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The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and chemoinformatics. This makes it a valuable tool for both academic research and practical applications in bee conservation. [ABSTRACT FROM AUTHOR]

Abstract Background Advances in high-throughput technologies available to modern biology have created an increasing flood of experimentally determined facts. Ordering, managing and describing these raw results is the first step which allows facts to become knowledge. Currently there are limited ways to automatically annotate such data, especially utilizing information deposited in published literature. Results To aid researchers in describing results from high-throughput experiments we developed HT-SAS, a web service for automatic annotation of proteins using general English words. For each protein a poll of Medline abstracts connected to homologous proteins is gathered using the UniProt-Medline link. Overrepresented words are detected using binomial statistics approximation. We tested our automatic approach with a protein test set from SGD to determine the accuracy and usefulness of our approach. We also applied the automatic annotation service to improve annotations of proteins from Plasmodium bergei expressed exclusively during the blood stage. Conclusion Using HT-SAS we created new, or enriched already established annotations for over 20% of proteins from Plasmodium bergei expressed in the blood stage, deposited in PlasmoDB. Our tests show this approach to information extraction provides highly specific keywords, often also when the number of abstracts is limited. Our service should be useful for manual curators, as a complement to manually curated information sources and for researchers working with protein datasets, especially from poorly characterized organisms.

Motivation Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein–ligand interactions. Results Here, we present a Protein–Ligand Extended Connectivity (PLEC) FP that implicitly encodes protein–ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC FPs were used to construct different machine learning models tailored for predicting protein–ligand affinities (pKi∕ d ). Even the simplest linear model built on the PLEC FP achieved Rp  = 0.817 on the Protein Databank (PDB) bind v2016 'core set', demonstrating its descriptive power. Availability and implementation The PLEC FP has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to 'learn' to extract features that are relevant for the task at hand. Results We have developed a novel deep neural network estimating the binding affinity of ligand–receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF-2013 'scoring power' benchmark and Astex Diverse Set and outperformed classical scoring functions. Availability and implementation The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Comparison of smallmolecules is a common component of many cheminformatics workflows, including the design of new compounds and libraries as well as side-effect predictions and drug repurposing. Currently, large-scale comparison methods rely mostly on simple fingerprint representation of molecules, which take into account the structural similarities of compounds. Methods that utilize 3D information depend on multiple conformer generation steps, which are computationally expensive and can greatly influence their results. The aim of this study was to augment molecule representation with spatial and physicochemical properties while simultaneously avoiding conformer generation. To achieve this goal, we describe a molecule as an undirected graph in which the nodes correspond to atoms with pharmacophoric properties and the edges of the graph represent the distances between features. This approach combines the benefits of a conformation-free representation of a molecule with additional spatial information. We implemented our approach as an open-source Python module called DeCAF (Discrimination, Comparison, Alignment tool for 2D PHarmacophores), freely available at http://bitbucket.org/marta-sd/decaf. We show DeCAF's strengths and weaknesses with usage examples and thorough statistical evaluation. Additionally, we show that our method can be manually tweaked to further improve the results for specific tasks. The full dataset on which DeCAF was evaluated and all scripts used to calculate and analyze the results are also provided. [ABSTRACT FROM AUTHOR]

Background: Advances in high-throughput technologies available to modern biology have created an increasing flood of experimentally determined facts. Ordering, managing and describing these raw results is the first step which allows facts to become knowledge. Currently there are limited ways to automatically annotate such data, especially utilizing information deposited in published literature. Results: To aid researchers in describing results from high-throughput experiments we developed HT-SAS, a web service for automatic annotation of proteins using general English words. For each protein a poll of Medline abstracts connected to homologous proteins is gathered using the UniProt-Medline link. Overrepresented words are detected using binomial statistics approximation. We tested our automatic approach with a protein test set from SGD to determine the accuracy and usefulness of our approach. We also applied the automatic annotation service to improve annotations of proteins from Plasmodium bergei expressed exclusively during the blood stage. Conclusion: Using HT-SAS we created new, or enriched already established annotations for over 20% of proteins from Plasmodium bergei expressed in the blood stage, deposited in PlasmoDB. Our tests show this approach to information extraction provides highly specific keywords, often also when the number of abstracts is limited. Our service should be useful for manual curators, as a complement to manually curated information sources and for researchers working with protein datasets, especially from poorly characterized organisms. [ABSTRACT FROM AUTHOR]

In recent years, commensal bacteria colonizing the human body have been recognized as important determinants of health and multiple pathologic conditions. Among the most extensively studied commensal bacteria are the gut microbiota, which perform a plethora of functions, including the synthesis of bioactive products, metabolism of dietary compounds, and immunomodulation, both through attenuation and immunostimulation. An imbalance in the microbiota population, i.e., dysbiosis, has been linked to many human pathologies, including various cancer types and neurodegenerative diseases. Targeting gut microbiota and microbiome–host interactions resulting from probiotics, prebiotics, and postbiotics is a growing opportunity for the effective treatment of various diseases. As more research is being conducted, the microbiome field is shifting from simple descriptive analysis of commensal compositions to more molecular, cellular, and functional studies. Insight into these mechanisms is of paramount importance for understanding and modulating the effects that microbiota, probiotics, and their derivatives exert on host health. [ABSTRACT FROM AUTHOR]

Tumor necrosis factor receptor-associated periodic syndrome (TRAPS) belongs to systemic autoinflammatory diseases (AIDs). Many of these syndromes are genetically conditioned and can be inherited. Diagnosis relies on clinical symptoms and should be confirmed by genetic testing. One of the most serious complications is AA amyloidosis. We present the diagnostic route of a 33-year-old male with AA amyloidosis and his children, leading to diagnosis of monogenic autoinflammatory syndrome, confirmed by genetic analysis. A novel variant of the in-frame insertion type in one allele of TNFRSF1A gene was found by whole exome sequencing and confirmed by Sanger sequencing, which allowed a diagnosis of TRAPS. Three-dimensional modeling was used to assess the structural changes introduced into TNFR1 molecule by the insertion. The analysis of the 3D model revealed that accommodation of the 4AA insert induces misalignment of three cysteine bridges (especially the C70-C96 bridge) in the extracellular domain, leading to putatively misfolded and improperly functioning TNFR1. Three of the patient's daughters inherited the same variant of the TNFRSF1A gene and presented TRAPS symptoms. TRAPS is a very rare disease, but in the presence of suggestive symptoms the genetic diagnostic workout should be undertaken. Early diagnosis followed by appropriate clinical management can prevent irreversible complications. [ABSTRACT FROM AUTHOR]

Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to build RF-Score, a scoring function utilizing the machine-learning technique known as Random Forest (RF). We also point out how to use different data, features, and regression models using either R or Python programming languages.

Small-molecule inhibitors of DNA methyltransferases such as RG108 represent promising candidates for cancer drug development. We report the synthesis and in vitro analysis of a biotinylated RG108 conjugate, 2-(1,3-dioxo-1,3-dihydro-isoindol-2-yl)-3-(5-[3-[5-(2-oxo-hexahydro-thieno[3,4-d]imidazol-4-yl)pentanoylamino]propoxy]-1H-indol-3-yl)propionic acid (bio-RG108), for the evaluation of interactions with DNA methyltransferase enzymes. The structural design of the chemically modified inhibitor was aided by molecular modeling, which suggested the possibility for extensive chemical modifications at the 5-position of the tryptophan moiety in RG108. The inhibitory activity of the corresponding derivative was confirmed in a cell-free biochemical assay, where bio-RG108 showed an undiminished inhibition of DNA methyltransferase activity (IC50 = 40 nM). Bio-RG108 therefore represents a suitable bioconjugate for the elucidation of inhibitory mechanisms and for the affinity purification of RG108-associated proteins.

DNA methyltransferases are promising targets for cancer therapy. In many cancer cells promoters of tumor suppressor genes are hypermethylated, which results in gene inactivation. It has been shown that DNA methyltransferase inhibitors can suppress tumor growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. To discover novel compounds for the inhibition of human DNA methyltransferases, we have screened a set of small molecules available from the NCI database. Using a 3-dimensional model of the human DNA methyltransferase 1 and a modified docking and scoring procedure, we have identified a small list of molecules with high affinities for the active site of the enzyme. The two highest scoring structures were found to inhibit DNA methyltransferase activity in vitro and in vivo. The newly discovered inhibitors validate our screening procedure and also provide a useful basis for further rational drug development.

DNA methylation is an epigenetic process affecting gene expression and chromatin organization. It can heritably silence or activate transcription of genes without any change in their nucleotide sequences, and for a long time was not recognized as an important regulatory mechanism. However, during the recent years it has been shown that improper methylation, especially hypermethylation of promoter regions, is observed in nearly all steps of tumorigenesis. Aberrant methylation is also the cause of several major pathologies including developmental disorders involving chromosome instabilities and mental retardation. A great progress has been made in our understanding of the enzymatic machinery involved in establishing and maintaining methylation patterns. This allowed for the development of new diagnostic tools and epigenetic treatment therapies. The new approaches hold a great potential; several inhibitors of DNA methyltransferases have already shown very promising therapeutic effects.

DNA methylation regulates gene expression in normal and malignant cells. The possibility to reactivate epigenetically silenced genes has generated considerable interest in the development of DNA methyltransferase inhibitors. Here, we provide a detailed characterization of RG108, a novel small molecule that effectively blocked DNA methyltransferases in vitro and did not cause covalent enzyme trapping in human cell lines. Incubation of cells with low micromolar concentrations of the compound resulted in significant demethylation of genomic DNA without any detectable toxicity. Intriguingly, RG108 caused demethylation and reactivation of tumor suppressor genes, but it did not affect the methylation of centromeric satellite sequences. These results establish RG108 as a DNA methyltransferase inhibitor with fundamentally novel characteristics that will be particularly useful for the experimental modulation of epigenetic gene regulation.