MOTIVATION: Intrinsically disordered regions of proteins play an essential role 
in the regulation of various biological processes. Key to their regulatory 
function is the binding of molecular recognition features (MoRFs) to globular 
protein domains in a process known as a disorder-to-order transition. Predicting 
the location of MoRFs in protein sequences with high accuracy remains an 
important computational challenge.
METHOD: In this study, we introduce MoRFCHiBi, a new computational approach for 
fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines 
the outcomes of two support vector machine (SVM) models that take advantage of 
two different kernels with high noise tolerance. The first, SVMS, is designed to 
extract maximal information from the general contrast in amino acid compositions 
between MoRFs, their surrounding regions (Flanks), and the remainders of the 
sequences. The second, SVMT, is used to identify similarities between regions in 
a query sequence and MoRFs of the training set.
RESULTS: We evaluated the performance of our predictor by comparing its results 
with those of two currently available MoRF predictors, MoRFpred and ANCHOR. 
Using three test sets that have previously been collected and used to evaluate 
MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other 
predictors with respect to different evaluation metrics. In addition, MoRFCHiBi 
is downloadable and fast, which makes it useful as a component in other 
computational prediction tools.
AVAILABILITY AND IMPLEMENTATION: http://www.chibi.ubc.ca/morf/.