OPAL

OPAL predicts molecular recognition features (MoRFs) within intrinsically disordered protein sequences to identify interaction-prone regions.

Key Features:

• MoRF prediction: Predicts molecular recognition features (MoRFs) located in long disordered regions of intrinsically disordered proteins.
• Dual-source information processing: Employs two independent sources of information computed by distinct component predictors.
• Composition and sequence-similarity scoring: Uses a component predictor that scores composition and sequence similarity between MoRF and non-MoRF regions.
• Structural and physicochemical scoring: Uses a component predictor that integrates half-sphere exposure (HSE), solvent accessible surface area (ASA), backbone angle information, and amino acid properties of flanking sequences.
• Score integration: Processes and combines scores from both sources using a common averaging method.
• Benchmark evaluation: Has been evaluated against MoRFpred, MoRFchibi, and MoRFchibi-web and reported to outperform these predictors.

Scientific Applications:

• Protein–protein interaction mapping: Identifies interaction-prone MoRF regions that mediate transient protein–protein interactions.
• Mechanistic studies of function and disease: Locates MoRFs within disordered regions to support analysis of molecular mechanisms underlying biological functions and disease processes.

Methodology:

Two independent component predictors generate scores: one based on composition and sequence similarity between MoRF and non-MoRF regions, and the other integrating HSE, ASA, backbone angle information and amino acid properties of flanking sequences; the scores are processed and combined by averaging.