Grantgeorge6721

chemi.muni.cz/soluprot/.

https//loschmidt.chemi.muni.cz/soluprot/.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

RNA molecules become attractive small-molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites.

We developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure's flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.

http//yanglab.nankai.edu.cn/RNAsite.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Both the lack or limitation of experimental data of transcription factor binding sites (TFBS) in plants and the independent evolutions of plant TFs make computational approaches for identifying plant TFBSs lagging behind the relevant human researches. Observing that TFs are highly conserved among plant species, here we first employ the deep convolutional neural network (DeepCNN) to build 265 Arabidopsis TFBS prediction models based on available DAP-seq (DNA affinity purification sequencing) datasets, and then transfer them into homologous TFs in other plants.

DeepCNN not only achieves greater successes on Arabidopsis TFBS predictions when compared with gkm-SVM and MEME, but also has learned its known motif for most Arabidopsis TFs as well as cooperative TF motifs with PPI (protein-protein-interaction) evidences as its biological interpretability. Under the idea of transfer learning, trans-species prediction performances on ten TFs of other three plants of Oryza sativa, Zea mays and Glycine max demonstrate the feasibility of current strategy.

The trained 265 Arabidopsis TFBS prediction models were packaged in a Docker image named TSPTFBS, which is freely available on DockerHub at https//hub.docker.com/r/vanadiummm/tsptfbs. Source code and documentation are available on GitHub at https//github.com/liulifenyf/TSPTFBS.

The trained 265 Arabidopsis TFBS prediction models were packaged in a Docker image named TSPTFBS, which is freely available on DockerHub at https//hub.docker.com/r/vanadiummm/tsptfbs. Source code and documentation are available on GitHub at https//github.com/liulifenyf/TSPTFBS.The metabolic and signaling functions of lysosomes depend on their intracellular positioning and trafficking, but the underlying mechanisms are little understood. Here, we have discovered a novel septin GTPase-based mechanism for retrograde lysosome transport. We found that septin 9 (SEPT9) associates with lysosomes, promoting the perinuclear localization of lysosomes in a Rab7-independent manner. SEPT9 targeting to mitochondria and peroxisomes is sufficient to recruit dynein and cause perinuclear clustering. read more We show that SEPT9 interacts with both dynein and dynactin through its GTPase domain and N-terminal extension, respectively. Strikingly, SEPT9 associates preferentially with the dynein intermediate chain (DIC) in its GDP-bound state, which favors dimerization and assembly into septin multimers. In response to oxidative cell stress induced by arsenite, SEPT9 localization to lysosomes is enhanced, promoting the perinuclear clustering of lysosomes. We posit that septins function as GDP-activated scaffolds for the cooperative assembly of dynein-dynactin, providing an alternative mechanism of retrograde lysosome transport at steady state and during cellular adaptation to stress.Protein micropatterning allows proteins to be precisely deposited onto a substrate of choice and is now routinely used in cell biology and in vitro reconstitution. However, drawbacks of current technology are that micropatterning efficiency can be variable between proteins and that proteins may lose activity on the micropatterns. Here, we describe a general method to enable micropatterning of virtually any protein at high specificity and homogeneity while maintaining its activity. Our method is based on an anchor that micropatterns well, fibrinogen, which we functionalized to bind to common purification tags. This enhances micropatterning on various substrates, facilitates multiplexed micropatterning, and dramatically improves the on-pattern activity of fragile proteins like molecular motors. Furthermore, it enhances the micropatterning of hard-to-micropattern cells. Last, this method enables subcellular micropatterning, whereby complex micropatterns simultaneously control cell shape and the distribution of transmembrane receptors within that cell. Altogether, these results open new avenues for cell biology.

International literature suggests that disadvantaged groups are at higher risk of morbidity and mortality from SARS-CoV-2 infection due to poorer living/working conditions and barriers to healthcare access. Yet, to date, there is no evidence of this disproportionate impact on non-national individuals, including economic migrants, short-term travellers and refugees.

We analyzed data from the Italian surveillance system of all COVID-19 laboratory-confirmed cases tested positive from the beginning of the outbreak (20th of February) to the 19th of July 2020. We used multilevel negative-binomial regression models to compare the case fatality and the rate of admission to hospital and intensive care unit (ICU) between Italian and non-Italian nationals. The analysis was adjusted for differences in demographic characteristics, pre-existing comorbidities, and period of diagnosis.

We analyzed 213180 COVID-19 cases, including 15974 (7.5%) non-Italian nationals. We found that, compared to Italian cases, non-Italian cases were diagnosed at a later date and were more likely to be hospitalized [adjusted rate ratio (ARR)=1.