Parameterizing non-canonical amino acids for cyclic peptide simulations

Area of Science:

• Biomolecular modeling
• Computational chemistry
• Peptide design

Background:

• Non-canonical amino acids (ncAAs) expand chemical space for biomolecular design, especially for cyclic peptides requiring precise interactions.
• Physics-driven methods are preferred for ncAAs due to generalizable energy functions, but parameter creation is challenging.
• Existing parameters are limited to canonical amino acids, necessitating custom definitions for ncAAs.

Purpose of the Study:

• To provide a comprehensive guide for building custom non-canonical amino acid (ncAA) parameters.
• To facilitate the use of ncAAs in biomolecular modeling and molecular dynamics (MD) simulations.
• To enable efficient generation of robust ncAA parameters for Rosetta and AMBER.

Main Methods:

• Developed a step-by-step protocol for creating ncAA residue definitions.
• Provided sample scripts for parameter generation compatible with Rosetta and AMBER.
• Included validation checkpoints and representative outputs for quality control.

Main Results:

• Successfully generated custom ncAA parameters for use in Rosetta and AMBER.
• Demonstrated the protocol's effectiveness for cyclic peptide modeling.
• Provided a reproducible workflow for ncAA parameterization.

Conclusions:

• The developed guide and resources streamline the process of incorporating ncAAs into biomolecular modeling and simulations.
• Novice and expert users can efficiently generate custom ncAA parameters for advanced peptide design.
• This work lowers the barrier to entry for utilizing the expanded chemical space offered by ncAAs.