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: project.Project, cns_file: str, row_index: int, cutoff: float, color: int, surf_name: str, surf_comment: str)

class schrodinger.application.desmond.mxmd.mxmd_cleanup.Spot(*, grid_points: Set[Tuple[float, float, float]], grid: numpy.ndarray, probe: str, grid_spacing: Tuple[float, float, float])

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 and their occupancy values.

__init__(*, grid_points: Set[Tuple[float, float, float]], grid: numpy.ndarray, probe: str, grid_spacing: Tuple[float, float, float])

Initialize self. See help(type(self)) for accurate signature.

volume

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])

Initialize self. See help(type(self)) for accurate signature.

add_spot(spot: schrodinger.application.desmond.mxmd.mxmd_cleanup.Spot)

spot must have the same grid_spacing

volume

score(func) → int

This function is how to treat occupancy value of overlapping grid points.

Parameters:func – can be either np.mean, np.max or np.sum or np.min

write_cns(filename: str, func: Callable = <function sum>)

Write hotspot grid to cns format. 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, probe_data: numpy.core.multiarray.array, grid_spacing: 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=3.0)

Bases: object

Class for cleaning up mixed solvent subjobs.

__init__(chkpt_file: str, sigma: float = 20.0, cluster_cutoff=3.0)

Initialize self. See help(type(self)) for accurate signature.

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_cns_hotspot_files() → Tuple[List[str], List[str]]

Write cns files and return a list of cns filenames with comments about hotspot volumes.

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, probe: str = '')

Change structure title and remove trajectory 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)