HHrep

HHrep identifies repetitive elements in protein sequences by pairwise comparison of profile hidden Markov models (HMMs), enabling de novo detection and characterization of highly divergent repeat units that may only be detectable through structural analysis.

Key Features:

• De novo repeat identification: Performs de novo identification of repetitive elements within protein sequences.
• Profile HMM pairwise comparison: Uses pairwise comparison of profile hidden Markov models (HMMs) as the core methodological approach.
• Detection of highly divergent repeats: Detects and characterizes repeat units that are highly divergent at the sequence level.
• Detection of complex fold-associated repeats: Identifies repeat units associated with complex folds including beta-propeller, ferredoxin-like, double psi barrels, and (beta-alpha)8 TIM barrels.
• Reveals sequence-derived symmetries: Reveals clear 4- and 8-fold symmetries from sequence data alone in TIM-barrel superfamilies.
• Suggests ancestral duplication patterns: Supports inference of ancestral duplication events such as beta-alpha-beta-alpha or beta-alpha unit duplications based on sequence symmetry.
• Sensitivity relative to structural analysis: Provides sensitivity for repeat detection that can uncover repeats otherwise identifiable only by structural analysis.

Scientific Applications:

• Evolutionary history of protein repeats: Enables study of the evolutionary origins and duplication events of repeat motifs in proteins.
• Structure–function analysis: Supports analysis of structure–function relationships in proteins containing repeat units and complex folds.
• Sequence-based symmetry detection: Allows identification of sequence-derived symmetry patterns that inform hypotheses about ancestral structural organization.

Methodology:

Pairwise comparison of profile hidden Markov models (HMMs) is used to de novo identify and characterize repeat units from protein sequences.