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Prediction of Intrinsically Unstructured Proteins

Intrinsically disordered proteins (IDPs) have no single well-defined tertiary structure under native conditions. IUPred2A is a combined web interface that allows to identify disordered protein regions using IUPred2 and disordered binding regions using ANCHOR2. IUPred2A is also capable of identifying protein regions that do or do not adopt a stable structure depending on the redox state of their environment. IUPred2A supersedes the previous IUPred and ANCHOR servers. For new features included in IUPred2A, see the New features section.

For a detailed description of how to run IUPred2A using various features and how to interpret the output, see the How to use and Examples sections. For a simple demonstration of how to input data, see the Samples below.

Protein Sequence
Enter SWISS-PROT/TrEMBL identifier or accession number:

or paste the amino acid sequence:

or provide your email address and upload a (multi)FASTA file (max 1MB):
Prediction type:



For a demonstration on how to run IUPred2A with ANCHOR2 binding site prediction on human p53 protein using its UniProt accession
and press Submit.
For a demonstration on how to run IUPred2A with redox state-dependent disorder prediction on the human cytochrome c oxidase copper chaperone using its sequence
and press Submit.


Bálint Mészáros, Gábor Erdős, Zsuzsanna Dosztányi
IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding
Nucleic Acids Research 2018;46(W1):W329-W337.

Zsuzsanna Dosztányi
Prediction of protein disorder based on IUPred
Protein Science 2017;27:331-340.

Dosztányi Z, Csizmók V, Tompa P, Simon I.
The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins.
J Mol Biol. 2005;347:827-39.

Mészáros B, Simon I, Dosztányi Z.
Prediction of protein binding regions in disordered proteins.
PLoS Comput Biol. 2009;5:e1000376.
Zsuzsanna Dosztanyi | Balint Meszaros | Gabor Erdos | MTA-ELTE Momentum Bioinformatics Research Group