A. Keith Dunker School of Medicine, Biochemistry & Molecular Biology

Empty picture place holder

A. Keith Dunker


Scopus Publication Detail

The publication detail shows the title, authors (with indicators showing other profiled authors), information on the publishing organization, abstract and a link to the article in Scopus. This abstract is what is used to create the fingerprint of the publication.

SPINE-D: Accurate prediction of short and long disordered regions by a single neural-network based method

Tuo Zhang; Eshel Faraggi; Bin Xue; A. Keith Dunker; Vladimir N. Uversky; Yaoqi Zhou

(Profiled Author: A. Keith Dunker)

Journal of Biomolecular Structure and Dynamics.‚ÄČ2012;29(4):799-813.


Short and long disordered regions of proteins have different preference for different amino acid residues. Different methods often have to be trained to predict them separately. In this study, we developed a single neural-network-based technique called SPINE-D that makes a three-state prediction first (ordered residues and disordered residues in short and long disordered regions) and reduces it into a two-state prediction afterwards. SPINE-D was tested on various sets composed of different combinations of Disprot annotated proteins and proteins directly from the PDB annotated for disorder by missing coordinates in X-ray determined structures. While disorder annotations are different according to Disprot and X-ray approaches, SPINE-D's prediction accuracy and ability to predict disorder are relatively independent of how the method was trained and what type of annotation was employed but strongly depend on the balance in the relative populations of ordered and disordered residues in short and long disordered regions in the test set. With greater than 85% overall specificity for detecting residues in both short and long disordered regions, the residues in long disordered regions are easier to predict at 81% sensitivity in a balanced test dataset with 56.5% ordered residues but more challenging (at 65% sensitivity) in a test dataset with 90% ordered residues. Compared to eleven other methods, SPINE-D yields the highest area under the curve (AUC), the highest Mathews correlation coefficient for residue-based prediction, and the lowest mean square error in predicting disorder contents of proteins for an independent test set with 329 proteins. In particular, SPINE-D is comparable to a meta predictor in predicting disordered residues in long disordered regions and superior in short disordered regions. SPINE-D participated in CASP 9 blind prediction and is one of the top servers according to the official ranking. In addition, SPINE-D was examined for prediction of functional molecular recognition motifs in several case studies. The server and databases are available at http://sparks.informatics.iupui.edu/. ©Adenine Press (2012).

PMID: 22208280     PMCID: PMC3297974    

Scientific Context

Context is not available for this document. This can mean several things, this document may not have enough text to produce a Fingerprint, or there are no Similar Experts and publications to display.