pyPept

pyPept converts peptide line notations into molecular graphs and generates 2D/3D representations and approximate conformers to enable computational representation and analysis of standard and non‑natural peptides.

Key Features:

• Line notation support: Supports FASTA, HELM and BILN line notations for peptide representation.
• Non‑natural amino acids: Handles standard proteinogenic residues and non‑natural amino acids via customizable monomer libraries.
• Molecular graph conversion: Transforms peptide line notations into molecular graphs for structural representation and analysis.
• 2D and 3D representations: Provides an automated wrapper to generate and output 2D depictions and 3D representations directly from line notations.
• Physicochemical property calculation: Computes various physicochemical properties from the molecular representations.
• Conformer generation: Rapidly generates approximate peptide conformers using secondary structure restraints that can be user‑defined or predicted by the tool.
• Complex topologies: Supports circular, branched and stapled peptide topologies.

Scientific Applications:

• Peptide design and engineering: Representation and incorporation of non‑natural residues for design workflows.
• Structural visualization: Generation of 2D and 3D visualizations for structural analysis.
• Physicochemical analysis: Calculation of physicochemical properties for screening and characterization.
• Conformer modeling: Rapid approximate conformer generation using secondary structure restraints for modeling studies.
• Modeling of constrained peptides: Representation and analysis of circular, branched and stapled peptides.

Methodology:

Parses FASTA, HELM and BILN line notations and converts them into molecular graphs; applies customizable monomer libraries to represent non‑natural amino acids; generates 2D depictions and outputs 3D representations via an automated wrapper; computes physicochemical properties; generates approximate peptide conformers using secondary structure restraints that may be user‑defined or predicted by the tool.