BioBulkFoundary: a customized webserver for exploring biosynthetic potentials of bulk chemicals.

SUMMARY: Advances in metabolic engineering have boosted the production of bulk chemicals, resulting in tons of production volumes of some bulk chemicals with very low prices. A decrease in the production cost and overproduction of bulk chemicals makes it necessary and desirable to explore the potential to synthesize higher-value products from them. It is also useful and important for society to explore the use of design methods involving synthetic biology to increase the economic value of these bulk chemicals. Therefore, we developed 'BioBulkFoundary', which provides an elaborate analysis of the biosynthetic potential of bulk chemicals based on the state-of-art exploration of pathways to synthesize value-added chemicals, along with associated comprehensive technology and economic database into a user-friendly framework. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://design.rxnfinder.org/biobulkfoundary/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SynBioStrainFinder: A microbial strain database of manually curated CRISPR/Cas genetic manipulation system information for biomanufacturing.

BACKGROUND: Microbial strain information databases provide valuable data for microbial basic research and applications. However, they rarely contain information on the genetic operating system of microbial strains. RESULTS: We established a comprehensive microbial strain database, SynBioStrainFinder, by integrating CRISPR/Cas gene-editing system information with cultivation methods, genome sequence data, and compound-related information. It is presented through three modules, Strain2Gms/PredStrain2Gms, Strain2BasicInfo, and Strain2Compd, which combine to form a rapid strain information query system conveniently curated, integrated, and accessible on a single platform. To date, 1426 CRISPR/Cas gene-editing records of 157 microbial strains have been manually extracted from the literature in the Strain2Gms module. For strains without established CRISPR/Cas systems, the PredStrain2Gms module recommends the system of the most closely related strain as a reference to facilitate the construction of a new CRISPR/Cas gene-editing system. The database contains 139,499 records of strain cultivation and genome sequences, and 773,298 records of strain-related compounds. To facilitate simple and intuitive data application, all microbial strains are also labeled with stars based on the order and availability of strain information. SynBioStrainFinder provides a user-friendly interface for querying, browsing, and visualizing detailed information on microbial strains, and it is publicly available at http://design.rxnfinder.org/biosynstrain/ . CONCLUSION: SynBioStrainFinder is the first microbial strain database with manually curated information on the strain CRISPR/Cas system as well as other microbial strain information. It also provides reference information for the construction of new CRISPR/Cas systems. SynBioStrainFinder will serve as a useful resource to extend microbial strain research and application for biomanufacturing.

ChemHub: a knowledgebase of functional chemicals for synthetic biology studies.

SUMMARY: The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90 000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600 000 published studies by more than 100 people over the past 10 years. AVAILABILITY AND IMPLEMENTATION: Multiple algorithms were implemented to enable biosynthetic pathway design and precursor discovery, which can support investigation of the biosynthetic potential of these functional chemicals. ChemHub is freely available at: http://www.rxnfinder.org/chemhub/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model.

MOTIVATION: The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. RESULTS: We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. AVAILABILITYAND IMPLEMENTATION: SARS2020 is available at http://design.rxnfinder.org/sars2020/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

EnzyMine: a comprehensive database for enzyme function annotation with enzymatic reaction chemical feature.

Addition of chemical structural information in enzymatic reactions has proven to be significant for accurate enzyme function prediction. However, such chemical data lack systematic feature mining and hardly exist in enzyme-related databases. Therefore, global mining of enzymatic reactions will offer a unique landscape for researchers to understand the basic functional mechanisms of natural bioprocesses and facilitate enzyme function annotation. Here, we established a new knowledge base called EnzyMine, through which we propose to elucidate enzymatic reaction features and then link them with sequence and structural annotations. EnzyMine represents an advanced database that extends enzyme knowledge by incorporating reaction chemical feature strategies, strengthening the connectivity between enzyme and metabolic reactions. Therefore, it has the potential to reveal many new metabolic pathways involved with given enzymes, as well as expand enzyme function annotation. Database URL: http://www.rxnfinder.org/enzymine/.

FADB-China: A molecular-level food adulteration database in China based on molecular fingerprints and similarity algorithms prediction expansion.

Food adulteration is a growing concern worldwide. The collation and analysis of food adulteration cases is of immense significance for food safety regulation and research. We collected 961 cases of food adulteration between 1998 and 2019 from the literature reports and announcements released by the Chinese government. Critical molecules were manually annotated in food adulteration substances as determined by food chemists, to build the first food adulteration database in China (http://www.rxnfinder.org/FADB-China/). This database is also the first molecular-level food adulteration database worldwide. Additionally, we herein propose an in silico method for predicting potentially illegal food additives on the basis of molecular fingerprints and similarity algorithms. Using this algorithm, we predict 1919 chemicals that may be illegally added to food; these predictions can effectively assist in the discovery and prevention of emerging food adulteration.

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/.

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.

RxnBLAST: molecular scaffold and reactive chemical environment feature extractor for biochemical reactions.

MOTIVATION: Molecular scaffolds are useful in medicinal chemistry to describe, discuss and visualize series of chemical compounds, biochemical transformations and associated biological properties. RESULTS: Here, we present RxnBLAST as a web-based tool for analyzing scaffold transformations and reactive chemical environment features in bioreactions. RxnBLAST extracts chemical features from bioreactions including atom-atom mapping, reaction centers, rules and functional groups to help understand chemical compositions and reaction patterns. Core-to-Core is proposed, which can be utilized in scaffold networks and for constructing a reaction space, as well as providing guidance for subsequent biosynthesis efforts. AVAILABILITY AND IMPLEMENTATION: RxnBLAST is available at: http://design.rxnfinder.org/rxnblast/.

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.

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.

AdditiveChem: A comprehensive bioinformatics knowledge-base for food additive chemicals.

Food additives are considered to be the catalysts and headstones of the modern food industry, affecting every step of food production, processing, and storage. The urgent need for a comprehensive curation of food additives, including their molecular structures, biological activities, and precise toxicological evaluations, prompted the creation of the AdditiveChem database (http://www.rxnfinder.org/additivechem/). This database has curated >9064 types of food additives, along with their molecular structure, chemical and physical properties, absorption, distribution, metabolism, excretion and toxicity properties, biosynthesis and biodegradation methods, usage specifications, toxicological and risk assessment data, and targets in the human body from 16 databases to construct an efficient search platform for in silico preliminary evaluations. AdditiveChem database will enable an exploration of the relationship between the structure and function of food additives.

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/.

PAP-1 ameliorates DSS-induced colitis with involvement of NLRP3 inflammasome pathway.

BACKGROUND: Macrophages are a primary type of innate immune cells activated in colitis. Kv1.3 channel is one of the major potassium channels in macrophages. NLRP3 inflammasome is a downstream molecule of Kv1.3 channel. PAP-1, a specific Kv1.3 channel blocker, has been shown to have immune-regulatory effects. OBJECTIVE: To investigate the effect of PAP-1 on intestinal inflammation in DSS-induced colitis and explore its possible mechanism. METHODS: C57BL/6 mice were divided into four groups: normal control group, normal+PAP-1 injection group, DSS model group, DSS model+PAP-1 injection group. Experimental colitis was induced by 5% DSS treatment; mice were injected intraperitoneally with PAP-1 from the first day for 7 consecutive days; then all mice were sacrificed, followed by isolation of colon tissue, peritoneal macrophages and spleen macrophages. The anti-inflammatory effects of PAP-1 and the expression levels of Kv1.3, iNOS, pro-IL-1ß, IL-1ß and NLRP3 inflammasome were measured. RESULTS: PAP-1 reduced DSS-induced colonic pathological damage, DAI score, MPO activity and levels of IL-1, IL-6, TNF-a, IL-18. Compared with the DSS model group, the expression of Kv1.3, iNOS, NLRP3, ASC, caspase-1p20, pro-IL-1ß and IL-1ß in colon were decreased in the DSS-induced colitis mice with PAP-1 injection. PAP-1 also reduced the expression of Kv1.3, iNOS, NLRP3, caspase-1p20 and IL-1ß on macrophages in colitis mice. CONCLUSION: PAP-1 had protective effects on DSS-induced colitis, which might be ascribed to the regulation of NLRP3 inflammasome pathway. Therefore, we found that PAP-1 was useful as a therapeutic agent in IBD and suggested a potential important role of PAP-1 in NLRP3 inflammasome-associated diseases.

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.

Resolvin D1 Resolve Inflammation in Experimental Acute Pancreatitis by Restoring Autophagic Flux.

BACKGROUND: Acute pancreatitis (AP) is a common acute gastrointestinal disorders. Increasing evidence indicated that autophagy is involved in the development of AP. Resolvin D1 is an endogenous pro-resolving lipid mediator, which can protect mice from cerulein-induced acute pancreatitis and facilitate autophagy in macrophage, but its mechanism remians unclear. AIMS: To investigate the effect of resolvin D1 on autophagy in mouse models of cerulein-induced AP. METHODS: C57BL/6 mice were randomly divided into control group, AP group and resolvin D1 group. The models of cerulein-induced AP were constructed by intraperitoneally cerulein. Resolvin D1 group was established by intraperitoneally resolvin D1 based on AP models, simultaneously, control group received normal saline. The severity of AP, the level of inflammatory cytokines, the number of autophagic vacuoles, and the expression of autophagy-related markers were evaluated among three groups. RESULTS: The AP models were established successfully. Compared to control group, the number of autophagic vacuoles and expressions of autophagy-related markers including Beclin-1, p62 and LC3-II were increased in AP models, In contrast, the degree of inflammation and levels of inflammatory cytokines in AP models were reduced after resolvin D1 treatment. Moreover, resolvin D1 attenuated the number of autophagic vacuoles and expressions of autophagy-related markers. CONCLUSIONS: Autophagic flux is impaired in cerulein-induced AP. Resolvin D1 ameliorate the severity of mice with cerulein-induced acute pancreatitis, possible attributing to its reducing impaired autophagy and restoring autophagic flux.

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/ .