Frontiers in chemistry
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Frontiers in molecular biosciences
Liquid-Liquid Phase Separation of TDP-43 and FUS in Physiology and Pathology of Neurodegenerative Diseases
Journal of chemical theory and computation
Modeling the Structure and Interactions of Intrinsically Disordered Peptides with Multiple Replica, Metadynamics-Based Sampling Methods and Force-Field Combinations
bioRxiv
Structured and disordered regions of Ataxin-2 contribute differently to the specificity and efficiency of mRNP granule formation
bioRxiv
Unique structural features govern the activity of a human mitochondrial AAA+ disaggregase, Skd3
Journal of Controlled Release
Liquid-cell transmission electron microscopy for imaging of thermosensitive recombinant polymers
Nicolas Locker on Friends or Foes? The Many Routes Caliciviruses Use to Manipulate RNA Granules
Wednesday, April 13, 2022
Science advances
ATP-responsive biomolecular condensates tune bacterial kinase signaling
Chemical reviews
Intrinsically Disordered Proteins: Critical Components of the Wetware
Methods in molecular biology (Clifton, NJ)
Collective Learnings of Studies of Stress Granule Assembly and Composition
Methods in molecular biology (Clifton, NJ)
Detecting Stress Granules in Drosophila Neurons
Methods in molecular biology (Clifton, NJ)
Monitoring and Quantification of the Dynamics of Stress Granule Components in Living Cells by Fluorescence Decay After Photoactivation
Methods in molecular biology (Clifton, NJ)
Monitoring Virus-Induced Stress Granule Dynamics Using Long-Term Live-Cell Imaging
Methods in molecular biology (Clifton, NJ)
Single-Molecule Imaging of mRNA Interactions with Stress Granules
Methods in molecular biology (Clifton, NJ)
Image-Based Screening for Stress Granule Regulators
Methods in molecular biology (Clifton, NJ)
APEX Proximity Labeling of Stress Granule Proteins
The journal of physical chemistry B
Effects of Cosolvents and Crowding Agents on the Stability and Phase Transition Kinetics of the SynGAP/PSD-95 Condensate Model of Postsynaptic Densities
BMC bioinformatics
Prediction of liquid-liquid phase separating proteins using machine learning
Jill Bouchard
Editor in Chief, Condensates.com
Here's a new phase separation predictor based on machine learning from the LLPSDB. I’ve saved it in our library of Resources–check it out here: https://condensates.com/resources/pspredictor/
Nature Chemistry
Charge-density reduction promotes ribozyme activity in RNA-peptide coacervates via RNA fluidization and magnesium partitioning
Trends in Cell Biology