MOTIVATION: Intrinsically disordered proteins lack stable 3-dimensional 
structure and play a crucial role in performing various biological functions. 
Key to their biological function are the molecular recognition features (MoRFs) 
located within long disordered regions. Computationally identifying these MoRFs 
from disordered protein sequences is a challenging task. In this study, we 
present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein 
sequences. OPAL utilizes two independent sources of information computed using 
different component predictors. The scores are processed and combined using 
common averaging method. The first score is computed using a component MoRF 
predictor which utilizes composition and sequence similarity of MoRF and 
non-MoRF regions to detect MoRFs. The second score is calculated using 
half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone 
angle information of the disordered protein sequence, using information from the 
amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and 
non-MoRF residues.
RESULTS: OPAL is evaluated using test sets that were previously used to evaluate 
MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate 
that OPAL outperforms all the available MoRF predictors and is the most accurate 
predictor available for MoRF prediction. It is available at 
http://www.alok-ai-lab.com/tools/opal/.
CONTACT: ashwini@hgc.jp or alok.sharma@griffith.edu.au.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.