Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways

Sulfamethoxazole degradation pathways in wastewater treatment: Bayesian network-based approach for a meta-analysis of scientific papers

Given the widespread presence of micropollutants in urban water systems, it is imperative to gain a comprehensive understanding of their degradation pathways. This paper focuses on sulfamethoxazole (SMX) as a model molecule due to its extensive study, aiming to elucidate its degradation pathways in biological (BIO) and oxidative (AOP) processes. Numerous reaction pathways are outlined in scientific papers. However, a significant deficiency in current methodologies has led to the development of a novel meta-analytical approach, seeking consensus among researchers by synthesizing data from studies characterized by their heterogeneity and contradictions. As an innovative alternative, probabilistic graphical models such as Bayesian networks (BNs) could illuminate the relationships and dependencies between various transformation products, providing a holistic view of the degradation process. Based on the analysis of an extensive bibliography gathering more than 45 articles for more than 140 molecules and 177 reaction pathways, this study proposes a meta-analysis methodology based on Bayesian networks.

Deep learning in template-free de novo biosynthetic pathway design of natural products

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.

Perspective on Lignin Conversion Strategies That Enable Next Generation Biorefineries

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.

Developing BioNavi for Hybrid Retrosynthesis Planning

Illuminating synthetic pathways is essential for producing valuable chemicals, such as bioactive molecules. Chemical and biological syntheses are crucial, and their integration often leads to more efficient and sustainable pathways. Despite the rapid development of retrosynthesis models, few of them consider both chemical and biological syntheses, hindering the pathway design for high-value chemicals. Here, we propose BioNavi by innovating multitask learning and reaction templates into the deep learning-driven model to design hybrid synthesis pathways in a more interpretable manner. BioNavi outperforms existing approaches on different data sets, achieving a 75% hit rate in replicating reported biosynthetic pathways and displaying superior ability in designing hybrid synthesis pathways. Additional case studies further illustrate the potential application of BioNavi in a de novo pathway design. The enhanced web server (http://biopathnavi.qmclab.com/bionavi/) simplifies input operations and implements step-by-step exploration according to user experience. We show that BioNavi is a handy navigator for designing synthetic pathways for various chemicals.

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.

Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in Escherichia coli

Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process. We previously reported a biosynthetic route that produces IPDO via extending the leucine catabolism (route A), the efficiency of which, however, is not satisfactory. To address this issue, we computationally designed a novel non-natural IPDO synthesis pathway (route B) using RetroPath RL, the state-of-the-art tool for bioretrosynthesis based on artificial intelligence methods. We compared this new pathway with route A and two other intuitively designed routes for IPDO biosynthesis from various perspectives. Route B, which exhibits the highest thermodynamic driving force, least non-native reaction steps, and lowest energy requirements, appeared to hold the greatest potential for IPDO production. All three newly designed routes were then implemented in the Escherichia coli BL21(DE3) strain. Results show that the computationally designed route B can produce 2.2 mg/L IPDO from glucose but no IPDO production from routes C and D. These results highlight the importance and usefulness of in silico design and comprehensive evaluation of the potential efficiencies of candidate pathways in constructing novel non-natural pathways for the production of biochemicals.

Deep learning for metabolic pathway design

The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.

Microbe-Plant Interactions Targeting Metal Stress: New Dimensions for Bioremediation Applications

In the age of industrialization, numerous non-biodegradable pollutants like plastics, HMs, polychlorinated biphenyls, and various agrochemicals are a serious concern. These harmful toxic compounds pose a serious threat to food security because they enter the food chain through agricultural land and water. Physical and chemical techniques are used to remove HMs from contaminated soil. Microbial-metal interaction, a novel but underutilized strategy, might be used to lessen the stress caused by metals on plants. For reclaiming areas with high levels of heavy metal contamination, bioremediation is effective and environmentally friendly. In this study, the mechanism of action of endophytic bacteria that promote plant growth and survival in polluted soils-known as heavy metal-tolerant plant growth-promoting (HMT-PGP) microorganisms-and their function in the control of plant metal stress are examined. Numerous bacterial species, such as Arthrobacter, Bacillus, Burkholderia, Pseudomonas, and Stenotrophomonas, as well as a few fungi, such as Mucor, Talaromyces, Trichoderma, and Archaea, such as Natrialba and Haloferax, have also been identified as potent bioresources for biological clean-up. In this study, we additionally emphasize the role of plant growth-promoting bacteria (PGPB) in supporting the economical and environmentally friendly bioremediation of heavy hazardous metals. This study also emphasizes future potential and constraints, integrated metabolomics approaches, and the use of nanoparticles in microbial bioremediation for HMs.

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.

Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications

Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering (TE) and regenerative medicine. In contrast to conventional biomaterials or synthetic materials, biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix (ECM). Additionally, such materials have mechanical adaptability, microstructure interconnectivity, and inherent bioactivity, making them ideal for the design of living implants for specific applications in TE and regenerative medicine. This paper provides an overview for recent progress of biomimetic natural biomaterials (BNBMs), including advances in their preparation, functionality, potential applications and future challenges. We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM. Moreover, we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications. Finally, we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.

Merging enzymatic and synthetic chemistry with computational synthesis planning

Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which are valuable due to their selectivity and sustainability but are few in number. We report a retrosynthetic search algorithm using two neural network models for retrosynthesis-one covering 7984 enzymatic transformations and one 163,723 synthetic transformations-that balances the exploration of enzymatic and synthetic reactions to identify hybrid synthesis plans. This approach extends the space of retrosynthetic moves by thousands of uniquely enzymatic one-step transformations, discovers routes to molecules for which synthetic or enzymatic searches find none, and designs shorter routes for others. Application to (-)-Δ⁹ tetrahydrocannabinol (THC) (dronabinol) and R,R-formoterol (arformoterol) illustrates how our strategy facilitates the replacement of metal catalysis, high step counts, or costly enantiomeric resolution with more elegant hybrid proposals.

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.

Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP

The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs.

The complete biosynthetic pathway from most natural products (NPs) are unknown. Here, the authors report BioNavi-NP, a computational toolkit for bio-retrosynthetic pathway elucidation or reconstruction for both NPs and NP-like compounds.

Design, Analysis, and Implementation of a Novel Biochemical Pathway for Ethylene Glycol Production in Clostridium autoethanogenum

The platform chemical ethylene glycol (EG) is used to manufacture various commodity chemicals of industrial importance, but largely remains synthesized from fossil fuels. Although several novel metabolic pathways have been reported for its bioproduction in model organisms, none has been reported for gas-fermenting, non-model acetogenic chassis organisms. Here, we describe a novel, synthetic biochemical pathway to convert acetate into EG in the industrially important gas-fermenting acetogen,Clostridium autoethanogenum. We not only developed a computational workflow to design and analyze hundreds of novel biochemical pathways for EG production but also demonstrated a successful pathway construction in the chosen host. The EG production was achieved using a two-plasmid system to bypass unfeasible expression levels and potential toxic enzymatic interactions. Although only a yield of 0.029 g EG/g fructose was achieved and therefore requiring further strain engineering efforts to optimize the designed strain, this work demonstrates an important proof-of-concept approach to computationally design and experimentally implement fully synthetic metabolic pathways in a metabolically highly specific, non-model host organism.

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.

Computational tools and resources for designing new pathways to small molecules

The metabolic engineering community relies on computational methods for pathway design to produce important small molecules in microbial hosts. Metabolic network databases are continuously curated and updated with known and novel reactions that expand the known biochemistry based on different sets of enzymatic reaction rules. To address the complexity of the metabolic networks, elaborate methods were developed to transform them into computable graphs, navigate them, and construct the best possible pathways. However, the recent experimental research points to the new challenges and opportunities for the computational pathway design. Here, we review the most recent advances, especially in the last two years, in computational discovery of new pathways and their prospects for expanding metabolic capabilities. We draw attention to the potential ways of improvement for pathway design algorithms, including the expansion of Design-Build-Test-Learn cycle to novel compounds and reactions and the standardization for the reaction rules and metabolic reaction databases.

Underground metabolism as a rich reservoir for pathway engineering

Motivation

Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level ‘underground’ enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering.

Results

Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development.

Availability and implementation

The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism.

Supplementary information

Supplementary data are available at Bioinformatics online.

Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx

Metabolic “dark matter” describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes (https://lcsb-databases.epfl.ch/Atlas2).

“Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.”

Enzymatic strategies for asymmetric synthesis

Enzymes, at the turn of the 21st century, are gaining a momentum. Especially in the field of synthetic organic chemistry, a broad variety of biocatalysts are being applied in an increasing number of processes running at up to industrial scale. In addition to the advantages of employing enzymes under environmentally friendly reaction conditions, synthetic chemists are recognizing the value of enzymes connected to the exquisite selectivity of these natural (or engineered) catalysts. The use of hydrolases in enantioselective protocols paved the way to the application of enzymes in asymmetric synthesis, in particular in the context of biocatalytic (dynamic) kinetic resolutions. After two decades of impressive development, the field is now mature to propose a panel of catalytically diverse enzymes for (i) stereoselective reactions with prochiral compounds, such as double bond reduction and bond forming reactions, (ii) formal enantioselective replacement of one of two enantiotopic groups of prochiral substrates, as well as (iii) atroposelective reactions with noncentrally chiral compounds. In this review, the major enzymatic strategies broadly applicable in the asymmetric synthesis of optically pure chiral compounds are presented, with a focus on the reactions developed within the past decade.

Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism

Diauxie, or the sequential consumption of carbohydrates in bacteria such as Escherichia coli, has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive advantage. Currently, the computational techniques used in industrial biotechnology fall short of explaining the intracellular dynamics underlying diauxic behavior. In particular, the understanding of the proteome dynamics in diauxie can be improved. We developed a robust iterative dynamic method based on expression- and thermodynamically enabled flux models to simulate the kinetic evolution of carbohydrate consumption and cellular growth. With minimal modeling assumptions, we couple kinetic uptakes, gene expression, and metabolic networks, at the genome scale, to produce dynamic simulations of cell cultures. The method successfully predicts the preferential uptake of glucose over lactose in E. coli cultures grown on a mixture of carbohydrates, a manifestation of diauxie. The simulated cellular states also show the reprogramming in the content of the proteome in response to fluctuations in the availability of carbon sources, and it captures the associated time lag during the diauxie phenotype. Our models suggest that the diauxic behavior of cells is the result of the evolutionary objective of maximization of the specific growth of the cell. We propose that genetic regulatory networks, such as the lac operon in E. coli, are the biological implementation of a robust control system to ensure optimal growth.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

Physiological limitations and opportunities in microbial metabolic engineering

Metabolic engineering can have a pivotal role in increasing the environmental sustainability of the transportation and chemical manufacturing sectors. The field has already developed engineered microorganisms that are currently being used in industrial-scale processes. However, it is often challenging to achieve the titres, yields and productivities required for commercial viability. The efficiency of microbial chemical production is usually dependent on the physiological traits of the host organism, which may either impose limitations on engineered biosynthetic pathways or, conversely, boost their performance. In this Review, we discuss different aspects of microbial physiology that often create obstacles for metabolic engineering, and present solutions to overcome them. We also describe various instances in which natural or engineered physiological traits in host organisms have been harnessed to benefit engineered metabolic pathways for chemical production.

Genetic strategies for improving hyaluronic acid production in recombinant bacterial culture

Hyaluronic acid (HA) is a biopolymer of repeating units of glucuronic acid and N-acetylglucosamine. Its market was valued at USD 8.9 billion in 2019. Traditionally, HA has been obtained from rooster comb-like animal tissues and fermentative cultures of attenuated pathogenic streptococci. Various attempts have been made to engineer a safe micro-organism for HA synthesis; however, the HA titres obtained from these attempts are in general still lower than those achieved by natural, pathogenic producers. In this scenario, ways to increase HA molecule length and titres in already constructed strains are gaining attention in the last years, but no recent publication has reviewed the main genetic strategies applied to improve HA production on heterologous hosts. In light of that, we hereby compile the advances made in the engineering of micro-organisms to improve HA synthesis.

Metabolic engineering of Escherichia coli for de novo production of 3-phenylpropanol via retrobiosynthesis approach

Background

3-Phenylpropanol with a pleasant odor is widely used in foods, beverages and cosmetics as a fragrance ingredient. It also acts as the precursor and reactant in pharmaceutical and chemical industries. Currently, petroleum-based manufacturing processes of 3-phenypropanol is environmentally unfriendly and unsustainable. In this study, we aim to engineer Escherichia coli as microbial cell factory for de novo production of 3-phenypropanol via retrobiosynthesis approach.

Results

Aided by in silico retrobiosynthesis analysis, we designed a novel 3-phenylpropanol biosynthetic pathway extending from l-phenylalanine and comprising the phenylalanine ammonia lyase (PAL), enoate reductase (ER), aryl carboxylic acid reductase (CAR) and phosphopantetheinyl transferase (PPTase). We screened the enzymes from plants and microorganisms and reconstructed the artificial pathway for conversion of 3-phenylpropanol from l-phenylalanine. Then we conducted chromosome engineering to increase the supply of precursor l-phenylalanine and combined the upstream l-phenylalanine pathway and downstream 3-phenylpropanol pathway. Finally, we regulated the metabolic pathway strength and optimized fermentation conditions. As a consequence, metabolically engineered E. coli strain produced 847.97 mg/L of 3-phenypropanol at 24 h using glucose-glycerol mixture as co-carbon source.

Conclusions

We successfully developed an artificial 3-phenylpropanol pathway based on retrobiosynthesis approach, and highest titer of 3-phenylpropanol was achieved in E. coli via systems metabolic engineering strategies including enzyme sources variety, chromosome engineering, metabolic strength balancing and fermentation optimization. This work provides an engineered strain with industrial potential for production of 3-phenylpropanol, and the strategies applied here could be practical for bioengineers to design and reconstruct the microbial cell factory for high valuable chemicals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01615-1.

Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

Motivation

Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge.

Results

Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant–product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks.

Availability and implementation

https://github.com/EPFL-LCSB/nicepath.

Supplementary information

Supplementary data are available at Bioinformatics online.

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives

Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.

Feasible-metabolic-pathway-exploration technique using chemical latent space

Motivation

Exploring metabolic pathways is one of the key techniques for developing highly productive microbes for the bioproduction of chemical compounds. To explore feasible pathways, not only examining a combination of well-known enzymatic reactions but also finding potential enzymatic reactions that can catalyze the desired structural changes are necessary. To achieve this, most conventional techniques use manually predefined-reaction rules, however, they cannot sufficiently find potential reactions because the conventional rules cannot comprehensively express structural changes before and after enzymatic reactions. Evaluating the feasibility of the explored pathways is another challenge because there is no way to validate the reaction possibility of unknown enzymatic reactions by these rules. Therefore, a technique for comprehensively capturing the structural changes in enzymatic reactions and a technique for evaluating the pathway feasibility are still necessary to explore feasible metabolic pathways.

Results

We developed a feasible-pathway-exploration technique using chemical latent space obtained from a deep generative model for compound structures. With this technique, an enzymatic reaction is regarded as a difference vector between the main substrate and the main product in chemical latent space acquired from the generative model. Features of the enzymatic reaction are embedded into the fixed-dimensional vector, and it is possible to express structural changes of enzymatic reactions comprehensively. The technique also involves differential-evolution-based reaction selection to design feasible candidate pathways and pathway scoring using neural-network-based reaction-possibility prediction. The proposed technique was applied to the non-registered pathways relevant to the production of 2-butanone, and successfully explored feasible pathways that include such reactions.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

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.

Biocatalysis - Key enabling tools from biocatalytic one-step and multi-step reactions to biocatalytic total synthesis

In the area of human-made innovations to improve the quality of life, biocatalysis has already had a great impact and contributed enormously to a growing number of catalytic transformations aimed at the detection and analysis of compounds, the bioconversion of starting materials and the preparation of target compounds at any scale, from laboratory small scale to industrial large scale. The key enabling tools which have been developed in biocatalysis over the last decades also provide great opportunities for further development and numerous applications in various sectors of the global bioeconomy. Systems biocatalysis is a modular, bottom-up approach to designing the architecture of enzyme-catalyzed reaction steps in a synthetic route from starting materials to target molecules. The integration of biocatalysis and sustainable chemistry in vitro aims at ideal conversions with high molecular economy and their intensification. Retrosynthetic analysis in the chemical and biological domain has been a valuable tool for target-oriented synthesis while, on the other hand, diversity-oriented synthesis builds on forward-looking analysis. Bioinformatic tools for rapid identification of the required enzyme functions, efficient enzyme production systems, as well as generalized bioprocess design tools, are important for rapid prototyping of the biocatalytic reactions. The tools for enzyme engineering and the reaction engineering of each enzyme-catalyzed one-step reaction are also valuable for coupling reactions. The tools to overcome interaction issues with other components or enzymes are of great interest in designing multi-step reactions as well as in biocatalytic total synthesis.

Towards the sustainable production of bulk-chemicals using biotechnology

The design and development of new routes for the production of sustainable bulk-chemicals requires focus on feedstock, conversion technology and downstream product recovery. This brief article discusses some of the constraints with using fermentation and suggests the removal of some constraints by using microbial biocatalysis or enzyme biocatalysis, which give a number of benefits in the context of the requirements for bulk-chemical production. Some potential process concepts are described, for products in the suitable low-price range. These examples (biodiesel, furfurals and amines) are used to illustrate the power of biocatalysis. Suggestions for future research efforts beyond molecular biology, involving process-based concepts, are also discussed.

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

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

Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power

The ATLAS of Biochemistry is a repository of both known and novel predicted biochemical reactions between biological compounds listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG). ATLAS was originally compiled based on KEGG 2015, though the number of KEGG reactions has increased by almost 20 percent since then. Here, we present an updated version of ATLAS created from KEGG 2018 using an increased set of generalized reaction rules. Furthermore, we improved the accuracy of the enzymes that are predicted for catalyzing novel reactions. ATLAS now contains ∼150 000 reactions, out of which 96% are novel. In this report, we present detailed statistics on the updated ATLAS and highlight the improvements with regard to the previous version. Most importantly, 107 reactions predicted in the original ATLAS are now known to KEGG, which validates the predictive power of our approach. The updated ATLAS is available at https://lcsb-databases.epfl.ch/atlas.

Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways

Natural products (NPs), also known as secondary metabolites, are produced in bacteria, fungi, and plants. NPs represent a rich source of antibacterial, antifungal, and anticancer agents. Recent advances in DNA sequencing technologies and bioinformatics unveiled nature's great potential for synthesizing numerous NPs that may confer unprecedented structural and biological features. However, discovering novel bioactive NPs by genome mining remains a challenge. Moreover, even with interesting bioactivity, the low productivity of many NPs significantly limits their practical applications. Here we discuss the progress in developing bioinformatics tools for efficient discovery of bioactive NPs. In addition, we highlight computational methods for optimizing the productivity of NPs of pharmaceutical importance.

Advances in biological conversion technologies: new opportunities for reaction engineering

Reaction engineering needs to embrace biological conversion technologies, on the road to identify more sustainable routes for chemical manufacture.

RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms

Background

The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions.

Results

Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules.

Conclusions

The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3025-9) contains supplementary material, which is available to authorized users.