Phaseek: A Generalizable Predictor of Liquid–Liquid Phase Separation from Protein Sequence

This dataset provides predicted liquid–liquid phase separation (LLPS) propensity scores across the full proteomes of 18 widely studied model organisms. The predictions were generated using Phaseek, our deep learning model developed to identify sequence features associated with LLPS behavior. For each protein, we include residue-level LLPS scores and highlight key regions predicted to drive phase separation, alongside randomly selected regions for comparison. The dataset also includes physicochemical properties and secondary structure features for both key and random regions, as described in the Phaseek publication. These LLPS-driving segments may correspond to functional elements involved in biomolecular condensate formation. The organisms and their corresponding abbreviations are: Hs – Homo sapiens Mm – Mus musculus Rn – Rattus norvegicus Dm – Drosophila melanogaster Dr – Danio rerio At – Arabidopsis thaliana Sc – Saccharomyces cerevisiae Ce – Caenorhabditis elegans Bt – Bos taurus Mmu – Macaca mulatta Cf – Canis lupus familiaris Ss – Sus scrofa Gg – Gallus gallus Xl – Xenopus laevis EcK12 – Escherichia coli K-12 Pt – Pan troglodytes Ag – Anopheles gambiae Pf – Plasmodium falciparum This dataset was generated using the Phaseek predictor and supports the findings presented in our publication:Generalizable Prediction of Liquid–Liquid Phase Separation from Protein Sequencehttp://dx.doi.org/10.1101/2025.01.27.635039