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https://www.selleckchem.com/products/AZD0530.html Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA. The key elements of CopulaNet include (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures.During the COVID-19 pandemic, semi-structured interviews were undertaken with 20 adults awaiting a diagnosis for their chronic breathlessness. Three key themes were identified using thematic analysis (1) de-prioritisation of diagnosis, (2) following UK 'lockdown' guidance for the general population but patients fearful they were more at risk, and (3) the impact of lockdown on coping strategies for managing breathlessness. The existing unpredictable pathway to diagnosis for those with chronic breathlessness has been further interrupted during the COVID-19 pandemic.Molecular profiling of circulating extracellular vesicles (EVs) provides a promising noninvasive means to diagnose, monitor, and predict the course of metastatic breast cancer (MBC). However, the analysis of EV protein markers has been confounde

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