Protein Disorder

MD (Meta-Disorder predictor) is a neural-network based meta-predictor that uses different sources of information predominantly obtained from orthogonal approaches. MD significantly outperformed its constituents, and compared favorably to other top prediction methods. MD is capable of predicting disordered regions of all "flavors", and identifying new ones that are not captured by other predictors.

NORSnet is a neural network based method that focuses on the identification of unstructured loops. NORSnet was trained to distinguish between very long contiguous segments with non-regular secondary structure (NORS regions) and well-folded proteins. NORSnet was trained on predicted information rather than on experimental data. Therefore, it was optimized on a large data, which is not biased by today's experimental means of capturing disorder. Thus, NORSnet reached into regions in sequence space that are not covered by the specialized disorder predictors. One disadvantage of this approach is that it is not optimal for the identification of the "average" disordered region.

Ucon (prediction of natively unstructured regions through contacts) is a method that combines protein-specific internal contacts with generic pairwise energy potentials to accurately predict long and functional unstructured regions. One advantage of Ucon over statistical-potential based methods is that it incorporates the contribution of the specific order of the amino-acids rather than the amino acid composition alone.

PROFbval is a neural-network method that aimed at predicting flexible and rigid residues in proteins from sequence alone. PROFbval was trained on B-factor data from PDB- Xray structures and, to an extent, can capture disordered residues. Additionally, surface residues that are predicted to be rigid by PROFbval are correlated with the location of enzyme active sites.