Accessibility navigation

The DISOclust Protein Disorder Prediction Method

NOTE: This server is no longer maintained, please use the latest version of IntFOLD to obtain DISOclust predictions.

About the server

The DISOclust method provides predictions of protein disorder based on the analysis of 3D structural models using ModFOLDclust. The DISOclust results are combined with those from an in-house version of DISOPRED in order to improve predictions.

Figure 1The DISOclust predicted disorder is mapped onto an ensemble of NMR models for CASP target T0460 (PDB code 2k4n). Click on the image to see an animation showing individual NMR models. The residues predicted to be disordered are coloured red; blue residues are predicted to be ordered. The image was rendered using Pymol (

The DISOclust Server v 1.1

Download DISOclust

Obtain DISOclust predictions via the ModFOLD server (use your own models)


  • The latest DISOclust version is now integrated with the IntFOLD server.
  • Jan 2009: Version 1.1 of the DISOclust server is now online. Submit your target sequence and receive intuitive graphical results.
  • Dec 2008: DISOclust is one of the top performing methods at CASP8
  • March 2008: Prototype DISOclust server ready for testing at CASP8.


Please cite the following paper when refering to the original description of the DISOclust method:
  • McGuffin, L. J. (2008) Intrinsic disorder prediction from the analysis of multiple protein fold recognition models. Bioinformatics, 24, 1798-804. PubMed
The DISOclust method is also integrated into the ModFOLD server, but you need to provide your own models. Please cite this paper if you have used the ModFOLD server:
  • McGuffin, L. J. (2008) The ModFOLD Server for the Quality Assessment of Protein Structural Models. Bioinformatics, 24, 586-587. PubMed
The DISOclust server also makes use of the downloadable version of the DISOPRED method:
  • Ward, J. J., Sodhi, J. S., McGuffin, L. J., Buxton, B. F. & Jones, D. T. (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J. Mol. Biol., 337, 635-645. PubMed


Tel: 0118 378 6332

Email: l.j.mcguffin

Page navigation


Search Form