MFDp

MFDp predicts intrinsic protein disorder from amino-acid sequences, providing per-residue disorder probabilities and per-sequence disorder content to support analysis of intrinsically disordered proteins (IDPs).

Key Features:

• Accurate Disorder Prediction: MFDp2 utilizes sequence-based methodologies to predict protein disorder by combining per-residue disorder probabilities with per-sequence disorder content and applying novel post-processing filters to enhance predictive quality.
• Comprehensive Sequence-Derived Information: Provides sequence-derived data including sequence conservation, predicted secondary structure, and relative solvent accessibility for profiling predicted disordered regions.
• Alignment with Annotated Disorder Chains: Aligns query sequences to chains with known disorder annotations to relate predictions to experimentally annotated disordered regions.
• Batch Processing Capability: Supports predictions for multiple proteins simultaneously to enable large-scale and comparative analyses.
• Downloadable Results: Outputs prediction results as parsable text files for downstream computational analysis or integration into workflows.

Scientific Applications:

• IDP characterization: Identification and profiling of disordered regions within proteins to advance research on intrinsically disordered proteins (IDPs).
• Functional inference: Interpretation of functional implications of disordered regions involved in cellular processes such as signaling and regulation.
• Large-scale analyses: Enabling large-scale and comparative studies of protein disorder across multiple sequences.
• Disease-related studies: Supporting investigations linking protein disorder to function and disease mechanisms.

Methodology:

MFDp2 applies sequence-based computational algorithms that combine per-residue disorder probabilities with per-sequence disorder content, aligns sequences to chains with known disorder annotations, provides sequence-derived attributes (sequence conservation, predicted secondary structure, relative solvent accessibility), and employs novel post-processing filters to refine prediction accuracy.