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.

MycotoxinDB: A Data-Driven Platform for Investigating Masked Forms of Mycotoxins

Mycotoxins are likely to be converted into masked forms when subjected to plant metabolism or food processing. These masked forms of mycotoxins together with their prototypes may cause mixture toxicity effects, causing adverse effects on animal welfare and productivity. The structure elucidation of masked mycotoxins is the most challenging task in mycotoxin research due to the limitations of traditional analysis methods. To assist in the rapid identification of masked mycotoxins, we developed a data-driven online prediction tool, MycotoxinDB, based on reaction rules. Using MycotoxinDB, we identified seven masked DONs from wheat samples. Given its widespread applications, we expect that MycotoxinDB will become an indispensable tool in future mycotoxin research. MycotoxinDB is freely available at: http://www.mycotoxin-db.com/.

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.”

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

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

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

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

Motivation

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

Results

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

Availability and implementation

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

Prospecting Biochemical Pathways to Implement Microbe-Based Production of the New-to-Nature Platform Chemical Levulinic Acid

Levulinic acid is a versatile platform molecule with potential to be used as an intermediate in the synthesis of many value-added products used across different industries, from cosmetics to fuels. Thus far, microbial biosynthetic pathways having levulinic acid as a product or an intermediate are not known, which restrains the development and optimization of a microbe-based process envisaging the sustainable bioproduction of this chemical. One of the doors opened by synthetic biology in the design of microbial systems is the implementation of new-to-nature pathways, that is, the assembly of combinations of enzymes not observed in vivo, where the enzymes can use not only their native substrates but also non-native ones, creating synthetic steps that enable the production of novel compounds. Resorting to a combined approach involving complementary computational tools and extensive manual curation, in this work, we provide a thorough prospect of candidate biosynthetic pathways that can be assembled for the production of levulinic acid in Escherichia coli or Saccharomyces cerevisiae. Out of the hundreds of combinations screened, five pathways were selected as best candidates on the basis of the availability of substrates and of candidate enzymes to catalyze the synthetic steps (that is, those steps that involve conversions not previously described). Genome-scale metabolic modeling was used to assess the performance of these pathways in the two selected hosts and to anticipate possible bottlenecks. Not only does the herein described approach offer a platform for the future implementation of the microbial production of levulinic acid but also it provides an organized research strategy that can be used as a framework for the implementation of other new-to-nature biosynthetic pathways for the production of value-added chemicals, thus fostering the emerging field of synthetic industrial microbiotechnology.

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.

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/.

Metabolic disassembler for understanding and predicting the biosynthetic units of natural products

BACKGROUND

Natural products are the source of various functional materials such as medicines, and understanding their biosynthetic pathways can provide information that is helpful for their effective production through the synthetic biology approach. A number of studies have aimed to predict biosynthetic pathways from their chemical structures in a retrosynthesis manner; however, sometimes the calculation finishes without reaching the starting material from the target molecule. In order to address this problem, the method to find suitable starting materials is required.

RESULTS

In this study, we developed a predictive workflow named the Metabolic Disassembler that automatically disassembles the target molecule structure into relevant biosynthetic units (BUs), which are the substructures that correspond to the starting materials in the biosynthesis pathway. This workflow uses a biosynthetic unit library (BUL), which contains starting materials, key intermediates, and their derivatives. We obtained the starting materials from the KEGG PATHWAY database, and 765 BUs were registered in the BUL. We then examined the proposed workflow to optimize the combination of the BUs. To evaluate the performance of the proposed Metabolic Disassembler workflow, we used 943 molecules that are included in the secondary metabolism maps of KEGG PATHWAY. About 95.8% of them (903 molecules) were correctly disassembled by our proposed workflow. For comparison, we also implemented a genetic algorithm-based workflow, and found that the accuracy was only about 52.0%. In addition, for 90.7% of molecules, our workflow finished the calculation within one minute.

CONCLUSIONS

The Metabolic Disassembler enabled the effective disassembly of natural products in terms of both correctness and computational time. It also outputs automatically highlighted color-coded substructures corresponding to the BUs to help users understand the calculation results. The users do not have to specify starting molecules in advance, and can input any target molecule, even if it is not in databases. Our workflow will be very useful for understanding and predicting the biosynthesis of natural products.

Exploring Chemical Biosynthetic Design Space with Transform-MinER

Transform-MinER (Transforming Molecules in Enzyme Reactions) is a web application facilitating the exploration of chemical biosynthetic space, guiding the user toward promising start points for enzyme design projects or directed evolution experiments. Two types of search are possible: Molecule Search allows a user to submit a source substrate enabling Transform-MinER to search for enzyme reactions acting on similar substrates, whereas Path Search additionally allows a user to submit a target molecule enabling Transform-MinER to search for a path of enzyme reactions acting on similar substrates to link source and target. Transform-MinER searches for potential reaction centers in the source substrate and uses chemoinformatic fingerprints to identify those that are situated in molecular environments similar to native counterparts, prioritizing steps that move closer to the target using reactions most similar to native in its exploration of search space. The ligand-based methodology behind Transform-MinER is presented, and its performance is validated yielding 90% success rates: first, on a data set of native pathways from the KEGG database, and second, on a data set of de novo enzyme reactions.

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.

SimCAL: a flexible tool to compute biochemical reaction similarity

Background

Computation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation.

Results

To address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods.

Conclusions

SimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2248-5) contains supplementary material, which is available to authorized users.

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

Background

The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms.

Results

We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism.

Conclusions

This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.Graphical abstract