High-throughput prediction of enzyme promiscuity based on substrate-product pairs.

The screening of enzymes for catalyzing specific substrate-product pairs is often constrained in the realms of metabolic engineering and synthetic biology. Existing tools based on substrate and reaction similarity predominantly rely on prior knowledge, demonstrating limited extrapolative capabilities and an inability to incorporate custom candidate-enzyme libraries. Addressing these limitations, we have developed the Substrate-product Pair-based Enzyme Promiscuity Prediction (SPEPP) model. This innovative approach utilizes transfer learning and transformer architecture to predict enzyme promiscuity, thereby elucidating the intricate interplay between enzymes and substrate-product pairs. SPEPP exhibited robust predictive ability, eliminating the need for prior knowledge of reactions and allowing users to define their own candidate-enzyme libraries. It can be seamlessly integrated into various applications, including metabolic engineering, de novo pathway design, and hazardous material degradation. To better assist metabolic engineers in designing and refining biochemical pathways, particularly those without programming skills, we also designed EnzyPick, an easy-to-use web server for enzyme screening based on SPEPP. EnzyPick is accessible at http://www.biosynther.com/enzypick/.

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/.

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

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.

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.

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.

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/.

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.

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.

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/.

AddictedChem: A Data-Driven Integrated Platform for New Psychoactive Substance Identification.

The mechanisms underlying drug addiction remain nebulous. Furthermore, new psychoactive substances (NPS) are being developed to circumvent legal control; hence, rapid NPS identification is urgently needed. Here, we present the construction of the comprehensive database of controlled substances, AddictedChem. This database integrates the following information on controlled substances from the US Drug Enforcement Administration: physical and chemical characteristics; classified literature by Medical Subject Headings terms and target binding data; absorption, distribution, metabolism, excretion, and toxicity; and related genes, pathways, and bioassays. We created 29 predictive models for NPS identification using five machine learning algorithms and seven molecular descriptors. The best performing models achieved a balanced accuracy (BA) of 0.940 with an area under the curve (AUC) of 0.986 for the test set and a BA of 0.919 and an AUC of 0.968 for the external validation set, which were subsequently used to identify potential NPS with a consensus strategy. Concurrently, a chemical space that included the properties of vectorised addictive compounds was constructed and integrated with AddictedChem, illustrating the principle of diversely existing NPS from a macro perspective. Based on these potential applications, AddictedChem could be considered a highly promising tool for NPS identification and evaluation.

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/.

Parallel Evolution of Chromatin Structure Underlying Metabolic Adaptation.

Parallel evolution occurs when a similar trait emerges in independent evolutionary lineages. Although changes in protein coding and gene transcription have been investigated as underlying mechanisms for parallel evolution, parallel changes in chromatin structure have never been reported. Here, Saccharomyces cerevisiae and a distantly related yeast species, Dekkera bruxellensis, are investigated because both species have independently evolved the capacity of aerobic fermentation. By profiling and comparing genome sequences, transcriptomic landscapes, and chromatin structures, we revealed that parallel changes in nucleosome occupancy in the promoter regions of mitochondria-localized genes led to concerted suppression of mitochondrial functions by glucose, which can explain the metabolic convergence in these two independent yeast species. Further investigation indicated that similar mutational processes in the promoter regions of these genes in the two independent evolutionary lineages underlay the parallel changes in chromatin structure. Our results indicate that, despite several hundred million years of separation, parallel changes in chromatin structure, can be an important adaptation mechanism for different organisms. Due to the important role of chromatin structure changes in regulating gene expression and organism phenotypes, the novel mechanism revealed in this study could be a general phenomenon contributing to parallel adaptation in nature.

The putative cellodextrin transporter-like protein CLP1 is involved in cellulase induction in Neurospora crassa.

Neurospora crassa recently has become a novel system to investigate cellulase induction. Here, we discovered a novel membrane protein, cellodextrin transporter-like protein 1 (CLP1; NCU05853), a putative cellodextrin transporter-like protein that is a critical component of the cellulase induction pathway in N. crassa. Although CLP1 protein cannot transport cellodextrin, the suppression of cellulase induction by this protein was discovered on both cellobiose and Avicel. The co-disruption of the cellodextrin transporters cdt2 and clp1 in strain Δ3ßG formed strain CPL7. With induction by cellobiose, cellulase production was enhanced 6.9-fold in CPL7 compared with Δ3ßG. We also showed that the suppression of cellulase expression by CLP1 occurred by repressing the expression of cellodextrin transporters, particularly cdt1 expression. Transcriptome analysis of the hypercellulase-producing strain CPL7 showed that the cellulase expression machinery was dramatically stimulated, as were the cellulase enzyme genes including the inducer transporters and the major transcriptional regulators.

Heterologous expression of a GH3 ß-glucosidase from Neurospora crassa in Pichia pastoris with high purity and its application in the hydrolysis of soybean isoflavone glycosides.

Previous studies have shown isoflavone aglycones to have more biological effects than their counterparts, isoflavone glycones. Some ß-glucosidases can hydrolyze isoflavone glucosides to release aglycones, and discovery of these has attracted great interest. A glycoside hydrolase (GH) family 3 ß-glucosidase (bgl2) gene from Neurospora crassa was heterologously expressed in Pichia pastoris with high purity. The recombinant BGL2 enzyme displayed its highest activity at pH 5.0 and 60 °C, and had its maximum activity against p-nitrophenyl-ß-d-glucopyranoside (pNPG) (143.27 ± 4.79 U/mg), followed by cellobiose (74.99 ± 0.78 U/mg), gentiobiose (47.55 ± 0.15 U/mg), p-nitrophenyl-ß-d-cellobioside (pNPC) (40.07 ± 0.87 U/mg), cellotriose (12.31 ± 0.36 U/mg) and cellotetraose (9.04 ± 0.14 U/mg). The kinetic parameters of Km and Vmax were 0.21 ± 0.01 mM and 147.93 ± 2.77 µM/mg/min for pNPG. The purified enzyme showed a heightened ability to convert the major soybean isoflavone glycosides (daidzin, genistin and glycitin) into their corresponding aglycone forms (daidzien, genistein and glycitein). With this activity against soybean isoflavone glycosides, BGL2 shows great potential for applications in the food, animal feed, and pharmaceutical industries.

Functional Analysis of Two l-Arabinose Transporters from Filamentous Fungi Reveals Promising Characteristics for Improved Pentose Utilization in Saccharomyces cerevisiae.

Limited uptake is one of the bottlenecks for l-arabinose fermentation from lignocellulosic hydrolysates in engineered Saccharomyces cerevisiae. This study characterized two novel l-arabinose transporters, LAT-1 from Neurospora crassa and MtLAT-1 from Myceliophthora thermophila. Although the two proteins share high identity (about 83%), they display different substrate specificities. Sugar transport assays using the S. cerevisiae strain EBY.VW4000 indicated that LAT-1 accepts a broad substrate spectrum. In contrast, MtLAT-1 appeared much more specific for l-arabinose. Determination of the kinetic properties of both transporters revealed that the Km values of LAT-1 and MtLAT-1 for l-arabinose were 58.12 ± 4.06 mM and 29.39 ± 3.60 mM, respectively, with corresponding Vmax values of 116.7 ± 3.0 mmol/h/g dry cell weight (DCW) and 10.29 ± 0.35 mmol/h/g DCW, respectively. In addition, both transporters were found to use a proton-coupled symport mechanism and showed only partial inhibition by d-glucose during l-arabinose uptake. Moreover, LAT-1 and MtLAT-1 were expressed in the S. cerevisiae strain BSW2AP containing an l-arabinose metabolic pathway. Both recombinant strains exhibited much faster l-arabinose utilization, greater biomass accumulation, and higher ethanol production than the control strain. In conclusion, because of higher maximum velocities and reduced inhibition by d-glucose, the genes for the two characterized transporters are promising targets for improved l-arabinose utilization and fermentation in S. cerevisiae.

Mutagenesis and evaluation of cellulase properties and cellulose hydrolysis of Talaromyces piceus.

A fungal species with a high yield of ß-glucosidase was isolated and identified as Talaromyces piceus 9-3 (anamorph: Penicillium piceum) by morphological and molecular characterization. Through dimethyl sulphate mutagenesis, the cellulase over-producing strain T. piceus H16 was obtained. The FPase activity and ß-glucosidase activity of T. piceus H16 were 5.83 and 53.12 IU ml(-1) respectively--a 5.34- and 4.43-times improvement from the parent strain T. piceus 9-3. The optimum pH and temperature for enzyme activity were pH 5.0 and 50 °C for FPase activity and pH 5.0 and 55 °C for ß-glucosidase activity, respectively. The cellulase were quite stable at 37 °C, only losing <10% of their initial activity after 24 h of incubation. Hydrolysis analysis results showed that a highly efficient synergistic effect was achieved by combining cellulase from T. piceus H16 with that from Trichoderma reesei RUT C30 on hydrolyzing different substrates due to the high ß-glucosidase activity of T. piceus H16. These data suggest that T. piceus H16 can be used as a potential cellulase producer with good prospects.

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study.

Background: Acute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources. Methods: In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model. Results: Eleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain. Conclusion: The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.

MCF2Chem: A manually curated knowledge base of biosynthetic compound production.

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.

Data-Driven Prediction of Molecular Biotransformations in Food Fermentation.

Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive metabolites produced during food fermentation. Hence, we propose a data-driven integrated platform (FFExplorer, http://www.rxnfinder.org/ffexplorer/) based on machine learning and data on 2,192,862 microbial sequence-encoded enzymes for computational prediction of fermentation products. Using FFExplorer, we explained the mechanism behind the disappearance of spicy taste during pepper fermentation and evaluated the detoxification effects of microbial fermentation for common food contaminants. FFExplorer will provide a valuable reference for inferring bioactive "dark matter" in fermented foods and exploring the application potential of microorganisms.