Tudor staphylococcal nuclease is a docking platform for stress granule components and is essential for SnRK1 activation in Arabidopsis

Tudor staphylococcal nuclease (TSN; also known as Tudor-SN, p100, or SND1) is a multifunctional, evolutionarily conserved regulator of gene expression, exhibiting cytoprotective activity in animals and plants and oncogenic activity in mammals. During stress, TSN stably associates with stress granules (SGs), in a poorly understood process. Here, we show that in the model plant Arabidopsis thaliana, TSN is an intrinsically disordered protein (IDP) acting as a scaffold for a large pool of other IDPs, enriched for conserved stress granule components as well as novel or plant-specific SG-localized proteins. While approximately 30% of TSN interactors are recruited to stress granules de novo upon stress perception, 70% form a protein-protein interaction network present before the onset of stress. Finally, we demonstrate that TSN and stress granule formation promote heat-induced activation of the evolutionarily conserved energy-sensing SNF1-related protein kinase 1 (SnRK1), the plant orthologue of mammalian AMP-activated protein kinase (AMPK). Our results establish TSN as a docking platform for stress granule proteins, with an important role in stress signalling

Rapid Prediction and Analysis of Protein Intrinsic Disorder

Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near-perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app

Variability of Accessory Proteins Rules the SARS-CoV-2 Pathogen

The coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which is pandemic with an estimated fatality rate less than 1% is ongoing. SARS-CoV-2 accessory proteins ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 with putative functions to manipulate host immune mechanisms such as interferons, immune signaling receptor NLRP3 (NOD-, LRR-, and pyrin domain-containing 3) inflammasome, inflammatory cytokines such as interleukin 1β (IL-1β) are critical in COVID-19 pathology. Outspread variations of each of the six accessory proteins of all complete proteomes (available as of October 26, 2020, in the National Center for Biotechnology Information depository) of SARS-CoV-2, were observed across six continents. Across all continents, the decreasing order of percentage of unique variations in the accessory proteins was found to be ORF3a\u3eORF8\u3eORF7a\u3eORF6\u3eORF10\u3eORF7b. The highest and lowest unique variations of ORF3a were observed in South America and Oceania, respectively. This finding suggests that the wide variations of accessory proteins seem to govern the pathogenicity of SARS-CoV-2, and consequently, certain propositions and recommendations can be made in the public interest