Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli.

Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum. Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida, resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis, which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens. This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest.

Balancing the AspC and AspA Pathways of Escherichia coli by Systematic Metabolic Engineering Strategy for High-Efficient l-Homoserine Production.

l-Homoserine is a promising C4 platform compound used in the agricultural, cosmetic, and pharmaceutical industries. Numerous works have been conducted to engineer Escherichia coli to be an excellent l-homoserine producer, but it is still unable to meet the industrial-scale demand. Herein, we successfully engineered a plasmid-free and noninducible E. coli strain with highly efficient l-homoserine production through balancing AspC and AspA synthesis pathways. First, an initial strain was constructed by increasing the accumulation of the precursor oxaloacetate and attenuating the organic acid synthesis pathway. To remodel the carbon flux toward l-aspartate, a balanced route prone to high yield based on TCA intensity regulation was designed. Subsequently, the main synthetic pathway and the cofactor system were strengthened to reinforce the l-homoserine synthesis. Ultimately, under two-stage DO control, strain HSY43 showed 125.07 g/L l-homoserine production in a 5 L fermenter in 60 h, with a yield of 0.62 g/g glucose and a productivity of 2.08 g/L/h. The titer, yield, and productivity surpassed the highest reported levels for plasmid-free strains in the literature. The strategies adopted in this study can be applied to the production of other l-aspartate family amino acids.

Enabling pathway design by multiplex experimentation and machine learning.

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems.

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

Modular Cloning by Golden Gate Assembly and Possible Application in Pathway Design.

Preparation of expression vectors using conventional cloning strategies is laborious and not suitable for the design of metabolic pathways or enzyme cascades, which usually requires the preparation of a vector library to identify productive clones. Recently, Modular Cloning as a novel cloning technique in synthetic biology has been developed. Modular Cloning relies on Golden Gate assembly and supports preparation of individual expression vectors in one-step and one-pot reactions, thus allowing rapid generation of vector libraries. A number of Modular Cloning toolkits for specific applications has been established, providing a collection of distinct genetic elements such as promoters, ribosome binding sites and tags, that can be combined individually in one-step using defined fusion sites. Modular Cloning has been successfully applied to generate various strains for producing value-added compounds. This was achieved by orchestrating complex pathways involving up to 20 enzymes. Due to the novelty of the genetic approach, industrial applications are still rare. In addition, some applications are limited due to the lack of high-throughput screening methods. This shifts the bottleneck from library preparation to screening capacity and needs to be addressed by future developments to pave the path for the establishment of Modular Cloning in industrial applications.

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.

Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes.

In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.

Recent Progress in Metabolic Engineering of Escherichia coli for the Production of Various C4 and C5-Dicarboxylic Acids.

As an alternative to petrochemical synthesis, well-established industrial microbes, such as Escherichia coli, are employed to produce a wide range of chemicals, including dicarboxylic acids (DCAs), which have significant potential in diverse areas including biodegradable polymers. The demand for biodegradable polymers has been steadily rising, prompting the development of efficient production pathways on four- (C4) and five-carbon (C5) DCAs derived from central carbon metabolism to meet the increased demand via the biosynthesis. In this context, E. coli is utilized to produce these DCAs through various metabolic engineering strategies, including the design or selection of metabolic pathways, pathway optimization, and enhancement of catalytic activity. This review aims to highlight the recent advancements in metabolic engineering techniques for the production of C4 and C5 DCAs in E. coli.

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.

Biosynthetic approaches to efficient assimilation of CO2 via photorespiration modification in plant chassis.

Plant chassis has emerged as the platform with great potential for bioproduction of high value-added products such as recombinant protein, vaccine and natural product. However, as the primary metabolic pathway, photorespiration results in the loss of photosynthetically fixed carbon compounds and limits the exploration of plant chassis. People are endeavored to reduce the photorespiration energy or carbon loss based on variation screening or genetic engineering. Insomuch as protein engineering of Rubisco has not resulted in the significant improvement of Rubisco specificity which is linked to the direct CO2 fixation, the biosynthetic approaches of photorespiration bypass are gaining much more attention and manifested great potentiality in conferring efficient assimilation of CO2 in plant chassis. In this review, we summarize the recent studies on the metabolic pathway design and implementation of photorespiration alternative pathway aiming to provide clues to efficiently enhance carbon fixation via the modification of photorespiration in plant chassis for bioproduction. These will benefit the development of plant synthetic metabolism for biorefineries via improvement of artificial carbon sequestration cycle, particularly for the mitigation of serious challenges such as extreme climate change, food and energy shortages in the future.

Metabolic pathway engineering for the non-growth-associated succinate production in Escherichia coli based on flux solution space.

Non-growth-associated bio-production using microorganisms has the potential to achieve a higher target yield than growth-associated production since the latter approach does not waste the substrate for cell growth. We previously proposed a metabolic pathway engineering method (SSDesign) for non-growth-associated target production based on metabolic flux solution space using elementary mode analysis. SSDesign predicts gene knockout combinations for enforcing cells to produce a target compound under non-growing conditions. For succinate production from glucose in Escherichia coli, gene knockouts of pykA-pykF-sfcA-maeB-zwf and pykA-pykF-sfcA-pntAB-sthA were predicted as candidates. In the present study, to verify the predictions of SSDesign, succinate productivities of these multiple knockout strains were evaluated in the stationary phase under microaerobic conditions. Succinate yields of the BW25113ΔpykAΔpykFΔsfcAΔmaeBΔzwf and BW25113ΔpykAΔpykFΔsfcAΔpntABΔsthA strains were 0.48 and 0.52 mol/mol, respectively, and were higher than that of wild type strain (0.20 mol/mol). The succinate yield of BW25113ΔpykAΔpykFΔsfcAΔpntABΔsthA strain was further improved to 0.66 mol/mol by overexpression of phosphoenolpyruvate carboxylase as a potential bottleneck step in the metabolic pathway.

[Graph-based and constraint-based heterologous metabolic pathway design methods and application].

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.

[Digital cell models and their applications: a review].

Various omics technologies are changing Biology into a data-driven science subject. Development of data-driven digital cell models is key for understanding system level organization and evolution principles of life, as well as for predicting cellular function under various environmental/genetic perturbations and subsequently for the design of artificial life. Consequently, the construction, analysis and design of digital cell models have become one of the core supporting technologies in synthetic biology. This paper summarized the research progress on digital cell models in the last ten years after the foundation of Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, with a focus on the development and quality control of genome-scale metabolic network for reliable metabolic pathway design and their application in guiding strain metabolic engineering. We also introduced the latest progress on developing cellular models with multiple constraints to improve prediction accuracy. At last, we briefly discussed the current challenges and future directions in digital cell model development. We believe that digital cell technology, along with genome sequencing, genome synthesis and genome editing, will greatly improve our ability in reading, writing, modifying and creating life.

Patient involvement in clinical pathway development, implementation and evaluation - A scoping review of international literature.

OBJECTIVE: Although various pathway design methods recognize patients as stakeholders, an overview of current practice is lacking. This article describes the results of a literature review assessing patient involvement in clinical cancer pathway development, implementation and evaluation. METHODS: A scoping review was conducted following PRISMA-ScR. Two databases were searched to identify studies published in English between 2014 and 2021. RESULTS: Of 12841articles identified 22 articles met the inclusion criteria and reported on one or more of the three phases: development phase (N = 2), implementation (N = 4), evaluation (N = 11), development/evaluation (N = 3), and implementation/evaluation (N = 2) of clinical pathways. The numbers of involved patients ranged from 10 to 793, and the reported methods varied considerably. CONCLUSION: This review presents a synthesis of methods for involving patients in the clinical pathway lifecycle. No relationship was found between methods and the number of involved patients or between pathway complexity and methods. Although patients are seen as valuable stakeholders in the pathway design, to involve them in practice using the best practice can be improved. PRACTICE IMPLICATIONS: The lack of a clear justification for the choice of methods and number of involved patients calls for further research and framework development to inform pathway developers.

Controlling selectivity of modular microbial biosynthesis of butyryl-CoA-derived designer esters.

Short-chain esters have broad utility as flavors, fragrances, solvents, and biofuels. Controlling selectivity of ester microbial biosynthesis has been an outstanding metabolic engineering problem. In this study, we enabled the de novo fermentative microbial biosynthesis of butyryl-CoA-derived designer esters (e.g., butyl acetate, ethyl butyrate, butyl butyrate) in Escherichia coli with controllable selectivity. Using the modular design principles, we generated the butyryl-CoA-derived ester pathways as exchangeable production modules compatible with an engineered chassis cell for anaerobic production of designer esters. We designed these modules derived from an acyl-CoA submodule (e.g., acetyl-CoA, butyryl-CoA), an alcohol submodule (e.g., ethanol, butanol), a cofactor regeneration submodule (e.g., NADH), and an alcohol acetyltransferase (AAT) submodule (e.g., ATF1, SAAT) for rapid module construction and optimization by manipulating replication (e.g., plasmid copy number), transcription (e.g., promoters), translation (e.g., codon optimization), pathway enzymes, and pathway induction conditions. To further enhance production of designer esters with high selectivity, we systematically screened various strategies of protein solubilization using protein fusion tags and chaperones to improve the soluble expression of multiple pathway enzymes. Finally, our engineered ester-producing strains could achieve 19-fold increase in butyl acetate production (0.64 g/L, 96% selectivity), 6-fold increase in ethyl butyrate production (0.41 g/L, 86% selectivity), and 13-fold increase in butyl butyrate production (0.45 g/L, 54% selectivity) as compared to the initial strains. Overall, this study presented a generalizable framework to engineer modular microbial platforms for anaerobic production of butyryl-CoA-derived designer esters from renewable feedstocks.

Graph-based and constraint-based heterologous metabolic pathway design methods and application / 生物工程学报

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.

Rational design of a highly efficient catalytic system for the production of PAPS from ATP and its application in the synthesis of chondroitin sulfate.

The compound 3'-phosphoadenosine-5'-phosphosulfate (PAPS) serves as a sulfate group donor in the production of valuable sulfated compounds. However, elevated costs and low conversion efficiency limit the industrial applicability of PAPS. Here, we designed and constructed an efficient and controllable catalytic system for the conversion of adenosine triphosphate (ATP) (disodium salt) into PAPS without inhibition from by-products. In vitro and in vivo testing in Escherichia coli identified adenosine-5'-phosphosulfate kinase from Penicillium chrysogenum (PcAPSK) as the rate-limiting enzyme. Based on analysis of the catalytic steps and molecular dynamics simulations, a mechanism-guided "ADP expulsion" strategy was developed to generate an improved PcAPSK variant (L7), with a specific activity of 48.94 U·mg-1 and 73.27-fold higher catalytic efficiency (kcat/Km) that of the wild-type enzyme. The improvement was attained chiefly by reducing the ADP-binding affinity of PcAPSK, as well as by changing the enzyme's flexibility and lid structure to a more open conformation. By introducing PcAPSK L7 in an in vivo catalytic system, 73.59 mM (37.32 g·L-1 ) PAPS was produced from 150 mM ATP in 18.5 h using a 3-L bioreactor, and achieved titer is the highest reported to date and corresponds to a 98.13% conversion rate. Then, the PAPS catalytic system was combined with the chondroitin 4-sulfotransferase using a one-pot method. Finally, chondroitin sulfate was transformed from chondroitin at a conversion rate of 98.75%. This strategy has great potential for scale biosynthesis of PAPS and chondroitin sulfate.

Non-natural Aldol Reactions Enable the Design and Construction of Novel One-Carbon Assimilation Pathways in vitro.

Methylotrophs utilizes cheap, abundant one-carbon compounds, offering a promising green, sustainable and economical alternative to current sugar-based biomanufacturing. However, natural one-carbon assimilation pathways come with many disadvantages, such as complicated reaction steps, the need for additional energy and/or reducing power, or loss of CO2, resulting in unsatisfactory biomanufacturing performance. Here, we predicted eight simple, novel and carbon-conserving formaldehyde (FALD) assimilation pathways based on the extended metabolic network with non-natural aldol reactions using the comb-flux balance analysis (FBA) algorithm. Three of these pathways were found to be independent of energy/reducing equivalents, and thus chosen for further experimental verification. Then, two novel aldol reactions, condensing D-erythrose 4-phosphate and glycolaldehyde (GALD) into 2R,3R-stereo allose 6-phosphate by DeoC or 2S,3R-stereo altrose 6-phosphate by TalBF178Y/Fsa, were identified for the first time. Finally, a novel FALD assimilation pathway proceeding via allose 6-phosphate, named as the glycolaldehyde-allose 6-phosphate assimilation (GAPA) pathway, was constructed in vitro with a high carbon yield of 94%. This work provides an elegant paradigm for systematic design of one-carbon assimilation pathways based on artificial aldolase (ALS) reactions, which could also be feasibly adapted for the mining of other metabolic pathways.

Computationally Prospecting Potential Pathways from Lignin Monomers and Dimers toward Aromatic Compounds.

The heterogeneity of the aromatic products originating from lignin catalytic depolymerization remains one of the major challenges associated with lignin valorization. Microbes have evolved catabolic pathways that can funnel heterogeneous intermediates to a few central aromatic products. These aromatic compounds can subsequently undergo intra- or extradiol ring opening to produce value-added chemicals. However, such funneling pathways are only partially characterized for a few organisms such as Sphingobium sp. SYK-6 and Pseudomonas putida KT2440. Herein, we apply the de novo pathway design tool (novoStoic) to computationally prospect possible ways of funneling lignin-derived mono- and biaryls. novoStoic employs reaction rules between molecular moieties to hypothesize de novo conversions by flagging known enzymes that carry out the same biotransformation on the most similar substrate. Both reaction rules and known reactions are then deployed by novoStoic to identify a mass-balanced biochemical network that converts a source to a target metabolite while minimizing the number of de novo steps. We demonstrate the application of novoStoic for (i) designing alternative pathways of funneling S, G, and H lignin monomers, and (ii) exploring cleavage pathways of ß-1 and ß-ß dimers. By exploring the uncharted chemical space afforded by enzyme promiscuity, novoStoic can help predict previously unknown native pathways leveraging enzyme promiscuity and propose new carbon/energy efficient lignin funneling pathways with few heterologous enzymes.

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

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