The performance of the newest version of IUPred has been tested using customized datasets. Intrinsically disordered protein regions (IDRs) were taken from the DisProt database as the positive testing dataset. Only IDRs with a length of at least 9 residues were included.

The negative dataset comprises protein regions that are known to represent independently folding, stable monomeric units encompassing a single domain according to CATH definitions. These structures were collected from the PDB and were filtered to include only one protein region from each UniRef90 sequence cluster. Structures with flexible residues, as evidenced by highly dissimilar NMR models or missing X-ray coordinates, were removed.

The positive and negative datasets used for benchmarking can be downloaded from here and here, respectively.

Using the above bechmark sets, IUPred2 can be characterized with the following binary classifier measures:

*as the value of precision depends heavily on the relative sizes of the positive and negative datasets, the database sizes were scaled to be equal to achieve an unbiased measure

The performance of ANCHOR2 was tested on the recently published DIBS database, as the positive testing dataset. DIBS represens the largest currently available set of experimentally verified IDRs capable of forming ordered structures upon binding to protein domains. Only entries not used in the training of ANCHOR2 were used in testing.

For negative testing, the same monomeric single-domain protein dataset was used as for testing IUPred2, but allowing for structures with up to 20% of flexible residues. Only entries not used in the training of ANCHOR2 were used in testing. Furthemore, ANCHOR2 was also evaluated on a set of flexible linkers that are disordered but are known to lack a primary binding function.

To get a fuller picture about the efficiency of ANCHOR2 on sequence sets with different compositions, two auxiliary datasets were also considered. The first is composed of disordered regions from DisProt, the next is a collection of random (decoy) segments from the human proteome excluding transmembrane regions, structured Pfam domains and extracellular proteins. Both datasets are expected to contain disordered binding regions, albeit to a significantly lower extent, compared to DIBS.

The positive, negative, and auxiliary datasets used for benchmarking can be downloaded from here.

Using the above bechmark sets, ANCHOR2 can be characterized with the following binary classifier measures. As ANCHOR often specifically identifies only strongly binding sub-regions inside larger binding regions, segment-based sensitivity was also calculated. In this case a binding region was considered found if it incorporates at least one ANCHOR-identified region, regardless of possible difference in length:

As currently there are no comprehensive datasets collecting a large number of experimentally verified examples for other types of context-dependent IDRs targeted by IUPred2A, the rigorous testing of these features are not possible as of yet. In accord, these features are marked as ‘Experimental’. However, a number of select examples are available in the How to use section.