MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins.
BACKGROUND
Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains.


RESULTS
We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, β, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues.


BACKGROUND
http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada.
Fatemeh Miri
FM
Disfani
Wei-Lun
WL
Hsu
Marcin J
MJ
Mizianty
Christopher J
CJ
Oldfield
Bin
B
Xue
A Keith
AK
Dunker
Vladimir N
VN
Uversky
Lukasz
L
Kurgan
D000596:Amino Acids; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D058977:Molecular Sequence Annotation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis*; D016415:Sequence Alignment; D060388:Support Vector Machine
D000596:Amino Acids; D011506:Proteins