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/

Enhanced Artificial Enzyme Activities on the Reconstructed Sawtoothlike Nanofacets of Pure and Pr-Doped Ceria Nanocubes

In this work, a simple one-step thermal oxidation process was established to achieve a significant surface increase in {110} and {111} nanofacets on well-defined, pure and Pr-doped, ceria nanocubes. More importantly, without changing most of the bulk properties, this treatment leads to a remarkable boost of their enzymatic activities: from the oxidant (oxidase-like) to antioxidant (hydroxyl radical scavenging) as well as the paraoxon degradation (phosphatase-like) activities. Such performance improvement might be due to the thermally generated sawtoothlike {111} nanofacets and defects, which facilitate the oxygen mobility and the formation of oxygen vacancies on the surface. Finally, possible mechanisms of nanoceria as artificial enzymes have been proposed in this manuscript. Considering the potential application of ceria as artificial enzymes, this thermal treatment may enable the future design of highly efficient nanozymes without changing the bulk composition.This work has been supported by the Ministry of Science, Innovation and Universities of Spain with Reference Numbers of ENE2017-82451-C3-2-R, MAT2016-81118-P and MAT2017-87579-R. The research projects funded by the Natural Science Foundation of Shandong Province (Grant ZR2017LB028), Key R&D Program of Shandong Province (Grant 2018GSF118032), and Fundamental Research Funds for the Central Universities (Grant 18CX02125A) in China are also acknowledged. TEM/STEM data were obtained at DMEUCA node of the Spanish Unique Scientific and Technological Infrastructure (ICTS) of Electron Microscopy of Materials ELECMIM. M. Tinoco thanks the FPU Scholarship Program (Grant AP2010-3737) from Ministry of Education of Spain. H. Pan is grateful for financial support (Grant 201406140130) from the Chinese Scholarship Council to accomplish her Ph.D. study at the University of Cadiz (Spain). J. M. Gonzalez, G. Blanco, and X. Chen are also grateful for the financial support from the joint project (Proyectos Integradores, Grant PI20201) in IMEYMAT of the University of Cadiz

MCF2Chem: A manually curated knowledge base of biosynthetic compound production

Abstract Background Microbes have been used as cell factories to synthesize various chemical compounds. Recent advances in synthetic biological technologies have accelerated the increase in the number and capacity of microbial cell factories; the variety and number of synthetic compounds produced via these cell factories have also grown substantially. However, no database is available that provides detailed information on the microbial cell factories and the synthesized compounds. Results In this study, we established MCF2Chem, a manually curated knowledge base on the production of biosynthetic compounds using microbial cell factories. It contains 8888 items of production records related to 1231 compounds that were synthesizable by 590 microbial cell factories, including the production data of compounds (titer, yield, productivity, and content), strain culture information (culture medium, carbon source/precursor/substrate), fermentation information (mode, vessel, scale, and condition), and other information (e.g., strain modification method). The database contains statistical analyses data of compounds and microbial species. The data statistics of MCF2Chem showed that bacteria accounted for 60% of the species and that “fatty acids”, “terpenoids”, and “shikimates and phenylpropanoids” accounted for the top three chemical products. Escherichia coli, Saccharomyces cerevisiae, Yarrowia lipolytica, and Corynebacterium glutamicum synthesized 78% of these chemical compounds. Furthermore, we constructed a system to recommend microbial cell factories suitable for synthesizing target compounds and vice versa by combining MCF2Chem data, additional strain- and compound-related data, the phylogenetic relationships between strains, and compound similarities. Conclusions MCF2Chem provides a user-friendly interface for querying, browsing, and visualizing detailed statistical information on microbial cell factories and their synthesizable compounds. It is publicly available at https://mcf.lifesynther.com . This database may serve as a useful resource for synthetic biologists

RDBridge: a knowledge graph of rare diseases based on large-scale text mining

Motivation: Despite low prevalence, rare diseases affect 300 million people worldwide. Research on pathogenesis and drug development lags due to limited commercial potential, insufficient epidemiological data, and a dearth of publications. The unique characteristics of rare diseases, including limited annotated data, intricate processes for extracting pertinent entity relationships, and difficulties in standardizing data, represent challenges for text mining. Results: We developed a rare disease data acquisition framework using text mining and knowledge graphs and constructed the most comprehensive rare disease knowledge graph to date, Rare Disease Bridge (RDBridge). RDBridge offers search functions for genes, potential drugs, pathways, literature, and medical imaging data that will support mechanistic research, drug development, diagnosis, and treatment for rare diseases. Availability and implementation: RDBridge is freely available at http://rdb.lifesynther.com/.ISSN:1367-4803ISSN:1460-205

Solar-to-hydrogen efficiency exceeding 2.5% achieved for overall water splitting with an all earth-abundant dual-photoelectrode

The solar-to-hydrogen (STH) efficiency of a traditional mono-photoelectrode photoelectrochemical water splitting system has long been limited as large external bias is required. Herein, overall water splitting with STH efficiency exceeding 2.5% was achieved using a self-biased photoelectrochemical-photovoltaic coupled system consisting of an all earth-abundant photoanode and a Si-solar-cell-based photocathode connected in series under parallel illumination. We found that parallel irradiation mode shows higher efficiency than tandem illumination especially for photoanodes with a wide light absorption range, probably as the driving force for water splitting reaction is larger and the photovoltage loss is smaller in the former. This work essentially takes advantage of a tandem solar cell which can enhance the solar-to-electricity efficiency from another point of view

SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

Abstract Background The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. Results We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users’ understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. Conclusions SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection

Additional file 2 of SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

Additional file 2. The code zip file on extracting tabular information from papers by paring the full-text XML file

Additional file 1 of SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

Additional file 1. The code zip file on extracting tabular information from papers in PDF format based on OCR

Additional file 3 of SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

Additional file 3. The list of reviews used for the tool and tool information extraction

Deep Learning Enables Rapid Identification of Mycotoxin-Degrading Enzymes

The identification of functional enzymes for the catalysis of specific biochemical reactions is a major bottleneck in the de novo design of biosynthesis and biodegradation pathways. Conventional methods based on microbial screening and functional metagenomics require long verification periods and incur high experimental costs; recent data-driven methods are only applicable to a few common substrates. To enable rapid and high-throughput identification of enzymes for complex and less-studied substrates, we propose a robust enzyme promiscuity prediction model based on positive unlabeled learning, which shortens the time needed for new enzyme discovery from several years to 29 days. Using this model, we identified 15 new degrading enzymes specific for the mycotoxins ochratoxin A and zearalenone, of which six could degrade > 90% mycotoxin content within 3 h. We anticipate that this model will become indispensable for the identification of new functional enzymes, thereby advancing the fields of synthetic biology, metabolic engineering, and pollutant biodegradation