CLAP

CLAP classifies full-length protein sequences using an alignment-free pattern-matching algorithm that assigns local matching scores (LMS) to generate functionally homogeneous clusters for protein function prediction and classification, with emphasis on multi-domain proteins.

Key Features:

• Alignment-free pattern-matching: Employs a pattern-matching algorithm instead of sequence alignment to mitigate issues from poor alignments in multi-domain proteins.
• Local Matching Scores (LMS): Assigns local matching scores to residues within matched patterns to quantify local sequence similarity.
• Full-length sequence utilization: Processes entire protein sequences without requiring prior domain definitions, capturing information in domains, tethered regions, and accessory domains.
• Independence from domain annotation: Operates independently of domain assignment, domain order, sequence length, and domain diversity.
• Statistically determined cut-offs: Parses matches at statistically determined cut-offs to define clusters and enhance clustering precision.
• Domain architectural homogeneity in clusters: Produces clusters that exhibit high domain architectural similarity among member sequences.

Scientific Applications:

• Functional homogeneity clustering: Generates functionally homogeneous sequence clusters using only sequence information.
• Multi-domain protein classification: Addresses classification and function-prediction challenges in proteins with varied lengths, domain shuffling, or circular permutations.
• Protein kinases and immunoglobulins analysis: Demonstrated, in pilot studies, the ability to produce clusters with high functional and domain-architectural similarity corresponding to sub-family level classifications.

Methodology:

Applies an alignment-free pattern-matching algorithm that assigns LMS to residues across full-length sequences and parses matches at statistically determined cut-offs to form clusters without relying on predefined domain annotations.