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

Abstract：

: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.

journal_name

J Biomol Struct Dyn

journal_title

Journal of biomolecular structure & dynamics

authors

Zhang T,Faraggi E,Xue B,Dunker AK,Uversky VN,Zhou Y

doi

10.1080/073911012010525022

subject

Has Abstract

pub_date

2012-01-01 00:00:00

pages

799-813

issue

eissn

0739-1102

issn

1538-0254

pii

c4317/SPINE-D-Accurate-Prediction-of-Short-and-Lon

journal_volume

pub_type

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