pyCHARMM: Embedding CHARMM Functionality in a Python Framework

CHARMM is rich in methodology and functionality as one
of the first
programs addressing problems of molecular dynamics and modeling of
biological macromolecules and their partners, e.g., small molecule
ligands. When combined with the highly developed CHARMM parameters
for proteins, nucleic acids, small molecules, lipids, sugars, and
other biologically relevant building blocks, and the versatile CHARMM
scripting language, CHARMM has been a trendsetting platform for modeling
studies of biological macromolecules. To further enhance the utility
of accessing and using CHARMM functionality in increasingly complex
workflows associated with modeling biological systems, we introduce
pyCHARMM, Python bindings, functions, and modules to complement and
extend the extensive set of modeling tools and methods already available
in CHARMM. These include access to CHARMM function-generated variables
associated with the system (psf), coordinates, velocities and forces,
atom selection variables, and force field related parameters. The
ability to augment CHARMM forces and energies with energy terms or
methods derived from machine learning or other sources, written in
Python, CUDA, or OpenCL and expressed as Python callable routines
is introduced together with analogous functions callable during dynamics
calculations. Integration of Python-based graphical engines for visualization
of simulation models and results is also accessible. Loosely coupled
parallelism is available for workflows such as free energy calculations,
using MBAR/TI approaches or high-throughput multisite λ-dynamics
(MSλD) free energy methods, string path optimization calculations,
replica exchange, and molecular docking with a new Python-based CDOCKER
module. CHARMM accelerated platform kernels through the CHARMM/OpenMM
API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated
into this Python framework. We anticipate that pyCHARMM will be a
robust platform for the development of comprehensive and complex workflows
utilizing Python and its extensive functionality as well as an optimal
platform for users to learn molecular modeling methods and practices
within a Python-friendly environment such as Jupyter Notebooks.