LOCP

LOCP identifies putative pilus operons in Gram-positive prokaryotes to enable analysis of genes encoding pili involved in host-microbe interactions such as pathogenicity, colonization, and adhesion.

Key Features:

• Two-step identification process: Uses hmmsearch with profile Hidden Markov Models (HMMs) to detect pilus-related sequences and then applies hypergeometric distribution analysis with Monte Carlo simulations to locate statistically significant, densely clustered chromosomal regions representing operons.
• Curated HMM library: Employs a carefully curated set of HMMs tailored to recognize distinctive motifs of pilus proteins.
• Statistical clustering: Applies hypergeometric distribution analysis complemented by Monte Carlo simulations to ensure robust detection of operon clusters.
• Enhanced sensitivity and specificity: Combines HMM-based motif recognition and statistical clustering to improve the sensitivity and specificity of operon prediction.

Scientific Applications:

• Gene prioritization for experimental study: Pinpoints genes associated with pilus formation for targeted laboratory investigations of adhesion and virulence.
• Genome screening for pilus operons: Identifies strains that possess pilus operons to facilitate studies of bacterial colonization and infection processes.
• Comparative detection and discovery: Enables identification of pilus operons across diverse Gram-positive bacteria, including unexpected hosts, to support studies of bacterial pathogenic mechanisms.

Methodology:

LOCP uses profile Hidden Markov Models (HMMs) searched with hmmsearch for sequence motif recognition, followed by hypergeometric distribution analysis and Monte Carlo simulations to detect statistically significant clusters of hits representing pilus operons.