ttsim3d 0.2.1

Simulate a 3D electrostatic potential map from a PDB in pyTorch

ttsim3d

Simulate 3D electrostatic potential maps from a PDB file in PyTorch. This package currently replicates theory laid out in Benjamin & Grigorieffa (2021).

For a full list of changes, see the CHANGELOG.

Installation

ttsim3d is available on PyPi and can be installed via

pip install ttsim3d

From source

To create a source installation, first download/clone the repository, then run the install command

git clone https://github.com/teamtomo/ttsim3d.git
cd ttsim3d
pip install -e .

Optional development and testing dependencies can also be installed by running

pip install -e ".[dev,test]"

Running CLI program

Installation of the package creates the executable program ttsim3d-cli which takes in a PDB file along with other simulation options and outputs the simulated 3D scattering potential to a .mrc file. All options for the program can be printed by running:

ttsim3d-cli --help

The following are descriptions of each of the options for the program

Option	Type	Default	Description
--pdb-filepath	Path	required	The path to the PDB file containing the atomic structure to simulate.
--mrc-filepath	Path	required	File path to save simulated volume.
--pixel-spacing	float	required	The pixel spacing of the simulated volume in units of Angstroms. Must be greater than 0.
--volume-shape	(int, int, int)	required	The shape of the simulated volume in pixels.
--voltage	float	300.0	The voltage of the microscope in kV. Default is 300 kV.
--upsampling	int	-1	The upsampling factor to apply to the simulation. The default is -1 and corresponds to automatic calculation of the upsampling factor.
--b-factor-scaling	float	1.0	The scaling factor to apply to the B-factors of the atoms in the pdb file. The default is 1.0.
--additional-b-factor	float	0.0	Additional B-factor to apply to the atoms in the pdb file. The default is 0.0.
--apply-dose-weighting	bool	True	If True, apply dose weighting to the simulation. Default is True.
--crit-exposure-bfactor	float	-1.0	B-factor to use in critical exposure calculations. The default is -1 and corresponds to the fitted critical exposure function in Grant and Grigorieff, 2015.
--dose-filter-modify-signal	Literal["None", "sqrt", "rel_diff"]	"None"	Signal modification to apply to the dose filter. Currently supports 'None', 'sqrt', and 'rel_diff'.
--dose-start	float	0.0	The starting dose in e/A^2.
--dose-end	float	30.0	The ending dose in e/A^2.
--apply-dqe	bool	True	If True, apply a DQE filter to the simulation.
--mtf-reference	Path or str	"k2_300kV"	Path to the modulation transfer function (MTF) reference star file, or one of the known MTF reference files. Default is 'k2_300kV'.
--gpu-ids	list[int]	unused	A list of GPU IDs to use for the simulation. Currently unused.

Python objects

There are two user-facing classes in ttsim3d built upon Pydantic models for validating inputs and simulating a volume. The first class, ttsim3d.models.Simulator, holds reference to a PDB file and basic simulation parameters related to that structure. The second class, ttsim3d.models.SimulatorConfig is used to configure more advanced options, such as dose weighting and simulation upsampling. An extremely basic use of these objects to run a simulation looks like

from ttsim3d.models import Simulator, SimulatorConfig

# Instantiate the configuration object 
sim_conf = SimulatorConfig(
    voltage=300.0,  # in keV
    apply_dose_weighting=True,
    dose_start=0.0,  # in e-/A^2
    dose_end=35.0,   # in e-/A^2
    upsampling=-1,   # auto
)

# Instantiate the simulator
sim = Simulator(
    pdb_filepath="some/path/to/structure.pdb",
    pixel_spacing=1.25,  # Angstroms
    volume_shape=(256, 256, 256),
    b_factor_scaling=1.0,
    additional_b_factor=15.0,  # Add to all atoms
    simulator_config=sim_conf,
)

# Run the simulation
volume = sim.run()
print(type(volume))  # torch.Tensor
print(volume.shape)  # (256, 256, 256)

# OR export the simulation to a mrc file
mrc_filepath = "some/path/to/simulated_structure/mrc"
sim.export_to_mrc(mrc_filepath)