PepFuNN

PepFuNN enables the study of peptide libraries and structure-activity relationship analyses through a modular Python framework.

Key Features:

• Physicochemical Property Calculations: Calculates physicochemical properties for peptides composed of natural amino acids and, in some cases, non-natural amino acids based on the availability of a public monomer dictionary.
• Similarity Analysis: Performs similarity analysis using different peptide representations.
• Clustering: Clusters peptides using molecular fingerprints or calculated descriptors.
• Library Design: Designs peptide libraries based on specific requirements.
• Mutation Selection: Extracts matched pairs from experimental campaigns to guide selection of the most relevant mutations in new design rounds.

Scientific Applications:

• Peptide Library Analysis: Studies the chemical space of peptide libraries containing natural and selected non-natural amino acids.
• Structure-Activity Relationship Analysis: Supports structure-activity relationship analyses for peptide-based drug discovery campaigns.
• Peptide Optimization: Guides iterative peptide design through experimental data-driven mutation selection from matched molecular pairs.

Methodology:

PepFuNN is implemented as a Python package comprising five modules for peptide analysis.