.18%, 94.43% and 94.30%, respectively, which is superior to the similar models we have known. In addition, the ablation experiment demonstrates the indispensability of MSGNN modules.

Design and engineering of artificial biosynthetic pathways-where do we stand and where do we go?

The production of commodity and specialty chemicals relies heavily on fossil fuels. The negative impact of this dependency on our environment and climate has spurred a rising demand for more sustainable methods to obtain such chemicals from renewable resources. Herein, biotransformations of these renewable resources facilitated by enzymes or (micro)organisms have gained significant attention, since they can occur under mild conditions and reduce waste. These biotransformations typically leverage natural metabolic processes, which limits the scope and production capacity of such processes. In this mini-review, we provide an overview of advancements made in the past 5 years to expand the repertoire of biotransformations in engineered microorganisms. This ranges from redesign of existing pathways driven by retrobiosynthesis and computational design to directed evolution of enzymes and de novo pathway design to unlock novel routes for the synthesis of desired chemicals. We highlight notable examples of pathway designs for the production of commodity and specialty chemicals, showcasing the potential of these approaches. Lastly, we provide an outlook on future pathway design approaches.

MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference

Development of robust and effective strategies for synthesizing new compounds, drug targeting and constructing GEnome-scale Metabolic models (GEMs) requires a deep understanding of the underlying biological processes. A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https://github.com/guofei-tju/MVML-MPI. Contact:  jtang@cse.sc.edu, guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn.

Designing artificial pathways for improving chemical production

Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.

A Hitchhiker's Guide to Supplying Enzymatic Reducing Power into Synthetic Cells

The construction from scratch of synthetic cells by assembling molecular building blocks is unquestionably an ambitious goal from a scientific and technological point of view. To realize functional life-like systems, minimal enzymatic modules are required to sustain the processes underlying the out-of-equilibrium thermodynamic status hallmarking life, including the essential supply of energy in the form of electrons. The nicotinamide cofactors NAD(H) and NADP(H) are the main electron carriers fueling reductive redox reactions of the metabolic network of living cells. One way to ensure the continuous availability of reduced nicotinamide cofactors in a synthetic cell is to build a minimal enzymatic module that can oxidize an external electron donor and reduce NAD(P)+. In the diverse world of metabolism there is a plethora of potential electron donors and enzymes known from living organisms to provide reducing power to NAD(P)+ coenzymes. This perspective proposes guidelines to enable the reduction of nicotinamide cofactors enclosed in phospholipid vesicles, while avoiding high burdens of or cross-talk with other encapsulated metabolic modules. By determining key requirements, such as the feasibility of the reaction and transport of the electron donor into the cell-like compartment, we select a shortlist of potentially suitable electron donors. We review the most convenient proteins for the use of these reducing agents, highlighting their main biochemical and structural features. Noting that specificity toward either NAD(H) or NADP(H) imposes a limitation common to most of the analyzed enzymes, we discuss the need for specific enzymes─transhydrogenases─to overcome this potential bottleneck.

Designing Microbial Cell Factories for the Production of Chemicals

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction

Motivation

During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways.

Results

To address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery.

Availability and implementation

The code and data underlying this article are freely available at https://github.com/dubingxue/MLGL-MP.

Synthesis of C2-C4 diols from bioresources: Pathways and metabolic intervention strategies

Diols are important platform chemicals with extensive industrial applications in biopolymer synthesis, cosmetics, and fuels. The increased dependence on non-renewable sources to meet the energy requirement of the population raised issues regarding fossil fuel depletion and environmental impacts. The utilization of biological methods for the synthesis of diols by utilizing renewable resources such as glycerol and agro-residual wastes gained attention worldwide because of its advantages. Among these, biotransformation of 1,3-propanediol (1,3-PDO) and 2,3-butanediol (2,3-BDO) were extensively studied and at present, these diols are produced commercially in large scale with high yield. Many important isomers of C2-C4 diols lack natural synthetic pathways and development of chassis strains for the synthesis can be accomplished by adopting synthetic biology approaches. This current review depicts an overall idea about the pathways involved in C2-C4 diol production, metabolic intervention strategies and technologies in recent years.

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.

A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis

Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective method that can reduce a large number of the initially predicted enzymatic reactions has been needed. Here, we present Deep learning-based Reaction Feasibility Checker (DeepRFC) to classify the feasibility of a given enzymatic reaction with high performance and speed. DeepRFC is designed to receive Simplified Molecular-Input Line-Entry System (SMILES) strings of a reactant pair, which is defined as a substrate and a product of a reaction, as an input, and evaluates whether the input reaction is feasible. A deep neural network is selected for DeepRFC as it leads to better classification performance than five other representative machine learning methods examined. For validation, the performance of DeepRFC is compared with another in-house reaction feasibility checker that uses the concept of reaction similarity. Finally, the use of DeepRFC is demonstrated for the retrobiosynthesis-based design of novel one-carbon assimilation pathways. DeepRFC will allow retrobiosynthesis to be more practical for metabolic engineering applications by efficiently screening a large number of retrobiosynthesis-derived enzymatic reactions. DeepRFC is freely available at https://bitbucket.org/kaistsystemsbiology/deeprfc.

Microbial production of multiple short-chain primary amines via retrobiosynthesis

Bio-based production of many chemicals is not yet possible due to the unknown biosynthetic pathways. Here, we report a strategy combining retrobiosynthesis and precursor selection step to design biosynthetic pathways for multiple short-chain primary amines (SCPAs) that have a wide range of applications in chemical industries. Using direct precursors of 15 target SCPAs determined by the above strategy, Streptomyces viridifaciens vlmD encoding valine decarboxylase is examined as a proof-of-concept promiscuous enzyme both in vitro and in vivo for generating SCPAs from their precursors. Escherichia coli expressing the heterologous vlmD produces 10 SCPAs by feeding their direct precursors. Furthermore, metabolically engineered E. coli strains are developed to produce representative SCPAs from glucose, including the one producing 10.67 g L-1 of iso-butylamine by fed-batch culture. This study presents the strategy of systematically designing biosynthetic pathways for the production of a group of related chemicals as demonstrated by multiple SCPAs as examples.

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.

In silico design and automated learning to boost next-generation smart biomanufacturing

The increasing demand for bio-based compounds produced from waste or sustainable sources is driving biofoundries to deliver a new generation of prototyping biomanufacturing platforms. Integration and automation of the design, build, test and learn (DBTL) steps in centers like SYNBIOCHEM in Manchester and across the globe (Global Biofoundries Alliance) are helping to reduce the delivery time from initial strain screening and prototyping towards industrial production. Notably, a portfolio of producer strains for a suite of material monomers was recently developed, some approaching industrial titers, in a tour de force by the Manchester Centre that was achieved in less than 90 days. New in silico design tools are providing significant contributions to the front end of the DBTL pipelines. At the same time, the far-reaching initiatives of modern biofoundries are generating a large amount of high-dimensional data and knowledge that can be integrated through automated learning to expedite the DBTL cycle. In this Perspective, the new design tools and the role of the learning component as an enabling technology for the next generation of automated biofoundries are discussed. Future biofoundries will operate under completely automated DBTL cycles driven by in silico optimal experimental planning, full biomanufacturing devices connectivity, virtualization platforms and cloud-based design. The automated generation of robotic build worklists and the integration of machine-learning algorithms will collectively allow high levels of adaptability and rapid design changes toward fully automated smart biomanufacturing.

A deep learning architecture for metabolic pathway prediction

MOTIVATION

Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier. In contrast to previously applied machine learning methods for this problem, our framework automatically extracts relevant shape features directly from input SMILES representations, which are atom-bond specifications of chemical structures composing the molecules.

RESULTS

Our method is capable of correctly predicting the respective metabolic pathway class of 95.16% of tested compounds, whereas competing methods only achieve an accuracy of 84.92% or less. Furthermore, our framework extends to the task of classification of compounds having mixed membership in multiple pathway classes. Our prediction accuracy for this multi-label task is 97.61%. We analyze the relative importance of various global physicochemical features to the pathway class prediction problem and show that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using our framework.

AVAILABILITY AND IMPLEMENTATION

https://github.com/baranwa2/MetabolicPathwayPrediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Reinforcement Learning for Bioretrosynthesis

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

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.

Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites

Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations.

Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.

Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways

Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology.

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 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.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

Enzyme Discovery: Enzyme Selection and Pathway Design

In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering.

Computational Methods to Assess the Production Potential of Bio-Based Chemicals

Elevated costs and long implementation times of bio-based processes for producing chemicals represent a bottleneck for moving to a bio-based economy. A prospective analysis able to elucidate economically and technically feasible product targets at early research phases is mandatory. Computational tools can be implemented to explore the biological and technical spectrum of feasibility, while constraining the operational space for desired chemicals. In this chapter, two different computational tools for assessing potential for bio-based production of chemicals from different perspectives are described in detail. The first tool is GEM-Path: an algorithm to compute all structurally possible pathways from one target molecule to the host metabolome. The second tool is a framework for Modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes, and economic impact assessment. Integrating GEM-Path and MuSIC will play a vital role in supporting early phases of research efforts and guide the policy makers with decisions, as we progress toward planning a sustainable chemical industry.

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.

Predicting novel substrates for enzymes with minimal experimental effort with active learning

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.

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.

The Need for Integrated Approaches in Metabolic Engineering

This review highlights state-of-the-art procedures for heterologous small-molecule biosynthesis, the associated bottlenecks, and new strategies that have the potential to accelerate future accomplishments in metabolic engineering. We emphasize that a combination of different approaches over multiple time and size scales must be considered for successful pathway engineering in a heterologous host. We have classified these optimization procedures based on the "system" that is being manipulated: transcriptome, translatome, proteome, or reactome. By bridging multiple disciplines, including molecular biology, biochemistry, biophysics, and computational sciences, we can create an integral framework for the discovery and implementation of novel biosynthetic production routes.

ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies

Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( http://lcsb-databases.epfl.ch/atlas/ ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.

A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals

In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry.

Biomass, strain engineering, and fermentation processes for butanol production by solventogenic clostridia

Butanol is considered an attractive biofuel and a commercially important bulk chemical. However, economical production of butanol by solventogenic clostridia, e.g., via fermentative production of acetone-butanol-ethanol (ABE), is hampered by low fermentation performance, mainly as a result of toxicity of butanol to microorganisms and high substrate costs. Recently, sugars from marine macroalgae and syngas were recognized as potent carbon sources in biomass feedstocks that are abundant and do not compete for arable land with edible crops. With the aid of systems metabolic engineering, many researchers have developed clostridial strains with improved performance on fermentation of these substrates. Alternatively, fermentation strategies integrated with butanol recovery processes such as adsorption, gas stripping, liquid-liquid extraction, and pervaporation have been designed to increase the overall titer of butanol and volumetric productivity. Nevertheless, for economically feasible production of butanol, innovative strategies based on recent research should be implemented. This review describes and discusses recent advances in the development of biomass feedstocks, microbial strains, and fermentation processes for butanol production.

Engineering Biosynthesis Mechanisms for Diversifying Polyhydroxyalkanoates

Polyhydroxyalkanoates (PHA) are a family of diverse biopolyesters synthesized by bacteria. PHA diversity, as reflected by its monomers, homopolymers, random and block copolymers, as well as functional polymers, can now be generated by engineering the three basic synthesis pathways including the acetoacetyl-CoA pathway, in situ fatty acid synthesis, and/or β-oxidation cycles, as well as PHA synthase specificity. It is now possible to tailor the PHA structures via genome editing or process engineering. The increasing PHA diversity and maturing PHA production technology should lead to more focused research into their low-cost and/or high-value applications.

Synthetic Biology for Cell-Free Biosynthesis: Fundamentals of Designing Novel In Vitro Multi-Enzyme Reaction Networks

Cell-free biosynthesis in the form of in vitro multi-enzyme reaction networks or enzyme cascade reactions emerges as a promising tool to carry out complex catalysis in one-step, one-vessel settings. It combines the advantages of well-established in vitro biocatalysis with the power of multi-step in vivo pathways. Such cascades have been successfully applied to the synthesis of fine and bulk chemicals, monomers and complex polymers of chemical importance, and energy molecules from renewable resources as well as electricity. The scale of these initial attempts remains small, suggesting that more robust control of such systems and more efficient optimization are currently major bottlenecks. To this end, the very nature of enzyme cascade reactions as multi-membered systems requires novel approaches for implementation and optimization, some of which can be obtained from in vivo disciplines (such as pathway refactoring and DNA assembly), and some of which can be built on the unique, cell-free properties of cascade reactions (such as easy analytical access to all system intermediates to facilitate modeling).

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

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.

Advancing metabolic engineering through systems biology of industrial microorganisms

Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further.

Applications of genome-scale metabolic network model in metabolic engineering

Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

Development and application of efficient pathway enumeration algorithms for metabolic engineering applications

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modeling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cerevisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation.

Computational methods in metabolic engineering for strain design

Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms.