OPAL+: Length‐Specific MoRF Prediction in Intrinsically Disordered Protein Sequences

• Published in: Proteomics (Weinheim) Vol. 19; no. 6; pp. e1800058 - n/a
• Main Authors: Sharma, Ronesh, Sharma, Alok, Raicar, Gaurav, Tsunoda, Tatsuhiko, Patil, Ashwini
• Format: Journal Article
• Language: English
• Published: Germany 01-03-2019
• Subjects: Animals; Humans; intrinsically disordered proteins; Intrinsically Disordered Proteins - chemistry; Markov Chains; molecular recognition features; MoRF prediction; Protein Conformation; Proteomics - methods; Software; Support Vector Machine; support vector machines; support vector machines; MoRF prediction; intrinsically disordered proteins; molecular recognition features
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.201800058

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.