Intrinsically disordered proteins (IDPs) contain long unstructured regions, 
which play an important role in their function. These intrinsically disordered 
regions (IDRs) participate in binding events through regions called molecular 
recognition features (MoRFs). Computational prediction of MoRFs helps identify 
the potentially functional regions in IDRs. In this study, OPAL+, a novel MoRF 
predictor, is presented. OPAL+ uses separate models to predict MoRFs of varying 
lengths along with incorporating the hidden Markov model (HMM) profiles and 
physicochemical properties of MoRFs and their flanking regions. Together, these 
features help OPAL+ achieve a marginal performance improvement of 0.4-0.7% over 
its predecessor for diverse MoRF test sets. This performance improvement comes 
at the expense of increased run time as a result of the requirement of HMM 
profiles. OPAL+ is available for download at 
https://github.com/roneshsharma/OPAL-plus/wiki/OPAL-plus-Download.