FoldUnfold

FoldUnfold predicts disordered regions in protein sequences using a mean packing density of residues to detect segments of weak expected packing associated with structural disorder and to inform studies of protein function and molecular interactions.

Key Features:

• Mean packing density metric: Introduces mean packing density of residues as a parameter for disorder detection.
• Detection principle: Identifies disordered segments by locating regions with weak expected packing density.
• Validation datasets: Evaluated on datasets comprising 559 globular proteins and 129 proteins with long disordered segments.
• Comparative performance: Demonstrated improved predictive performance relative to DISOPRED, PONDR VL3H, IUPred, and GlobPlot.
• Biological relevance: Targets disordered regions that are implicated in molecular interactions and cellular processes.

Scientific Applications:

• Disordered region identification: Mapping disordered segments in proteins to support studies of structure–function relationships and molecular interactions.
• Benchmarking of predictors: Comparative evaluation and benchmarking of disorder prediction methods using curated protein datasets.
• Analysis of long disordered segments: Characterization of proteins containing long intrinsically disordered regions alongside globular proteins.

Methodology:

Computes mean packing density per residue and flags regions with weak expected packing as disordered; performance was validated on 559 globular proteins and 129 proteins with long disordered segments and compared against DISOPRED, PONDR VL3H, IUPred, and GlobPlot.