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Creators/Authors contains: "Ahmed, A."

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  4. Abstract Proteins are tiny players involved in the activation and deactivation of multiple signaling cascades through interactions in cells. The TNFR1 and MADD interact with each other and mediate downstream protein signaling pathways which cause neuronal cell death and Alzheimer’s disease. In the current study, a molecular docking approach was employed to explore the interactive behavior of TNFR1 and MADD proteins and their role in the activation of downstream signaling pathways. The computational sequential and structural conformational results revealed that Asp400, Arg58, Arg59 were common residues of TNFR1 and MADD which are involved in the activation of downstream signaling pathways.more »Aspartic acid in negatively charged residues is involved in the biosynthesis of protein. However, arginine is a positively charged residue with the potential to interact with oppositely charged amino acids. Furthermore, our molecular dynamic simulation results also ensured the stability of the backbone of TNFR1 and MADD death domains (DDs) in binding interactions. This DDs interaction mediates some conformational changes in TNFR1 which leads to the activation of mediators proteins in the cellular signaling pathways. Taken together, a better understanding of TNFR1 and MADD receptors and their activated signaling cascade may help treat Alzheimer’s disease. The death domains of TNFR1 and MADD could be used as a novel pharmacological target for the treatment of Alzheimer’s disease by inhibiting the MAPK pathway.« less
    Free, publicly-accessible full text available December 1, 2022
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  9. Abstract Background Viruses are a significant player in many biosphere and human ecosystems, but most signals remain “hidden” in metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, and insufficiently advanced identification tools. Results Here, we introduce VirSorter2, a DNA and RNA virus identification tool that leverages genome-informed database advances across a collection of customized automatic classifiers to improve the accuracy and range of virus sequence detection. When benchmarked against genomes from both isolated and uncultivated viruses, VirSorter2 uniquely performed consistently with high accuracy (F1-score > 0.8) across viral diversity, while all other tools under-detected viruses outsidemore »of the group most represented in reference databases (i.e., those in the order Caudovirales ). Among the tools evaluated, VirSorter2 was also uniquely able to minimize errors associated with atypical cellular sequences including eukaryotic genomes and plasmids. Finally, as the virosphere exploration unravels novel viral sequences, VirSorter2’s modular design makes it inherently able to expand to new types of viruses via the design of new classifiers to maintain maximal sensitivity and specificity. Conclusion With multi-classifier and modular design, VirSorter2 demonstrates higher overall accuracy across major viral groups and will advance our knowledge of virus evolution, diversity, and virus-microbe interaction in various ecosystems. Source code of VirSorter2 is freely available ( https://bitbucket.org/MAVERICLab/virsorter2 ), and VirSorter2 is also available both on bioconda and as an iVirus app on CyVerse ( https://de.cyverse.org/de ).« less
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