OPAL plus : Length-Specific MoRF Prediction in Intrinsically Disordered Protein Sequences

View/ Open
Author(s)
Sharma, Ronesh
Sharma, Alok
Raicar, Gaurav
Tsunoda, Tatsuhiko
Patil, Ashwini
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
PROTEOMICS
Volume
19
Issue
6
Copyright Statement
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. This is the peer reviewed version of the following article: OPAL+: Length‐Specific MoRF Prediction in Intrinsically Disordered Protein Sequences, Proteomics, Vol. 19, 1800058, pp. 1-4, 2019, which has been published in final form at 10.1002/pmic.201800058. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
Subject
Biological sciences
Biomedical and clinical sciences