BioTRY: A Comprehensive Knowledge Base for Titer, Rate, and Yield of Biosynthesis

Synthetic biology is rapidly evolving into a data-intensive science that increasingly relies on massive data sets; one of its applications is the evaluation of the economic viability of fermentation processes. However, the key economic indicators, namely titer, rate, and yield (TRY), which respectively reflect the downstream processing, reactor size, and raw material costs, are not well captured in bioinformatics databases. In this paper, we present BioTRY, an intuitive and user-friendly tool that contains >5,000 biochemicals and >3,800 strains, along with over 52,000 corresponding TRY entries with original references. It is freely available at http://www.synbiohealth.cn/biotry. To our knowledge, BioTRY is the first available database on biosynthesis TRY data from original research. We anticipate that BioTRY will become a useful tool that aids researchers and decision-makers in understanding the current development state of biosynthesis and allows them to foresee potential prospects and applications for biosynthesis.

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.

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/.

A data-driven integrative platform for computational prediction of toxin biotransformation with a case study

Recently, biogenic toxins have received increasing attention owing to their high contamination levels in feed and food as well as in the environment. However, there is a lack of an integrative platform for seamless linking of data-driven computational methods with 'wet' experimental validations. To this end, we constructed a novel platform that integrates the technical aspects of toxin biotransformation methods. First, a biogenic toxin database termed ToxinDB (http://www.rxnfinder.org/toxindb/), containing multifaceted data on more than 4836 toxins, was built. Next, more than 8000 biotransformation reaction rules were extracted from over 300,000 biochemical reactions extracted from ~580,000 literature reports curated by more than 100 people over the past decade. Based on these reaction rules, a toxin biotransformation prediction model was constructed. Finally, the global chemical space of biogenic toxins was constructed, comprising ~550,000 toxins and putative toxin metabolites, of which 94.7% of the metabolites have not been previously reported. Additionally, we performed a case study to investigate citrinin metabolism in Trichoderma, and a novel metabolite was identified with the assistance of the biotransformation prediction tool of ToxinDB. This unique integrative platform will assist exploration of the 'dark matter' of a toxin's metabolome and promote the discovery of detoxification enzymes.

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.

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/.

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.

Bio2Rxn: sequence-based enzymatic reaction predictions by a consensus strategy

Summary

The development of sequencing technologies has generated large amounts of protein sequence data. The automated prediction of the enzymatic reactions of uncharacterized proteins is a major challenge in the field of bioinformatics. Here, we present Bio2Rxn as a web-based tool to provide putative enzymatic reaction predictions for uncharacterized protein sequences. Bio2Rxn adopts a consensus strategy by incorporating six types of enzyme prediction tools. It allows for the efficient integration of these computational resources to maximize the accuracy and comprehensiveness of enzymatic reaction predictions, which facilitates the characterization of the functional roles of target proteins in metabolism. Bio2Rxn further links the enzyme function prediction with more than 300 000 enzymatic reactions, which were manually curated by more than 100 people over the past 9 years from more than 580 000 publications.

Availability and implementation

Bio2Rxn is available at: http://design.rxnfinder.org/bio2rxn/.

Contact

qnhu@sibs.ac.cn

Supplementary information

Supplementary data are available at Bioinformatics online.

FRCD: A comprehensive food risk component database with molecular scaffold, chemical diversity, toxicity, and biodegradability analysis

The presence of natural toxins, pesticide residues, and illegal additives in food products has been associated with a range of potential health hazards. However, no systematic database exists that comprehensively includes and integrates all research information on these compounds, and valuable information remains scattered across numerous databases and extensive literature reports. Thus, using natural language processing technology, we curated 12,018 food risk components from 152,737 literature reports, 12 authoritative databases, and numerous related regulatory documents. Data on molecular structures, physicochemical properties, chemical taxonomy, absorption, distribution, metabolism, excretion, toxicity properties, and physiological targets within the human body were integrated to afford the comprehensive food risk component database (FRCD, http://www.rxnfinder.org/frcd/). We also analyzed the molecular scaffold and chemical diversity, in addition to evaluating the toxicity and biodegradability of the food risk components. The FRCD could be considered a highly promising tool for future food safety studies.

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.

Little Antimicrobial Peptides with Big Therapeutic Roles

Antimicrobial Peptides (AMPs) are short amphipathic biological molecules generally with less than 100 amino acids. AMPs not only present high bioactivities against bacteria, fungi or protists-induced infections, but also play important roles in anticancer activity, immune response and inflammation regulation. AMPs are classified as ribosomally synthesized, non-ribosomally synthesized and post-translationally modified, non-ribosomally synthesized ones and several synthetic or semisynthetic peptides according to their synthesis with or without the involvement of ribosomes. The molecular characterization and bioactivity action mechanisms are summarized for several ribosomally synthesized AMPs and main non-ribosomally synthesized members (cyclopeptides, lipopeptides, glycopeptides, lipoglycopeptides). We also analyze challenges and new strategies to overcome drug resistance and application limitations for AMP discovery. In conclusion, the growing novel small molecular AMPs have huge therapeutic potentials of antibacterial, antiviral, anticancer and immunoregulatory bioactivities through new techniquesdriven drug discovery strategy including bioinformatics prediction, de novo rational design and biosynthesis.

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/.