MOTIVATION: Intrinsically Disordered Proteins (IDPs) lack stable tertiary 
structure and they actively participate in performing various biological 
functions. These IDPs expose short binding regions called Molecular Recognition 
Features (MoRFs) that permit interaction with structured protein regions. Upon 
interaction they undergo a disorder-to-order transition as a result of which 
their functionality arises. Predicting these MoRFs in disordered protein 
sequences is a challenging task.
METHOD: In this study, we present MoRFpred-plus, an improved predictor over our 
previous proposed predictor to identify MoRFs in disordered protein sequences. 
Two separate independent propensity scores are computed via incorporating 
physicochemical properties and HMM profiles, these scores are combined to 
predict final MoRF propensity score for a given residue. The first score 
reflects the characteristics of a query residue to be part of MoRF region based 
on the composition and similarity of assumed MoRF and flank regions. The second 
score reflects the characteristics of a query residue to be part of MoRF region 
based on the properties of flanks associated around the given residue in the 
query protein sequence. The propensity scores are processed and common averaging 
is applied to generate the final prediction score of MoRFpred-plus.
RESULTS: Performance of the proposed predictor is compared with available MoRF 
predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training 
and test sets used to evaluate the mentioned predictors, the proposed predictor 
outperforms these predictors and generates lower false positive rate. In 
addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it 
can be used as input to other computational tools.
AVAILABILITY: 
https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download.