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).

Description

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:

Reference

  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). https://doi.org/10.1016/j.jbc.2021.101343

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