ParSe: predict phase-separating protein regions from the primary sequence

This is a form that allows you to enter a sequence and predict the regions in a protein that are disordered, and which subset of those regions can undergo liquid-liquid phase separation (LLPS).


Proteins that undergo LLPS have been implicated in the formation of membraneless organelles and are of interest because of their role in regulating the formation of P bodies, the nucleolus, and stress granules. LLPS in intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDRs) is thought to be mediated by many weak, multi-valent, intermolecular interactions.

This algorithm explores the possibility that LLPS can be predicted using two factors. First, the strength of interactions between the protein and solvent, as estimated by a model of the polymer scaling exponent (νmodel). Second, the propensity for a sequence to form β-turns. Using these factors, any protein sequence can be parsed into one of three categories:

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  1. Paiz, E.A., Allen, J.H., Correia, J.J., Fitzkee, N.C., Hough, L.E., Whitten, S.T. “Beta turn propensity and a model polymer scaling exponent identify intrinsically disordered phase-separating proteins” J. Biol. Chem. 297, 101343 (2021).

Primary Sequence

Maximum sequence length that can be analyzed is 10,000 residues; minimum length is 25.

Sequence length: 
Whole sequence νmodel: 
Whole sequence β-turn propensity: 
Whole sequence rmodel: 

ParSe Results

Protein Regions

Identified regions have 20 or more contiguous residues that are at least 90% of only one label: P, D, or F.

Residue-level Information