PyFraME: Python framework for Fragment-based Multiscale Embedding

This archive contains the source of PyFraME releases. The project itself is hosted on GitLab (https://gitlab.com/FraME-projects/PyFraME).

PyFraME is a Python package providing a framework for managing fragment-based multiscale embedding calculations. In such calculations, a molecular system is divided into two principal domains: a central core and its environment. The core part is treated at the highest level of theory while the effects from the environment are included effectively through an embedding potential. Using PyFraME the user can automatize the workflow starting from an initial structure to the final embedding potential. It can be used to build a multilayer description of the molecular environment. Each layer can be described either by a standard embedding potential, i.e., using a predefined set of parameters, or by deriving the embedding-potential parameters based on first-principles calculations. For the latter, a fragmentation method is used to subdivide large molecular structures into smaller computationally manageable fragments. The number of layers, as well as the composition and level of theory used for each layer, can be fully customized.

The basic workflow consists of three main steps. First, a molecular structure is given as an input. Currently, PyFraME supports input files in the PDB format. The input file reader extracts information about the structure and composition of the system, and it also defines the basic units of the system, i.e., fragments. Small molecules typically constitute a fragment on their own, but larger molecules need to be broken down into smaller fragments. For example, for proteins, a fragment would usually consist of an amino-acid residue. The molecular system to be used for the embedding calculation is then built by extracting subsets from the full list of fragments according to user-specified criteria, such as name, chain ID, distance, or a combination thereof, and placed into separate regions. As mentioned above, any number of regions may be added, and each can be fully customized. Once the system has been built, the final step is the derivation of the embedding potential. Depending on the specifics, it may involve a large number of separate calculations on the individual fragments in order to compute the embedding-potential parameters. For large molecules, where the parameters cannot be computed directly, PyFraME uses a fragmentation method based on the molecular fractionation with conjugate caps (MFCC) approach to derive the parameters. The individual fragment calculations are typically performed by Dalton and the LoProp Python package but this can be customized. The fragmentation of the system, fragment calculations, and subsequent joining of parameters to build the embedding potential are fully automatized and can make full use of large-scale HPC resources.