FunARTS

FunARTS identifies biosynthetic gene clusters (BGCs) and associated resistance and housekeeping genes in fungal genomes to predict bioactive compound modes of action and prioritize BGCs for natural product discovery.

Key Features:

• BGC identification: Identifies biosynthetic gene clusters (BGCs) in fungal genomes.
• Target-directed genome mining: Implements target-directed genome mining to associate BGCs with potential molecular targets.
• Mode-of-action prediction: Predicts compound modes of action by linking associated resistant target genes to BGCs.
• Resistance and housekeeping gene linkage: Links housekeeping and known resistance genes to BGC proximity and gene duplication events to detect self-resistance patterns.
• Gene cluster networking: Generates gene cluster networking by comparing BGC similarities across multiple genomes.
• Prioritization: Prioritizes BGCs with potential novel modes of action based on resistance and similarity analyses.

Scientific Applications:

• Natural product discovery: Discovery and prioritization of fungal secondary metabolites encoded by BGCs.
• Mode-of-action inference: Identification of BGCs likely to encode bioactive compounds with novel modes of action.
• Therapeutic lead identification: Guiding selection of candidate compounds for therapeutic applications, including against multi-drug resistant infections.
• Resistance mechanism characterization: Characterization of putative self-resistance genes and gene duplication events within BGCs.
• Comparative BGC genomics: Comparative analysis and networking of BGCs across multiple fungal genomes.

Methodology:

Uses target-directed genome mining; links housekeeping and known resistance genes to BGC proximity and duplication events; predicts modes of action from associated resistant target genes; and generates gene cluster networking by comparing BGC similarities across multiple genomes.