MOTIVATION: Intrinsically disordered regions of proteins play an essential role 
in the regulation of various biological processes. Key to their regulatory 
function is often the binding to globular protein domains via sequence elements 
known as molecular recognition features (MoRFs). Development of computational 
tools for the identification of candidate MoRF locations in amino acid sequences 
is an important task and an area of growing interest. Given the relative 
sparseness of MoRFs in protein sequences, the accuracy of the available MoRF 
predictors is often inadequate for practical usage, which leaves a significant 
need and room for improvement. In this work, we introduce MoRFCHiBi_Web, which 
predicts MoRF locations in protein sequences with higher accuracy compared to 
current MoRF predictors.
METHODS: Three distinct and largely independent property scores are computed 
with component predictors and then combined to generate the final MoRF 
propensity scores. The first score reflects the likelihood of sequence windows 
to harbour MoRFs and is based on amino acid composition and sequence similarity 
information. It is generated by MoRFCHiBi using small windows of up to 40 
residues in size. The second score identifies long stretches of protein disorder 
and is generated by ESpritz with the DisProt option. Lastly, the third score 
reflects residue conservation and is assembled from PSSM files generated by 
PSI-BLAST. These propensity scores are processed and then hierarchically 
combined using Bayes rule to generate the final MoRFCHiBi_Web predictions.
RESULTS: MoRFCHiBi_Web was tested on three datasets. Results show that 
MoRFCHiBi_Web outperforms previously developed predictors by generating less 
than half the false positive rate for the same true positive rate at practical 
threshold values. This level of accuracy paired with its relatively high 
processing speed makes MoRFCHiBi_Web a practical tool for MoRF prediction.
AVAILABILITY: http://morf.chibi.ubc.ca:8080/morf/.