The constrained search program
The constrained search program takes in locations and orientations for one particle and searches for another particle based on these locations and orientations. This allows us to perform for fewer cross correlations, reducing the noise and thus increasing our sensitivity. This increased sensitivity is incredibly useful when searching for smaller proteins which may associate with a larger complex but otherwise fall below the noise floor of full-orientation 2DTM.
Configuration options
A default config file for the constrained search program is available here on the GitHub page. This file is separated into multiple "blocks" each configuring distinct portions of the program discussed briefly below.
Top-level template paths
The first field in the configuration file is the path to the simulated 3D map of the constrained particle we want to search for. Note this is not the the 3D map used in either of the match template or refine template steps.
template_volume_path: /some/path/to/template.mrc
Center vector between the structures
The center vector is the vector that points from the reference particle to the constrained particle when all Euler angles are 0 (default orientation). We include the script get_center_vector.py to calculate this based on two aligned (relative to each other) PDB files.
The following example is specific to the constrained 40S ribosome search.
center_vector: [53.65, 82.58, 47.17]
Particle stacks for the reference and constrained particle
You must provide particle stacks for the constrained particle as well as the reference particle. The euler angles and locations are taken from the reference particle stack, and the mean and variance are taken from the constrained particle stack input, allowing us to accurately calculate a z-score. The extracted box size determines how many pixels will be searched over and is calculated as . Since we want as few cross-correlations as possible, the additional extracted pixels should be kept as low as possible while still allowing for some variability in the (x, y) position.
particle_stack_reference: # This is from the reference particles
df_path: /some/path/to/particles.csv
extracted_box_size: [520, 520]
original_template_size: [512, 512]
particle_stack_constrained: # This is from the constrained particles
df_path: /some/path/to/particles.csv
extracted_box_size: [520, 520]
original_template_size: [512, 512]
Orientation refinement configuration
We must specify what angular space to perform the orientation search over. The first thing we must specify is the primary rotation axis, which we set as the Z axis. If unknown, this can be calculated using two PDB models (one rotated and one unrotated) using the script get_rot_axis.py.
As well as searching over one rotation axis, we can search over a second axis, which by default is the y axis.
This can be changed to any axis orthogonal to the primary axis by specifying a roll_axis and using the base_grid_method: roll.
If a second axis is not known, a roll axis search can be performed (search_roll_axis: true) with a specified roll_step.
The most important parameters to specify is the range and step size for the psi and theta searches. The psi angles are rotations around the Z-axis, and theta rotations around the orthogonal axis. Since psi and phi are redundant for small angular searches, we usually do not need to search over phi.
orientation_refinement_config:
enabled: true
rotation_axis_euler_angles: [0.0, 0.0, 0.0] # This is the rotation axis
base_grid_method: uniform
psi_step: 1.0 # psi in degrees
theta_step: 1.0 # theta and phi in degrees
phi_min: -0.0
phi_max: 0.0
theta_min: -8.0
theta_max: 2.0
psi_min: -13.0
psi_max: 2.0
search_roll_axis: false
roll_axis: [0,1] # [x,y] This defines the roll axis (orthogonal to the rotation axis).
roll_step: 2.0
Pre-processing filters and computational config
These should be the same as for Match Template.