Not only is this a really cool method at the intersection of proteomics and imaging, the database if full of amazing pictures (including certain condensates)! I’ve saved it in our library of Resources–check it out here: https://condensates.com/resources/opencell/

BioRxiv

Single-particle cryo-EM analysis of the shell architecture and internal organization of an intact α-carboxysome

BMC bioinformatics

Prediction of liquid-liquid phase separating proteins using machine learning

bioRxiv

Phase separation of tunable biomolecular condensates predicted by an interacting particle model

bioRxiv

Particle flow modulates growth dynamics and nanoscale-arrested growth of transcription factor condensates in living cells

BioRxiv

Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

bioRxiv

Sequence based prediction of protein phase separation into disordered condensates using machine learning

Briefings in bioinformatics

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning

bioRxiv

OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization

Nucleic acids research

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

Jill Bouchard

Editor in Chief, Condensates.com

If you are one of the many condensate scientists that uses DisProt, it might be time to check out the latest updates coming in 2022!

Genetics

The length scale of multivalent interactions is evolutionarily conserved in fungal and vertebrate phase-separating proteins

BioRxiv