Training models employing physico-chemical properties of DNA for protein binding site detection

Transcription Factors (TFs) are one of the most important agents acting on gene expression regulation, fundamentally determining the organized functional operation of cellular machinery. At a molecular level, this effect is achieved by the sequence specific physical binding of TF proteins to particular parts of the DNA. Transcription Factors regulate gene expression in complex ways and the detection of their binding sites is an important part of many experiments. Predicting Transcription Factor Binding Sites (TFBS) from DNA sequence data has been a challenging task in the field of bioinformatics. The abundance of available DNA sequences strongly encourages the use of machine learning for this problem. Until now most of these efforts were primarily based on the traditional nucleotide-based representation of DNA. To elaborate a more detailed description of this macromolecule, we have worked out a new Physico-Chemical Descriptor (PCD) based DNA representation and used it as input for training neural networks to predict TFBSs. We show that the PCD representation is a viable format for deep learning models, and our feature selection investigation highlights the importance of proper PCD subset choices. The distinct prediction efficiencies detected upon the usage of arbitrarily selected feature subsets indicates that the different DNA features affect the DNA binding process of TFs to various extent

Genistein isoflavone glycoconjugates in sour cherry cultivars : Prunus cerasus L.

Although the isoflavone genistein has well-established health-beneficial effects, it is not a major component of Western diet, since soy consumption, the main dietary source of genistein, is low in these populations. Genistein compounds were studied in twelve commercial sour cherry (Prunus cerasus L.) cultivars grown in Hungary. High performance liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry, equipped with electrospray ion source (HPLC-ESI-qTOFMS) was used for screening and confirmatory analyses. Genistin and genistein were found in some Hungarian native sour cherry cultivars including ‘Pipacs1’, ‘Kántorjánosi’, ‘Debreceni bőtermő’ and ‘Éva’. Genistein content in fruits of the latter three cultivars ranged between 0.4 to 0.6 mg, while in ‘Pipacs1’ a total of 4.4 mg genistein compounds (expressed as aglycone equivalents per 100 g of fresh fruit) was determined. These cultivars may play an important role as complementary genistein sources in the Western diet. Especially ‘Pipacs 1’, may be best utilized in functional food products

DNA Readout Viewer (DRV) : Visualisation of Specificity Determining Patterns of Protein-Binding DNA Segments

The sequence specific recognition of DNA by regulatory proteins typically occurs by establishing hydrogen bonds and non-bonded contacts between chemical substructures of nucleotides and amino acids forming the compatible interacting surfaces. The recognition process is also influenced by the physicochemical and conformational character of the target oligonucleotide motif. Although the role of these mechanisms in DNA-protein interactions is well-established, bioinformatical methods rarely address them directly, instead binding specificity is mostly assessed at nucleotide level. DNA Readout Viewer (DRV) aims to provide a novel DNA representation, facilitating in-depth view into these mechanisms by the concurrent visualisation of functional groups and a diverse collection of DNA descriptors. By applying its intuitive representation concept for various DNA recognition related visualisation tasks, DRV can contribute to unravelling the binding specificity factors of DNA-protein interactions.DRV is freely available at https://drv.brc.hu.Supplementary data are available at Bioinformatics online

A COVID-19 hatása a húsipari ellátási lánc egy szűk szegmensében

A 2020-as év legnagyobb kihívásának a koronavírus járvány bizonyult, mely jelentős hatást gyakorolt az FMCG szektorra és a kkv-kra egyaránt. A multinacionális kereskedelmi központokkal szemben a kisvállalkozások jelentős lépéseket kényszerültek tenni, hogy a pandémia időszakában talpon maradhassanak. Az e-kereskedelem szerepének felértékelődése globális nyomást gyakorolt a kkv-kra, hogy ne essenek el a bevételünk nagy részétől. A járványügyi kihívások és korlátozások nem csak a személyes kiszolgálásnál jelentettek akadályt, hanem az áruellátás során is fennakadást okoztak. A tanulmány célja egy nyers és fagyasztott árut forgalmazó családi vállalkozással készített mélyinterjú, és a vevőkörével végzett kérdőíves kutatás keretein belül a húsipari ellátási lánc folytonosságának vizsgálata, valamint a bevételek alakulásának elemzése a koronavírus időszaka alatt. A primer kutatás során egy 47 főből álló bázis válaszainak feldolgozásával, és a mélyinterjú során kapott adatok kiértékelésével fogunk válaszokat adni

Transcription factor binding site detection using convolutional neural networks with a functional group-based data representation

Transcription factors (TFs) play an essential role in molecular biology by regulating gene expression. The binding sites of TFs can vary by a large amount and the numerous possible binding locations make their detection a challenging issue. Recently, several machine learning approaches using nucleotide sequence data were applied to classify DNA sequences regarding Transcription Factor Binding Sites (TFBS). We propose a novel training strategy without the traditional 1D nucleotide-based DNA sequence representation by instead using a 2D topological matrix of sub-nucleotide chemical functional groups substantially defining the protein binding ability of DNA fragments. We train convolutional neural networks using this novel Functional Group DNA Representation (FGDR) to solve a TFBS classification task. We compare our results with the efficiency of previous nucleotide-based training approaches and show that learning from an FGDR data sequence has several benefits regarding TFBS classification. Moreover, we reason that learning deep neural networks from the FGDR representation produces competitive results while only introducing a pre-processing conversion step. Finally, we show that employing an ensemble of models from the nucleotide and FGDR representations for network training results in higher classification performance than any of the single input approaches. © Published under licence by IOP Publishing Ltd

Suboptimal multisensory processing in pediatric migraine without aura: a comparative, cross-sectional study

Alterations of sensory processing in migraine are well known. There is some evidence to suggest that multisensory processing is altered in migraine as well, but the area is underexplored, especially regarding pediatric migraine. A visual and an audiovisual version of the Rutgers Acquired Equivalence Test paradigm was administered to pediatric patients with migraine without aura (aged 7–17.5 years) and to age- and sex-matched controls. The application of audiovisual stimuli significantly facilitated associative pair learning in migraine-free children and adolescents, but not in pediatric migraine patients. The results of this study corroborate the hypothesis that multisensory processing is altered in pediatric migraine without aura

A Penicillium chrysogenum-based expression system for the production of small, cysteine-rich antifungal proteins for structural and functional analyses

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In vitro activity of calcium channel blockers in combination with conventional antifungal agents against clinically important filamentous fungi

Despite the current therapeutic options, filamentous fungal infections are associated with high mortality rate especially in immunocompromised patients. In order to find a new potential therapeutic approach, the in vitro inhibitory effect of two antiarrhythmic agents, diltiazem and verapamil hydrochloride were tested against different clinical isolates of ascomycetous and mucoralean filamentous fungi. The in vitro combinations of these non-antifungal drugs with azole and polyene antifungal agents were also examined. Susceptibility tests were carried out using the broth microdilution method according to the instructions of the Clinical and Laboratory Standards Institute document M38-A2. Checkerboard microdilution assay was used to assess the interactions between antifungal and non-antifungal drugs. Compared to antifungal agents, diltiazem and verapamil hydrochloride exerted a relatively low antifungal activity with high minimal inhibitory concentration values (853–2731 μg/ml). Although in combination they could increase the antifungal activity of amphotericin B, itraconazole and voriconazole. Indifferent and synergistic interactions were registered in 33 and 17 cases, respectively. Antagonistic interactions were not revealed between the investigated compounds. However, the observed high MICs suggest that these agents could not be considered as alternative systemic antifungal agents