Exploring Chemical Biosynthetic Design Space with Transform-MinER

A modular and synthetic biosynthesis platform for de novo production of diverse halogenated tryptophan-derived molecules

Halogen-containing molecules are ubiquitous in modern society and present unique chemical possibilities. As a whole, de novo fermentation and synthetic pathway construction for these molecules remain relatively underexplored and could unlock molecules with exciting new applications in industries ranging from textiles to agrochemicals to pharmaceuticals. Here, we report a mix-and-match co-culture platform to de novo generate a large array of halogenated tryptophan derivatives in Escherichia coli from glucose. First, we engineer E. coli to produce between 300 and 700 mg/L of six different halogenated tryptophan precursors. Second, we harness the native promiscuity of multiple downstream enzymes to access unexplored regions of metabolism. Finally, through modular co-culture fermentations, we demonstrate a plug-and-play bioproduction platform, culminating in the generation of 26 distinct halogenated molecules produced de novo including precursors to prodrugs 4-chloro- and 4-bromo-kynurenine and new-to-nature halogenated beta carbolines.

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online.

Pickaxe: a Python library for the prediction of novel metabolic reactions

BACKGROUND

Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.

RESULTS

Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.

CONCLUSION

Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering

Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.

Structure-Guided Engineering of a Family IV Cold-Adapted Esterase Expands Its Substrate Range

Cold active esterases have gained great interest in several industries. The recently determined structure of a family IV cold active esterase (EstN7) from Bacillus cohnii strain N1 was used to expand its substrate range and to probe its commercially valuable substrates. Database mining suggested that triacetin was a potential commercially valuable substrate for EstN7, which was subsequently proved experimentally with the final product being a single isomeric product, 1,2-glyceryl diacetate. Enzyme kinetics revealed that EstN7’s activity is restricted to C2 and C4 substrates due to a plug at the end of the acyl binding pocket that blocks access to a buried water-filled cavity. Residues M187, N211 and W206 were identified as key plug forming residues. N211A stabilised EstN7 allowing incorporation of the destabilising M187A mutation. The M187A-N211A double mutant had the broadest substrate range, capable of hydrolysing a C8 substrate. W206A did not appear to have any significant effect on substrate range either alone or when combined with the double mutant. Thus, the enzyme kinetics and engineering together with a recently determined structure of EstN7 provide new insights into substrate specificity and the role of acyl binding pocket plug residues in determining family IV esterase stability and substrate range.

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.”

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.

The Enzyme Portal: an integrative tool for enzyme information and analysis

Enzymes play essential roles in all life processes and are used extensively in the biomedical and biotechnological fields. However, enzyme-related information is spread across multiple resources making its retrieval time-consuming. In response to this challenge, the Enzyme Portal has been established to facilitate enzyme research, by providing a freely available hub where researchers can easily find and explore enzyme-related information. It integrates relevant enzyme data for a wide range of species from various resources such as UniProtKB, PDBe and ChEMBL. Here, we describe what type of enzyme-related data the Enzyme Portal provides, how the information is organized and, by show-casing two potential use cases, how to access and retrieve it.

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.

Unlocking the Full Evolutionary Potential of Artificial Metalloenzymes Through Direct Metal-Protein Coordination : A review of recent advances for catalyst development

Generation of artificial metalloenzymes (ArMs) has gained much inspiration from the general understanding of natural metalloenzymes. Over the last decade, a multitude of methods generating transition metal-protein hybrids have been developed and many of these new-to-nature constructs catalyse reactions previously reserved for the realm of synthetic chemistry. This perspective will focus on ArMs incorporating 4d and 5d transition metals. It aims to summarise the significant advances made to date and asks whether there are chemical strategies, used in nature to optimise metal catalysts, that have yet to be fully recognised in the synthetic enzyme world, particularly whether artificial enzymes produced to date fully take advantage of the structural and energetic context provided by the protein. Further, the argument is put forward that, based on precedence, in the majority of naturally evolved metalloenzymes the direct coordination bonding between the metal and the protein scaffold is integral to catalysis. Therefore, the protein can attenuate metal activity by positioning ligand atoms in the form of amino acids, as well as making non-covalent contributions to catalysis, through intermolecular interactions that pre-organise substrates and stabilise transition states. This highlights the often neglected but crucial element of natural systems that is the energetic contribution towards activating metal centres through protein fold energy. Finally, general principles needed for a different approach to the formation of ArMs are set out, utilising direct coordination inspired by the activation of an organometallic cofactor upon protein binding. This methodology, observed in nature, delivers true interdependence between metal and protein. When combined with the ability to efficiently evolve enzymes, new problems in catalysis could be addressed in a faster and more specific manner than with simpler small molecule catalysts.