SEQL-NRPS

SEQL-NRPS predicts substrate specificities of non-ribosomal peptide synthetase (NRPS) A-domains from their amino acid sequences to identify motifs correlated with substrate incorporation.

Key Features:

• NRPS A-domain substrate prediction: Predicts which amino acid substrates individual NRPS modules incorporate based solely on primary amino acid sequences.
• Sequence-based analysis: Analyzes primary structures of NRPS A-domains to identify sequence signals associated with specificity.
• Discriminative classification (SEQL): Employs the Sequence Learner (SEQL) discriminative classification method to detect predictive sequence patterns.
• Motif occurrence analysis: Examines occurrences of specific motifs within amino acid sequences and compares these motifs across sequences.
• Genome-scale in silico screening: Enables in silico searches across genomes to identify NRPS enzymes likely to synthesize particular substrates.
• Validation and mechanistic insight: Compares predictions with other methods to validate performance and provide insights into motifs that may influence substrate specificity.

Scientific Applications:

• NRPS substrate annotation: Assignment of substrate specificities to individual NRPS modules in sequence data.
• Genome mining for natural products: Identification of NRPS enzymes in genomes that may synthesize novel bioactive compounds.
• Drug discovery support: Prioritization of NRPS systems for experimental characterization in antibiotic and secondary metabolite discovery.
• Study of enzymatic specificity: Investigation of motif-function relationships underlying substrate selection in microbial NRPSs.

Methodology:

SEQL-NRPS analyzes primary amino acid sequences of NRPS A-domains using the Sequence Learner (SEQL) discriminative classifier to identify and compare motif occurrences across sequences and predict the substrates incorporated by individual modules.