Disordered region detection software tools | Protein structure data analysis
Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.
Provides a suite of methods important for the prediction of protein structural and functional features. predictProtein is a web server that incorporates over 30 tools. This software searches up-to-date public sequence databases, creates alignments, and predicts aspects of protein structure and function. It can help when little is known about the protein in question. For medium-to-high throughput analyses, downloadable software packages and the PredictProtein Machine Image (PPMI) are available.
Allows users to submit a protein sequence, and returns a probability estimate of each residue in the sequence being disordered. Predicted intrinsically disordered regions (IDRs) are annotated as protein binding through a novel SVM-based classifier, which uses profile data and additional sequence-derived features.
A web app to predict protein disorder. Metadisorder is a meta method which means that it tries to calculate "consensus" from results returned by other methods, consisting in 4 parts: matedisorder, metadirsorder3d (to fold recognition methods), metadisordermf (optimize components integration using a combination of the first two tools) and metadisordermd2 (metadisordermd with different scoring function). Metadisorder is one of the best predictors of protein disorder, evaluated during independent tests (CASP8 and CASP9).
Two predictors to solve the length-dependency problem in prediction of intrinsic protein disorder, i.e. that the amino acid compositions and sequence properties may vary among disordered regions of different lengths. The VSL predictors achieved well-balanced accuracies on both short and long disordered regions and were significantly more accurate than several previous intrinsic protein disorder predictors.