MOTIVATION: Intrinsic disorder (ID), i.e. the lack of a unique folded 
conformation at physiological conditions, is a common feature for many proteins, 
which requires specialized biochemical experiments that are not high-throughput. 
Missing X-ray residues from the PDB have been widely used as a proxy for ID when 
developing computational methods. This may lead to a systematic bias, where 
predictors deviate from biologically relevant ID. Large benchmarking sets on 
experimentally validated ID are scarce. Recently, the DisProt database has been 
renewed and expanded to include manually curated ID annotations for several 
hundred new proteins. This provides a large benchmark set which has not yet been 
used for training ID predictors.
RESULTS: Here, we describe the first systematic benchmarking of ID predictors on 
the new DisProt dataset. In contrast to previous assessments based on missing 
X-ray data, this dataset contains mostly long ID regions and a significant 
amount of fully ID proteins. The benchmarking shows that ID predictors work 
quite well on the new dataset, especially for long ID segments. However, a large 
fraction of ID still goes virtually undetected and the ranking of methods is 
different than for PDB data. In particular, many predictors appear to confound 
ID and regions outside X-ray structures. This suggests that the ID prediction 
methods capture different flavors of disorder and can benefit from highly 
accurate curated examples.
AVAILABILITY AND IMPLEMENTATION: The raw data used for the evaluation are 
available from URL: http://www.disprot.org/assessment/.
CONTACT: silvio.tosatto@unipd.it.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.