ConoDictor

ConoDictor classifies conopeptides (the primary bioactive components of cone snail venom) into superfamilies based on amino acid sequence.

Key Features:

• Dual-Approach Classification: Combines predictions from profile hidden Markov models and generalized profiles to enhance classification accuracy and reliability.
• Profile HMMs (HMMER 3): Applies profile hidden Markov models using hmmsearch from HMMER 3 to model conopeptide sequence families.
• Generalized Profiles (pftools 2.3): Uses ps_scan.pl from the pftools 2.3 package to apply generalized profiles that complement HMM predictions.
• Implementation: Implemented in Perl and incorporates the common gateway interface (CGI) module and PHP.

Scientific Applications:

• Superfamily Assignment: Assigns conopeptides to superfamilies from amino acid sequence data to categorize venom peptides.
• Venom Peptide Characterization: Supports analysis of newly identified conopeptides to inform their structural and functional properties.
• Drug Discovery: Facilitates exploration and identification of novel bioactive compounds relevant to drug discovery and development.

Methodology:

Applies profile HMMs via hmmsearch (HMMER 3) and generalized profiles via ps_scan.pl (pftools 2.3), integrating both prediction types for conopeptide superfamily classification.