KPHMMER

KPHMMER detects pathway-specific Pfam domains associated with user-defined KEGG pathways to identify secondary metabolism–related biosynthetic machinery.

Key Features:

• Pathway-Specific Domain Extraction: Extracts Pfam domains that are specific to user-defined KEGG pathways and specified organisms, enabling targeted detection of pathway-associated domains including those involved in secondary metabolism.
• Pfam-A HMM Subsetting: Focuses searches on a subset of the Pfam-A HMM file relevant to the user's query to restrict domain detection to pathway-specific HMM models.
• Improved Detection Performance: Demonstrates superior detection of secondary metabolism-specific Pfam domain sets by limiting searches to pathway-relevant domain models.
• Reduced Computational Cost: Narrows the analysis scope to pathway-specific domains to reduce computational resources required for domain detection across genomes.
• Genome-wide Versatility: Applicable to a wide range of genome-sequenced species for analysis of pathway-specific Pfam domains.
• Python implementation: Implemented in Python.

Scientific Applications:

• Biosynthetic pathway discovery: Facilitates identification and characterization of biosynthetic pathways and machinery involved in natural product synthesis, especially secondary metabolites.
• Pharmacology and drug development: Supports identification of Pfam domain sets and gene signatures linked to clinically significant natural products for pharmacological and drug discovery studies.

Methodology:

Leverages Hidden Markov Models (HMMs) to identify Pfam domains associated with user-defined KEGG pathways and specified organisms by focusing on a pathway-relevant subset of the Pfam-A HMM file.