CCHMMPROF

CCHMMPROF predicts coiled-coil segments in protein sequences using a profile-based Hidden Markov Model to identify coiled-coil domains for structural and functional analysis.

Key Features:

• Hidden Markov Model (HMM): CCHMMPROF employs a Hidden Markov Model that leverages information from multiple sequence alignments (profiles) to capture evolutionary signals relevant to coiled-coil prediction.
• Profile integration: The method integrates profile-based information into the HMM framework rather than relying on single-sequence scoring.
• Predictive performance: The predictor reports a discrimination accuracy of 97%, a true positive rate of 79%, and a false positive rate of 1% for coiled-coil sequence detection.
• Localization accuracy: Residue-level accuracy is 80%, with per-segment and per-protein prediction rates of 81% and 80%, respectively.

Scientific Applications:

• Protein Structure Prediction: Identification of coiled-coil domains to support interpretation of protein structural models.
• Biological Interaction Analysis: Detection of coiled coils to inform studies of protein–protein interaction networks and interaction interfaces.
• Genome Annotation: High-throughput annotation of proteomes to identify proteins containing potential coiled-coil domains.

Methodology:

Integrates profile-based information from multiple sequence alignments into a Hidden Markov Model; contrasted with methods based on position-specific score matrices (PSSMs) or machine learning techniques.