OPAL: prediction of MoRF regions in intrinsically disordered protein sequences
Author(s) -
Ronesh Sharma,
Gaurav Raicar,
Tatsuhiko Tsunoda,
Ashwini Patil,
Alok Sharma
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty032
Subject(s) - computer science , sequence (biology) , similarity (geometry) , component (thermodynamics) , artificial intelligence , task (project management) , pattern recognition (psychology) , function (biology) , intrinsically disordered proteins , computational biology , biological system , image (mathematics) , biology , physics , genetics , nuclear magnetic resonance , management , economics , thermodynamics
Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues.
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