FragBuilder: an efficient Python library to setup quantum chemistry calculations on peptides models.

Area of Science:

• Computational Chemistry
• Structural Biology
• Biophysics

Background:

• Accurate peptide structure generation is crucial for computational studies.
• Existing methods may lack efficiency or flexibility in conformational sampling.

Purpose of the Study:

• To introduce FragBuilder, a Python library for rapid and efficient generation of realistic peptide model structures.
• To facilitate the setup of quantum mechanical calculations on peptide models.
• To provide tools for conformational sampling and geometry optimization.

Main Methods:

• Utilizes Python for library development.
• Allows manual specification of peptide conformations.
• Implements sampling of backbone and side-chain conformations from continuous distributions.
• Integrates MMFF94 force field for geometry optimization.
• Supports output in various file formats (e.g., XYZ, PDB) and for quantum chemistry programs.

Main Results:

• FragBuilder enables quick generation of peptide model structures.
• The library supports diverse conformational exploration (manual and sampled).
• Geometry optimization using MMFF94 is readily available.
• Output compatibility with standard formats and quantum chemistry software is ensured.

Conclusions:

• FragBuilder offers a powerful and efficient solution for creating peptide models for computational studies.
• The library streamlines the process from structure generation to preparation for quantum mechanical calculations.
• Its flexibility and ease of use make it a valuable tool for researchers in computational chemistry and related fields.