schrodinger.application.desmond.mxmd.mxmd_cleanup module¶

A post-simulation clean-up script for Mixed Solvent (MxMD) workflow. This script extracts the occupancy data for all co-solvent subjob and combines them into occupancy maps. It then clusters and identifies Hotspots from these maps.

As an output, a Maestro Project file (.prjzip) and a ‘results’ directory are written. The directory contains CNS maps for all co-solvent probes and a Maestro structure of the last snapshots for all co-solvent subjob. The command should be run from the base directory of the mixed solvent job.

schrodinger.application.desmond.mxmd.mxmd_cleanup.set_isosurface(pt: schrodinger.project.project.Project, cns_file: str, row_index: int, cutoff: float, color: tuple, surf_name: str, surf_comment: str)¶: When writing a project table, this function will setup all isosurface properties.

class schrodinger.application.desmond.mxmd.mxmd_cleanup.Spot(*, grid_points: Set[Tuple[float, float, float]], grid: numpy.ndarray, probe_name: str, grid_spacing: Tuple[float, float, float], probe_mols: Set[schrodinger.structure._structure._Molecule])¶

Bases: object

Occupancy map for each probe is clustered and discretized into separate occupancy clusters called ‘Spots’. Each Spot object contains the grid’s coordinates, their occupancy values and the probe CTs.

__init__(*, grid_points: Set[Tuple[float, float, float]], grid: numpy.ndarray, probe_name: str, grid_spacing: Tuple[float, float, float], probe_mols: Set[schrodinger.structure._structure._Molecule])¶

property volume¶

property probe_name¶

property probe_mols¶

class schrodinger.application.desmond.mxmd.mxmd_cleanup.Hotspot(box_size: Tuple[float, float, float], center: Tuple[float, float, float], grid_spacing: Tuple[float, float, float])¶

Bases: object

A Hotspot refers to a collection of grid points, occupied by two or more Spots.

__init__(box_size: Tuple[float, float, float], center: Tuple[float, float, float], grid_spacing: Tuple[float, float, float])¶

add_spot(spot: schrodinger.application.desmond.mxmd.mxmd_cleanup.Spot)¶: Spot must have the same grid_spacing

property volume: float¶

score(func) → int¶: This function is how to treat occupancy value of overlapping grid points. :param func: can be either np.mean, np.max or np.sum or np.min

get_probes_structure(hotspot_id=None, log=False) → schrodinger.structure._structure.Structure¶

write_mae(filename: str)¶: Write probe CTs that correspond to this hotspot.

write_cns(filename: str, crop_size: Optional[float] = None, func: Callable = <function sum>)¶

Write hotspot grid to cns format.

Parameters: crop_size – to reduce the size of he CNS files that is written, crop the grid to specified size.

The sigma values for each grid point will be reduced by func

schrodinger.application.desmond.mxmd.mxmd_cleanup.read_checkpoint_file(chkpt_file: Union[pathlib.Path, str]) → Optional[schrodinger.application.desmond.cmj.Engine]¶

Read multisim checkpoint file. :param chkpt_file: Checkpoint file name.

Returns: Engine object:
Raise: ValueError if the checkpoint file could not be read.

schrodinger.application.desmond.mxmd.mxmd_cleanup.get_stage(job: schrodinger.application.desmond.cmj.Engine, stage_name: str) → Tuple[Optional[int], Optional[str]]¶

Return the index of stage with name stage_name.

Parameters: job – The checkpoint file object.
Returns: Index of stage_name or None if not found. Job directory or None if not found.

schrodinger.application.desmond.mxmd.mxmd_cleanup.split_into_spots(probe_name: str, probe_data: numpy.array, probe_mols: List[schrodinger.structure._structure._Molecule], grid_spacing: Tuple[float, float, float], grid_center: Tuple[float, float, float], box_size: Tuple[float, float, float], sigma: float, cluster_cutoff: float) → List[schrodinger.application.desmond.mxmd.mxmd_cleanup.Spot]¶: Given an occupancy grid for a single probe, cluster these points and create Spot objects from them.

class schrodinger.application.desmond.mxmd.mxmd_cleanup.CleanUp(chkpt_file: str, sigma: float = 20.0, cluster_cutoff: float = 3.0, ligand_xyz: Optional[numpy.array] = None)¶

Bases: object

Class for cleaning up mixed solvent subjobs.

__init__(chkpt_file: str, sigma: float = 20.0, cluster_cutoff: float = 3.0, ligand_xyz: Optional[numpy.array] = None)¶

run()¶: Run the cleanup workflow.

get_subjob_names() → List[str]¶

Returns: A list of subjob names.

create_results_directory()¶: Create directory in which all data results will be written to.

read_archive_data()¶: Copy CNS and raw file from the subjob analysis’ stage.

gen_normgrid_data()¶: Generate normalized occupancy data

write_cns_mae_files() → List[str]¶

Write CNS and Maestro files for each probe type.

Returns: List of cns files for each probe

write_hotspot_files() → Tuple[List[str], List[str], List[str]]¶: Write CNS files and return a list of cns and mae filenames with comments about hotspot details.

check_ligand_overlap(hotspots: List[schrodinger.application.desmond.mxmd.mxmd_cleanup.Hotspot])¶

set_ref_ct()¶: Read one of the output structures and extract the original input coordinates.

write_maestro_project()¶: Write a maestro prj table containing a summary of the results.

prepare_ct(ct: schrodinger.structure._structure.Structure, probe: str = '')¶

Change structure title and remove trajectory and hierarchy info.

Parameters

ct – Structure to modify.
probe – If specified, the name of the probe. Otherwise use the jobname. This is the default.

schrodinger.application.desmond.mxmd.mxmd_cleanup.parse_cmd(cmdline: List[str]) → argparse.Namespace¶

schrodinger.application.desmond.mxmd.mxmd_cleanup.main(cmdline=None)¶