Genesilico metadisorder service - prediction of intrinsically unstructured proteins
Method overview
Submit sequence
Paper about "MetaDisorder"
Gallery of IUPs
List of protein disorder predictors

Prediction of Intrinsically Unstructured Proteins (protein disorder) from amino acid sequence only.

Intrinsically unstructured proteins (IUPs) known also as unfolded proteins or disordered proteins are proteins characterized by lack of stable tertiary structure under physiological conditions. Moreover, such unfolded proteins remain functional. The intrinsic flexibility of disorder allows to form multiple interactions with various proteins by the same disordered protein. The prediction of protein disorder is important as IUPs are connected with many diseases (many key oncogens have large unstructured regions, e.g. p53 and BRCA1).

Metadisorder is one of the best predictors of protein disorder (evaluated during independent tests - CASP8 and CASP9)

Before running the method (it is quite expensive in case of time and resources) you may want to see exemplary graphical output or text output.

Your email address:  

Protein sequence (without header line):


  • The maximal sequence length is set to 1000 amino acids.
  • Input should be in plain text.
  • All non-amino acid characters will be removed from the sequence. 
  • Input can be upper or lower case.
  • Input should be in on letter amino acid code.
  • Link to results will be sent via email. Alternativelly, you can bookmark result page, it will be automatically refreshed.
Metadisorder is an online tool for prediction of protein disorder. It is a meta method which means that it tries to calculate "consensus" from results returned by other methods.

Metadisorder web service consists of four parts:
  1. metadisorder (the weighted consensus is build using 13 primary disorder methods: DisEMBL (3 versions), DISOPRED2, DISpro, Globplot, iPDA, IUPred (2 versions), Pdisorder, Poodle-s, Poodle-l, PrDOS, Spritz (2 versions), and RONN), this component was tested during CASP8 on which it proves to be the best method.
  2. metadisorder3d (this component uses fold recognition methods (PSI-BLAST, FFAS, HHsearch, Phyre, Pcons, MGenThreader) to find similar sequences. Genetic algorithm is used to infer protein disorder using gaps in the alignments.
  3. metadisordermd (combines two previous components into new meta method. Again, genetic algorithm is used to optimize components integration.
  4. metadisordermd2 (variant of metadisordermd for which different scoring function was used during genetic algorithm optimization step).

Contact: Lukasz Kozlowski

If you find MetaDisorder useful, please consider citing the reference that describes this work:
Kozlowski LP, Bujnicki JM. MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics. 2012 May 24;13(1):111.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan)

This work was supported by Polish Ministry of Science and Higher Education (grant NN301 190139).

Valid HTML 4.01!