δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches.

Area of Science:

• Biochemistry
• Computational Biology
• Neuroscience

Background:

• Delta-conotoxins (δ-conotoxins) are cone snail venom peptides that inhibit voltage-gated sodium channels, causing neurological effects.
• Structural characterization of these peptides is challenging due to isolation and synthesis difficulties.

Purpose of the Study:

• To model and analyze 18 previously uncharacterized δ-conotoxins using computational methods.
• To provide structural insights into these peptides and their potential pharmacological activities.

Main Methods:

• Utilized deep-learning algorithm AlphaFold for structure prediction.
• Employed comparative modeling method RosettaCM.
• Modeled peptides from piscivorous, vermivorous, and molluscivorous cone snails.

Main Results:

• Generated structural models for 18 novel δ-conotoxins.
• Identified potential structural features influencing peptide binding and activity.
• Provided insights into the pharmacological relevance of these toxins.

Conclusions:

• Computational modeling is effective for characterizing novel disulfide-rich peptides like δ-conotoxins.
• The study offers a roadmap for modeling similar peptides.
• Findings have implications for drug development targeting sodium channels.