Improved disorder prediction by combination of orthogonal approaches.
Disordered proteins are highly abundant in regulatory processes such as transcription and cell-signaling. Different methods have been developed to predict protein disorder often focusing on different types of disordered regions. Here, we present MD, a novel META-Disorder prediction method that molds various sources of information predominantly obtained from orthogonal prediction methods, to significantly improve in performance over its constituents. In sustained cross-validation, MD not only outperforms its origins, but it also compares favorably to other state-of-the-art prediction methods in a variety of tests that we applied.


BACKGROUND
http://www.rostlab.org/services/md/
Schlessinger
Avner
A
CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America. as2067@columbia.edu
Punta
Marco
M
Yachdav
Guy
G
Kajan
Laszlo
L
Rost
Burkhard
B
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
D011506:Proteins