Abstract
We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in a variety of useful formats, such as XYZ or PDB formats, or directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license.
Highlights
Modeling of chemical properties of proteins is a challenging task in modern computational biochemistry, mainly due to the large number of atoms that need to be treated computationally, compared to the computational speed of modern computers
We have used the presented Python library to carry out calculations on peptides modeling the backbone of a protein in the parametrization of amide proton chemical shifts (Christensen et al, 2013)
Optimization When generating peptides with a specific set of dihedral angles the structure may, in some cases, contain steric clashes. We found this prevented us from starting quantum mechanical geometry optimization on the structures, even when these were generated to match angles from experimental structures
Summary
Modeling of chemical properties of proteins is a challenging task in modern computational biochemistry, mainly due to the large number of atoms that need to be treated computationally, compared to the computational speed of modern computers. Calculations on peptides have been used to parametrize protein-specific molecular mechanics force fields, and models for NMR properties of proteins such as chemical shifts and spin-spin coupling constants (Mackerell, 2004; Vila et al, 2009; Case, Scheurer & Bruschweiler, 2000). We have used the presented Python library to carry out calculations on peptides modeling the backbone of a protein in the parametrization of amide proton chemical shifts (Christensen et al, 2013). We have carried out more than 1.5 million quantum mechanical geometry optimization and NMR shielding calculations on peptides in order to extend our model of protein chemical shifts. Our library includes methods for geometry optimization with a molecular mechanics force field, efficient conformational sampling from continuous probability distributions and lastly output to a variety of output formats or, optionally, directly as input file for a quantum chemistry program. Gaussian 09 (Frisch et al, 2009) is supported via specialized classes, and nearly 100 additional file formats are supported through the file writer
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