Properties of Stress Granule and P-Body Proteomes.

Stress granules and P-bodies are cytosolic biomolecular condensates that dynamically form by the phase separation of RNAs and proteins. They participate in translational control and buffer the proteome. Upon stress, global translation halts and mRNAs bound to the translational machinery and other proteins coalesce to form stress granules (SGs). Similarly, translationally stalled mRNAs devoid of translation initiation factors shuttle to P-bodies (PBs). Here, we review the cumulative progress made in defining the protein components that associate with mammalian SGs and PBs. We discuss the composition of SG and PB proteomes, supported by a new user-friendly database (http://rnagranuledb.lunenfeld.ca/) that curates current literature evidence for genes or proteins associated with SGs or PBs. As previously observed, the SG and PB proteomes are biased toward intrinsically disordered regions and have a high propensity to contain primary sequence features favoring phase separation. We also provide an outlook on how the various components of SGs and PBs may cooperate to organize and form membraneless organelles.

Construction of the ETECFinder database for the characterization of enterotoxigenic Escherichia coli (ETEC) and revision of the VirulenceFinder web tool at the CGE website.

The identification of pathogens is essential for effective surveillance and outbreak detection, which lately has been facilitated by the decreasing cost of whole-genome sequencing (WGS). However, extracting relevant virulence genes from WGS data remains a challenge. In this study, we developed a web-based tool to predict virulence-associated genes in enterotoxigenic Escherichia coli (ETEC), which is a major concern for human and animal health. The database includes genes encoding the heat-labile toxin (LT) (eltA and eltB), heat-stable toxin (ST) (est), colonization factors CS1 through 30, F4, F5, F6, F17, F18, and F41, as well as toxigenic invasion and adherence loci (tia, tibAC, etpBAC, eatA, yghJ, and tleA). To construct the database, we revised the existing ETEC nomenclature and used the VirulenceFinder webtool at the CGE website [VirulenceFinder 2.0 (dtu.dk)]. The database was tested on 1,083 preassembled ETEC genomes, two BioProjects (PRJNA421191 with 305 and PRJNA416134 with 134 sequences), and the ETEC reference genome H10407. In total, 455 new virulence gene alleles were added, 50 alleles were replaced or renamed, and two were removed. Overall, our tool has the potential to greatly facilitate ETEC identification and improve the accuracy of WGS analysis. It can also help identify potential new virulence genes in ETEC. The revised nomenclature and expanded gene repertoire provide a better understanding of the genetic diversity of ETEC. Additionally, the user-friendly interface makes it accessible to users with limited bioinformatics experience. IMPORTANCE: Detecting colonization factors in enterotoxigenic Escherichia coli (ETEC) is challenging due to their large number, heterogeneity, and lack of standardized tests. Therefore, it is important to include these ETEC-related genes in a more comprehensive VirulenceFinder database in order to obtain a more complete coverage of the virulence gene repertoire of pathogenic types of E. coli. ETEC vaccines are of great importance due to the severity of the infections, primarily in children. A tool such as this could assist in the surveillance of ETEC in order to determine the prevalence of relevant types in different parts of the world, allowing vaccine developers to target the most prevalent types and, thus, a more effective vaccine.

The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol.

INTRODUCTION: Research utilising artificial intelligence (AI) and cardiovascular magnetic resonance (CMR) is rapidly evolving with various objectives, however AI model development, generalisation and performance may be hindered by availability of robust training datasets including contrast enhanced images. METHODS: NotIs CMR is a large UK, prospective, multicentre, observational cohort study to guide the development of a biventricular AI scar model. Patients with ischaemic heart disease undergoing clinically indicated contrast-enhanced cardiac magnetic resonance imaging will be recruited at Nottingham University Hospitals NHS Trust and Mid-Yorkshire Hospital NHS Trust. Baseline assessment will include cardiac magnetic resonance imaging, demographic data, medical history, electrocardiographic and serum biomarkers. Participants will undergo monitoring for a minimum of 5 years to document any major cardiovascular adverse events. The main objectives include (1) AI training, validation and testing to improve the performance, applicability and adaptability of an AI biventricular scar segmentation model being developed by the authors and (2) develop a curated, disease-specific imaging database to support future research and collaborations and, (3) to explore associations in clinical outcome for future risk prediction modelling studies. CONCLUSION: NotIs CMR will collect and curate disease-specific, paired imaging and clinical datasets to develop an AI biventricular scar model whilst providing a database to support future research and collaboration in Artificial Intelligence and ischaemic heart disease.

An update on genetic variants of the NKX2-5.

NKX2-5 is a homeodomain transcription factor that plays a crucial role in heart development. It is the first gene where a single genetic variant (GV) was found to be associated with congenital heart diseases in humans. In this study, we carried out a comprehensive survey of NKX2-5 GVs to build a unified, curated, and updated compilation of all available GVs. We retrieved a total of 1,380 unique GVs. From these, 970 had information on their frequency in the general population and 143 have been linked to pathogenic phenotypes in humans. In vitro effect was ascertained for 38 GVs. The homeodomain had the biggest cluster of pathogenic variants in the protein: 49 GVs in 60 residues, 23 in its third α-helix, where 11 missense variants may affect protein-DNA interaction or the hydrophobic core. We also pinpointed the likely location of pathogenic GVs in four linear motifs. These analyses allowed us to assign a putative explanation for the effect of 90 GVs. This study pointed to reliable pathogenicity for GVs in helix 3 of the homeodomain and may broaden the scope of functional and structural studies that can be done to better understand the effect of GVs in NKX2-5 function.

Context-specific interactions in literature-curated protein interaction databases.

BACKGROUND: Databases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study. RESULTS: Using raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate. CONCLUSIONS: We recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.

HADEG: A curated hydrocarbon aerobic degradation enzymes and genes database.

Databases of genes and enzymes involved in hydrocarbon degradation have been previously reported. However, these databases specialize on only a specific group of hydrocarbons and/or are constructed partly based on enzyme sequences with putative functions indicated by in silico research, with no experimental evidence. Here, we present a curated database of Hydrocarbon Aerobic Degradation Enzymes and Genes (HADEG) containing proteins and genes involved in alkane, alkene, aromatic, and plastic aerobic degradation and biosurfactant production based solely on experimental evidence, which are present in bacteria, and fungi. HADEG includes 259 proteins for petroleum hydrocarbon degradation, 160 for plastic degradation, and 32 for biosurfactant production. This database will help identify and predict hydrocarbon degradation genes/pathways and biosurfactant production in genomes.

The TriMet_DB: A Manually Curated Database of the Metabolic Proteins of Triticum aestivum.

Mass-spectrometry-based wheat proteomics is challenging because the current interpretation of mass spectrometry data relies on public databases that are not exhaustive (UniProtKB/Swiss-Prot) or contain many redundant and poor or un-annotated entries (UniProtKB/TrEMBL). Here, we report the development of a manually curated database of the metabolic proteins of Triticum aestivum (hexaploid wheat), named TriMet_DB (Triticum aestivum Metabolic Proteins DataBase). The manually curated TriMet_DB was generated in FASTA format so that it can be read directly by programs used to interpret the mass spectrometry data. Furthermore, the complete list of entries included in the TriMet_DB is reported in a freely available resource, which includes for each protein the description, the gene code, the protein family, and the allergen name (if any). To evaluate its performance, the TriMet_DB was used to interpret the MS data acquired on the metabolic protein fraction extracted from the cultivar MEC of Triticum aestivum. Data are available via ProteomeXchange with identifier PXD037709.

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT1A Receptor Case.

The drug discovery and development process requires a lot of time, financial, and workforce resources. Any reduction in these burdens might benefit all stakeholders in the healthcare domain, including patients, government, and companies. One of the critical stages in drug discovery is a selection of molecular structures with a strong affinity to a particular molecular target. The possible solution is the development of predictive models and their application in the screening process, but due to the complexity of the problem, simple and statistical models might not be sufficient for practical application. The manuscript presents the best-in-class predictive model for the serotonin 1A receptor affinity and its validation according to the Organization for Economic Co-operation and Development guidelines for regulatory purposes. The model was developed based on a database with close to 9500 molecules by using an automatic machine learning tool (AutoML). The model selection was conducted based on the Akaike information criterion value and 10-fold cross-validation routine, and later good predictive ability was confirmed with an additional external validation dataset with over 700 molecules. Moreover, the multi-start technique was applied to test if an automatic model development procedure results in reliable results.

The plant endoplasmic reticulum UPRome: A repository and pathway browser for genes involved in signaling networks linked to the endoplasmic reticulum.

The endoplasmic reticulum (ER) houses sensors that respond to environmental stress and underly plants' adaptative responses. These sensors transduce signals that lead to changes in nuclear gene expression. The ER to nuclear signaling pathways are primarily attributed to the unfolded protein response (UPR) and are also integrated with a wide range of development, hormone, immune, and stress signaling pathways. Understanding the role of the UPR in signaling network mechanisms that associate with particular phenotypes is crucially important. While UPR-associated genes are the subject of ongoing investigations in a few model plant systems, most remain poorly annotated, hindering the identification of candidates across plant species. This open-source curated database provides a centralized resource of peer reviewed knowledge of ER to nuclear signaling pathways for the plant community. We provide a UPRome interactive viewer for users to navigate through the pathways and to access annotated information. The plant ER UPRome website is located at http://uprome.tamu.edu. We welcome contributions from the researchers studying the ER UPR to incorporate additional genes into the database through the "contact us" page.

Microbial Identification Using rRNA Operon Region: Database and Tool for Metataxonomics with Long-Read Sequence.

Recent development of long-read sequencing platforms has enabled researchers to explore bacterial community structure through analysis of full-length 16S rRNA gene (∼1,500 bp) or 16S-ITS-23S rRNA operon region (∼4,300 bp), resulting in higher taxonomic resolution than short-read sequencing platforms. Despite the potential of long-read sequencing in metagenomics, resources and protocols for this technology are scarce. Here, we describe MIrROR, the database and analysis tool for metataxonomics using the bacterial 16S-ITS-23S rRNA operon region. We collected 16S-ITS-23S rRNA operon sequences extracted from bacterial genomes from NCBI GenBank and performed curation. A total of 97,781 16S-ITS-23S rRNA operon sequences covering 9,485 species from 43,653 genomes were obtained. For user convenience, we provide an analysis tool based on a mapping strategy that can be used for taxonomic profiling with MIrROR database. To benchmark MIrROR, we compared performance against publicly available databases and tool with mock communities and simulated data sets. Our platform showed promising results in terms of the number of species covered and the accuracy of classification. To encourage active 16S-ITS-23S rRNA operon analysis in the field, BLAST function and taxonomic profiling results with 16S-ITS-23S rRNA operon studies, which have been reported as BioProject on NCBI are provided. MIrROR (http://mirror.egnome.co.kr/) will be a useful platform for researchers who want to perform high-resolution metagenome analysis with a cost-effective sequencer such as MinION from Oxford Nanopore Technologies. IMPORTANCE Metabarcoding is a powerful tool to investigate community diversity in an economic and efficient way by amplifying a specific gene marker region. With the advancement of long-read sequencing technologies, the field of metabarcoding has entered a new phase. The technologies have brought a need for development in several areas, including new markers that long-read can cover, database for the markers, tools that reflect long-read characteristics, and compatibility with downstream analysis tools. By constructing MIrROR, we met the need for a database and tools for the 16S-ITS-23S rRNA operon region, which has recently been shown to have sufficient resolution at the species level. Bacterial community analysis using the 16S-ITS-23S rRNA operon region with MIrROR will provide new insights from various research fields.

cFos-ANAB: A cFos-based Web Tool for Exploring Activated Neurons and Associated Behaviors.

cFos is one of the most widely-studied genes in the field of neuroscience. Currently, there is no systematic database focusing on cFos in neuroscience. We developed a curated database-cFos-ANAB-a cFos-based web tool for exploring activated neurons and associated behaviors in rats and mice, comprising 398 brain nuclei and sub-nuclei, and five associated behaviors: pain, fear, feeding, aggression, and sexual behavior. Direct relationships among behaviors and nuclei (even cell types) under specific stimulating conditions were constructed based on cFos expression profiles extracted from original publications. Moreover, overlapping nuclei and sub-nuclei with potentially complex functions among different associated behaviors were emphasized, leading to results serving as important clues to the development of valid hypotheses for exploring as yet unknown circuits. Using the analysis function of cFos-ANAB, multi-layered pictures of networks and their relationships can quickly be explored depending on users' purposes. These features provide a useful tool and good reference for early exploration in neuroscience. The cFos-ANAB database is available at www.cfos-db.net .

AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application to Corynebacterium striatum, an Emerging Pathogen.

Antimicrobial peptides (AMPs) have been recognized for their ability to target processes important for biofilm formation. Given the vast array of AMPs, identifying potential anti-biofilm candidates remains a significant challenge, and prompts the need for preliminary in silico investigations prior to extensive in vitro and in vivo studies. We have developed Biofilm-AMP (B-AMP), a curated 3D structural and functional repository of AMPs relevant to biofilm studies. In its current version, B-AMP contains predicted 3D structural models of 5544 AMPs (from the DRAMP database) developed using a suite of molecular modeling tools. The repository supports a user-friendly search, using source, name, DRAMP ID, and PepID (unique to B-AMP). Further, AMPs are annotated to existing biofilm literature, consisting of a vast library of over 10,000 articles, enhancing the functional capabilities of B-AMP. To provide an example of the usability of B-AMP, we use the sortase C biofilm target of the emerging pathogen Corynebacterium striatum as a case study. For this, 100 structural AMP models from B-AMP were subject to in silico protein-peptide molecular docking against the catalytic site residues of the C. striatum sortase C protein. Based on docking scores and interacting residues, we suggest a preference scale using which candidate AMPs could be taken up for further in silico, in vitro and in vivo testing. The 3D protein-peptide interaction models and preference scale are available in B-AMP. B-AMP is a comprehensive structural and functional repository of AMPs, and will serve as a starting point for future studies exploring AMPs for biofilm studies. B-AMP is freely available to the community at https://b-amp.karishmakaushiklab.com and will be regularly updated with AMP structures, interaction models with potential biofilm targets, and annotations to biofilm literature.

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.

Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure-activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R2) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model's predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.

Accessing Expert-Curated Pharmacological Data in the IUPHAR/BPS Guide to PHARMACOLOGY.

The IUPHAR/BPS Guide to PHARMACOLOGY is an expert-curated, open-access database of information on drug targets and the substances that act on them. This unit describes the procedures for searching and downloading ligand-target binding data and for finding detailed annotations and the most relevant literature. The database includes concise overviews of the properties of 1,700 data-supported human drug targets and related proteins, divided into families, and 9,000 small molecule and peptide experimental ligands and approved drugs that bind to those targets. More detailed descriptions of pharmacology, function, and pathophysiology are provided for a subset of important targets. The information is reviewed regularly by expert subcommittees of the IUPHAR Committee on Receptor Nomenclature and Drug Classification. A new immunopharmacology portal has recently been added, drawing together data on immunological targets, ligands, cell types, processes and diseases. The data are available for download and can be accessed computationally via Web services. © 2018 by John Wiley & Sons, Inc.

cFos-ANAB: A cFos-based Web Tool for Exploring Activated Neurons and Associated Behaviors / 神经科学通报·英文版

cFos is one of the most widely-studied genes in the field of neuroscience. Currently, there is no systematic database focusing on cFos in neuroscience. We developed a curated database—cFos-ANAB—a cFos-based web tool for exploring activated neurons and associated behaviors in rats and mice, comprising 398 brain nuclei and sub-nuclei, and five associated behaviors: pain, fear, feeding, aggression, and sexual behavior. Direct relationships among behaviors and nuclei (even cell types) under specific stimulating conditions were constructed based on cFos expression profiles extracted from original publications. Moreover, overlapping nuclei and sub-nuclei with potentially complex functions among different associated behaviors were emphasized, leading to results serving as important clues to the development of valid hypotheses for exploring as yet unknown circuits. Using the analysis function of cFos-ANAB, multi-layered pictures of networks and their relationships can quickly be explored depending on users’ purposes. These features provide a useful tool and good reference for early exploration in neuroscience. The cFos-ANAB database is available at www.cfos-db.net.

cFos-ANAB: A cFos-based Web Tool for Exploring Activated Neurons and Associated Behaviors / 神经科学通报·英文版

cFos is one of the most widely-studied genes in the field of neuroscience. Currently, there is no systematic database focusing on cFos in neuroscience. We developed a curated database-cFos-ANAB-a cFos-based web tool for exploring activated neurons and associated behaviors in rats and mice, comprising 398 brain nuclei and sub-nuclei, and five associated behaviors: pain, fear, feeding, aggression, and sexual behavior. Direct relationships among behaviors and nuclei (even cell types) under specific stimulating conditions were constructed based on cFos expression profiles extracted from original publications. Moreover, overlapping nuclei and sub-nuclei with potentially complex functions among different associated behaviors were emphasized, leading to results serving as important clues to the development of valid hypotheses for exploring as yet unknown circuits. Using the analysis function of cFos-ANAB, multi-layered pictures of networks and their relationships can quickly be explored depending on users' purposes. These features provide a useful tool and good reference for early exploration in neuroscience. The cFos-ANAB database is available at www.cfos-db.net .

Virk: an active learning-based system for bootstrapping knowledge base development in the neurosciences.

The frequency and volume of newly-published scientific literature is quickly making manual maintenance of publicly-available databases of primary data unrealistic and costly. Although machine learning (ML) can be useful for developing automated approaches to identifying scientific publications containing relevant information for a database, developing such tools necessitates manually annotating an unrealistic number of documents. One approach to this problem, active learning (AL), builds classification models by iteratively identifying documents that provide the most information to a classifier. Although this approach has been shown to be effective for related problems, in the context of scientific databases curation, it falls short. We present Virk, an AL system that, while being trained, simultaneously learns a classification model and identifies documents having information of interest for a knowledge base. Our approach uses a support vector machine (SVM) classifier with input features derived from neuroscience-related publications from the primary literature. Using our approach, we were able to increase the size of the Neuron Registry, a knowledge base of neuron-related information, by a factor of 90%, a knowledge base of neuron-related information, in 3 months. Using standard biocuration methods, it would have taken between 1 and 2 years to make the same number of contributions to the Neuron Registry. Here, we describe the system pipeline in detail, and evaluate its performance against other approaches to sampling in AL.

CDAC--development of a curated database of anticancer compounds from marine resources.

Anticancer compounds from marine source find application in cancer treatment. Several such compounds have been identified and documented. Here, we describe the development of CDAC, a curated database on anticancer compounds from marine sources.