Cell2Chem: mining explored and unexplored biosynthetic chemical spaces

SUMMARY

Living cell strains have important applications in synthesizing their native compounds and potential for use in studies exploring the universal chemical space. Here, we present a web server named as Cell2Chem which accelerates the search for explored compounds in organisms, facilitating investigations of biosynthesis in unexplored chemical spaces. Cell2Chem uses co-occurrence networks and natural language processing to provide a systematic method for linking living organisms to biosynthesized compounds and the processes that produce these compounds. The Cell2Chem platform comprises 40 370 species and 125 212 compounds. Using reaction pathway and enzyme function in silico prediction methods, Cell2Chem reveals possible biosynthetic pathways of compounds and catalytic functions of proteins to expand unexplored biosynthetic chemical spaces. Cell2Chem can help improve biosynthesis research and enhance the efficiency of synthetic biology.

AVAILABILITY AND IMPLEMENTATION

Cell2Chem is available at: http://www.rxnfinder.org/cell2chem/.

Transcriptor: a comprehensive platform for annotation of the enzymatic functions of transcripts

MOTIVATION

Rapid advances in sequencing technology have resulted huge increases in the accessibility of sequencing data. Moreover, researchers are focusing more on organisms that lack a reference genome. However, few easy-to-use web servers focusing on annotations of enzymatic functions are available. Accordingly, in this study, we describe Transcriptor, a novel platform for annotating transcripts encoding enzymes.

RESULTS

The transcripts were evaluated using more than 300 000 in-house enzymatic reactions through bridges of Enzyme Commission numbers. Transcriptor also enabled ontology term identification and along with associated enzymes, visualization and prediction of domains and annotation of regulatory structure, such as long noncoding RNAs, which could facilitate the discovery of new functions in model or nonmodel species. Transcriptor may have applications in elucidation of the roles of organs transcriptomes and secondary metabolite biosynthesis in organisms lacking a reference genome.

AVAILABILITY AND IMPLEMENTATION

Transcriptor is available at http://design.rxnfinder.org/transcriptor/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PhDSeeker: Pheromone-Directed Seeker for metabolic pathways

Manually finding relationship networks among compounds can be a hard and time-consuming task. However, this process is fundamental when looking for a metabolic pathway that explains how multiple compounds are related, to identify relevant pathways in organisms, filling gaps on metabolic networks, or when new mechanisms for the synthesis of important compounds are sought. Here, we present PhDSeeker, a new tool for the automatic search of metabolic pathways. This tool is able to relate simultaneously several compounds. Furthermore, its flexibility allows it to be easily configured for addressing a wide range of situations. Solutions found are provided not only in plain text but also as interactive representations that can be analyzed in a web browser. Source code is available at https://github.com/sinc-lab/phdseeker. A web service is also available at https://sinc.unl.edu.ar/web-demo/phds/. Several fully documented study cases, including their settings and solutions files, are also provided as Supplementary Material.

Data-driven rational biosynthesis design: from molecules to cell factories

A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.

BCSExplorer: a customized biosynthetic chemical space explorer with multifunctional objective function analysis

SUMMARY

The biosynthetic ability of living organisms has important applications in producing bulk chemicals, biofuels and natural products. Based on the most comprehensive biosynthesis knowledgebase, a computational system, BCSExplorer, is proposed to discover the unexplored chemical space using nature's biosynthetic potential. BCSExplorer first integrates the most comprehensive biosynthetic reaction database with 280 000 biochemical reactions and 60 000 chemicals biosynthesized globally over the past 130 years. Second, in this study, a biosynthesis tree is computed for a starting chemical molecule based on a comprehensive biotransformation rule library covering almost all biosynthetic possibilities, in which redundant rules are removed using a new algorithm. Moreover, biosynthesis feasibility, drug-likeness and toxicity analysis of a new generation of compounds will be pursued in further studies to meet various needs. BCSExplorer represents a novel method to explore biosynthetically available chemical space.

AVAILABILITY AND IMPLEMENTATION

BCSExplorer is available at: http://www.rxnfinder.org/bcsexplorer/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Validamycin Shunt Pathway for Valienamine Synthesis in Engineered Streptomyces hygroscopicus 5008

Valienamine is the key functional component of many natural glycosidase inhibitors, including the crop protectant validamycin A and the clinical antidiabetic agent acarbose. Due to its important biomedical activity, it is also the prominent lead compound for the exploration of therapeutic agents, such as the stronger α-glucosidase inhibitor voglibose. Currently, the main route for obtaining valienamine is a multistep biosynthetic process involving the synthesis and degradation of validamycin A. Here, we established an alternative, vastly simplified shunt pathway for the direct synthesis of valienamine based on an envisioned non-natural transamination in the validamycin A producer Streptomyces hygroscopicus 5008. We first identified candidate aminotransferases for the non-natural ketone substrate valienone and conducted molecular evolution in vitro. The WecE enzyme from Escherichia coli was verified to complete the envisioned step with >99.9% enantiomeric excess and was further engineered to produce a 32.6-fold more active mutant, VarB, through protein evolution. Subsequently, two copies of VarB were introduced into the host, and the new shunt pathway produced 0.52 mg/L valienamine after a 96-h fermentation. Our study thus illustrates a dramatically simplified alternative shunt pathway for valienamine production and introduces a promising foundational platform for increasing the production of valienamine and its valuable N-modified derivatives for use in pharmaceutical applications.

Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

Background

The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways.

Results

A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest.

Conclusions

This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.

CF-Targeter: A Rational Biological Cell Factory Targeting Platform for Biosynthetic Target Chemicals

Biosynthesis is a promising method for chemical synthesis. However, due to varieties between different microorganism hosts, yield and heterologous pathways needed for production of target chemical may also vary from different strains. One of the main challenges in metabolic engineering is to select an appropriate chassis host for specified target chemical production. However, with thousands of microorganisms existing in nature and extremely complicated metabolism within them, it is still time-consuming and error-prone work to achieve such a goal only through experimental methods, even with some existing computational methods. Hence, more efficient methods should be proposed to assist in selecting appropriate chassis hosts. In this article, based on symbolic reaction repositories and a pathway search algorithm which performed 1 400 000 searches for per target compound, we established a biological reasoning system for appropriate chassis host selection by coupling with various GEM-models. By using a supercomputer to calculate the biosynthetic pathways for more than 1 month, nearly 50 000 000 biosynthetic pathways are computed for production of 6026 compounds within 70 microorganisms. With retrieved organisms for specified target production, several heterologous biosynthetic pathways can be shown in length order, and then the maximum theoretical yields and thermodynamic feasibility can be calculated in real time under customized growth conditions and physiological states. From the computation results, the system not only identifies experimentally validated pathways but also outputs more efficient solutions with less heterologous steps or higher maximum possible theoretical yield by engineering other organism hosts. CF-targeter is available at http://www.rxnfinder.org/cf_targeter/.

Biochemical engineering in China

Chinese biochemical engineering is committed to supporting the chemical and food industries, to advance science and technology frontiers, and to meet major demands of Chinese society and national economic development. This paper reviews the development of biochemical engineering, strategic deployment of these technologies by the government, industrial demand, research progress, and breakthroughs in key technologies in China. Furthermore, the outlook for future developments in biochemical engineering in China is also discussed.

PrecursorFinder: a customized biosynthetic precursor explorer

Summary

Synthetic biology has a great potential to produce high value pharmaceuticals, commodities or bulk chemicals. However, many biosynthetic target molecules have no defined or predicted biosynthetic pathways. Biosynthetic precursors are crucial to create biosynthetic pathways. Thus computer-assisted tools for precursor identification are urgently needed to develop novel metabolic pathways. To this end, we present PrecursorFinder, a computational tool that explores biosynthetic precursors for the query target molecules using chemical structure, similarity as well as MCS (maximum common substructure). This platform comprises more than 60 000 compounds biosynthesized for being promising precursors, which are extracted from >500 000 scientific literatures and manually curated by more than 100 people over the past 8 years. The PrecursorFinder could speed up the process of biosynthesis research and make synthetic biology or metabolic engineering more efficient.

Availability and implementation

PrecursorFinder is available at: http://www.rxnfinder.org/precursorfinder/.

Supplementary information

Supplementary data are available at Bioinformatics online.

EcoSynther: A Customized Platform To Explore the Biosynthetic Potential in E. coli

Developing computational tools for a chassis-centered biosynthetic pathway design is very important for a productive heterologous biosynthesis system by considering enormous foreign biosynthetic reactions. For many cases, a pathway to produce a target molecule consists of both native and heterologous reactions when utilizing a microbial organism as the host organism. Due to tens of thousands of biosynthetic reactions existing in nature, it is not trivial to identify which could be served as heterologous ones to produce the target molecule in a specific organism. In the present work, we integrate more than 10,000 E. coli non-native reactions and utilize a probability-based algorithm to search pathways. Moreover, we built a user-friendly Web server named EcoSynther. It is able to explore the precursors and heterologous reactions needed to produce a target molecule in Escherichia coli K12 MG1655 and then applies flux balance analysis to calculate theoretical yields of each candidate pathway. Compared with other chassis-centered biosynthetic pathway design tools, EcoSynther has two unique features: (1) allow for automatic search without knowing a precursor in E. coli and (2) evaluate the candidate pathways under constraints from E. coli physiological states and growth conditions. EcoSynther is available at http://www.rxnfinder.org/ecosynther/ .

A review of parameters and heuristics for guiding metabolic pathfinding

Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.

SensiPath: computer-aided design of sensing-enabling metabolic pathways

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.