C-HMM

C-HMM detects remote protein homologues by applying cascading Hidden Markov Models to identify deep evolutionary relationships in protein sequence databases.

Key Features:

• Cascading Hidden Markov Models: Uses cascading Hidden Markov Models (HMMs) to improve detection of distant evolutionary relationships between protein sequences.
• Cluster-Based Profile Generation: Clusters sequence hits and generates HMM profiles from clusters to support iterative cascade searches.
• Multi-Level Structural Coverage: Identifies homologous relationships across protein family, superfamily, and fold structural levels.

Scientific Applications:

• Remote Homology Detection: Identifies distant homologous proteins within large protein sequence databases.
• Protein Function Annotation: Supports functional annotation of proteins through detection of evolutionary relationships.
• Protein Evolution Studies: Investigates evolutionary relationships among protein families, superfamilies, and folds.

Methodology:

C-HMM performs cascaded sequence searches using Hidden Markov Models by clustering sequence hits, generating HMM profiles from clusters, and iteratively detecting remote homologues across protein sequence databases.