pyKVFinder.run_workflow

Detects and characterizes cavities (volume, area, depth [optional], hydropathy [optional] and interface residues).

Parameters:

input (Union[str, pathlib.Path]) – A path to a target structure file, in PDB or XYZ format, to detect and characterize cavities.
ligand (Union[str, pathlib.Path], optional) – A path to ligand file, in PDB or XYZ format, by default None.
vdw (Union[str, pathlib.Path], optional) – A path to a van der Waals radii file, by default None. If None, apply the built-in van der Waals radii file: vdw.dat.
box (Union[str, pathlib.Path], optional) – A path to box configuration file (TOML-formatted), by default None.
step (Union[float, int], optional) – Grid spacing (A), by default 0.6.
probe_in (Union[float, int], optional) – Probe In size (A), by default 1.4.
probe_out (Union[float, int], optional) – Probe Out size (A), by default 4.0.
removal_distance (Union[float, int], optional) – Length to be removed from the cavity-bulk frontier (A), by default 2.4.
volume_cutoff (Union[float, int], optional) – Cavities volume filter (A3), by default 5.0.
ligand_cutoff (Union[float, int], optional) – Radius value to limit a space around a ligand (A), by default 5.0.
include_depth (bool, optional) – Whether to characterize the depth of the detected cavities, by default False.
include_hydropathy (bool, optional) – Whether to characterize the hydropathy of the detected cavities, by default False.
hydrophobicity_scale (Union[str, pathlib.Path], optional) – Name of a built-in hydrophobicity scale (EisenbergWeiss, HessaHeijne, KyteDoolitte, MoonFleming, RadzickaWolfenden, WimleyWhite, ZhaoLondon) or a path to a TOML-formatted file with a custom hydrophobicity scale, by default EisenbergWeiss.
surface (str, optional) – Keywords options are SES (Solvent Excluded Surface) or SAS (Solvent Accessible Surface), by default SES.
ignore_backbone (bool, optional) – Whether to ignore backbone atoms (C, CA, N, O) when defining interface residues, by default False.
model (int, optional) – Model number, by default None. If None, keep atoms from all models.
nthreads (int, optional) – Number of threads, by default None. If None, the number of threads is os.cpu_count() - 1.
verbose (bool, optional) – Print extra information to standard output, by default False.

Returns:

results –

A class with the following attributes defined:

cavities : numpy.ndarray

Cavity points in the 3D grid (cavities[nx][ny][nz]). Cavities array has integer labels in each position, that are:

-1: bulk points;

0: biomolecule points;

1: empty space points;

>=2: cavity points.

The empty space points are regions that do not meet the chosen volume cutoff to be considered a cavity.

surface : numpy.ndarray

Surface points in the 3D grid (surface[nx][ny][nz]). Surface array has integer labels in each position, that are:

-1: bulk points;

0: biomolecule or empty space points;

>=2: surface points.

The empty space points are regions that do not meet the chosen volume cutoff to be considered a cavity.

depths : numpy.ndarray, optional

A numpy.ndarray with depth of cavity points (depth[nx][ny][nz]).

scales : numpy.ndarray, optional

A numpy.ndarray with hydrophobicity scale value mapped at surface points (scales[nx][ny][nz]).

ncav : int

Number of cavities.

volume : Dict[str, float]

A dictionary with volume of each detected cavity.

area : Dict[str, float]

A dictionary with area of each detected cavity.

max_depth : Dict[str, float], optional

A dictionary with maximum depth of each detected cavity.

avg_depth : Dict[str, float], optional

A dictionary with average depth of each detected cavity.

avg_hydropathy : Dict[str, float], optional

A dictionary with average hydropathy for each detected cavity and the range of the hydrophobicity scale (min, max).

residues: Dict[str, List[List[str]]]

A dictionary with a list of interface residues for each detected cavity.

frequencies : Dict[str, Dict[str, Dict[str, int]]], optional

A dictionary with frequencies of residues and class for residues of each detected cavity.

_vertices : numpy.ndarray

A numpy.ndarray or a list with xyz vertices coordinates (origin, X-axis, Y-axis, Z-axis).

_step : float

Grid spacing (A).

_input : Union[str, pathlib.Path], optional

A path to input PDB or XYZ file.

_ligand : Union[str, pathlib.Path], optional

A path to ligand PDB or XYZ file.

Return type:

pyKVFinderResults

Raises:

TypeError – input must have .pdb or .xyz extension.
TypeError – ligand must have .pdb or .xyz extension.

Note

The cavity nomenclature is based on the integer label. The cavity marked with 2, the first integer corresponding to a cavity, is KAA, the cavity marked with 3 is KAB, the cavity marked with 4 is KAC and so on.

Note

The classes of residues are:

Aliphatic apolar (R1): Alanine, Glycine, Isoleucine, Leucine, Methionine, Valine.
Aromatic (R2): Phenylalanine, Tryptophan, Tyrosine.
Polar Uncharged (R3): Asparagine, Cysteine, Glutamine, Proline, Serine, Threonine.
Negatively charged (R4): Aspartate, Glutamate.
Positively charged (R5): Arginine, Histidine, Lysine.
Non-standard (RX): Non-standard residues.

See also

pyKVFinderResults

Example

The standard workflow for cavity detection with spatial (surface points, volume, area) and constitutional (interface residues and their frequencies) characterization can be run at once with one command:

>>> import os
>>> import pyKVFinder
>>> pdb = os.path.join(os.path.dirname(pyKVFinder.__file__), 'data', 'tests', '1FMO.pdb')
>>> results = pyKVFinder.run_workflow(pdb)
>>> results
<pyKVFinderResults object>
>>> results.cavities
array([[[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]],
       ...,
       [[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int32)
>>> results.surface
array([[[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]],
       ...,
       [[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int32)
>>> results.ncav
>>> 18
>>> results.volume
{'KAA': 137.16, 'KAB': 47.52, 'KAC': 66.96, 'KAD': 8.21, 'KAE': 43.63, 'KAF': 12.53, 'KAG': 6.26, 'KAH': 520.13, 'KAI': 12.31, 'KAJ': 26.57, 'KAK': 12.31, 'KAL': 33.91, 'KAM': 23.11, 'KAN': 102.82, 'KAO': 6.05, 'KAP': 15.55, 'KAQ': 7.99, 'KAR': 7.78}
>>> results.area
{'KAA': 126.41, 'KAB': 62.37, 'KAC': 74.57, 'KAD': 19.06, 'KAE': 57.08, 'KAF': 22.77, 'KAG': 15.38, 'KAH': 496.97, 'KAI': 30.58, 'KAJ': 45.64, 'KAK': 30.58, 'KAL': 45.58, 'KAM': 45.25, 'KAN': 129.77, 'KAO': 12.28, 'KAP': 25.04, 'KAQ': 13.46, 'KAR': 16.6}
>>> results.residues
{'KAA': [['14', 'E', 'SER'], ['15', 'E', 'VAL'], ['18', 'E', 'PHE'], ['19', 'E', 'LEU'], ['100', 'E', 'PHE'], ['152', 'E', 'LEU'], ['155', 'E', 'GLU'], ['156', 'E', 'TYR'], ['292', 'E', 'LYS'], ['302', 'E', 'TRP'], ['303', 'E', 'ILE'], ['306', 'E', 'TYR']], 'KAB': [['18', 'E', 'PHE'], ['22', 'E', 'ALA'], ['25', 'E', 'ASP'], ['26', 'E', 'PHE'], ['29', 'E', 'LYS'], ['97', 'E', 'ALA'], ['98', 'E', 'VAL'], ['99', 'E', 'ASN'], ['156', 'E', 'TYR']], 'KAC': [['141', 'E', 'PRO'], ['142', 'E', 'HIS'], ['144', 'E', 'ARG'], ['145', 'E', 'PHE'], ['148', 'E', 'ALA'], ['299', 'E', 'THR'], ['300', 'E', 'THR'], ['305', 'E', 'ILE'], ['310', 'E', 'VAL'], ['311', 'E', 'GLU'], ['313', 'E', 'PRO']], 'KAD': [['122', 'E', 'TYR'], ['124', 'E', 'ALA'], ['176', 'E', 'GLN'], ['318', 'E', 'PHE'], ['320', 'E', 'GLY'], ['321', 'E', 'PRO'], ['322', 'E', 'GLY'], ['323', 'E', 'ASP']], 'KAE': [['95', 'E', 'LEU'], ['98', 'E', 'VAL'], ['99', 'E', 'ASN'], ['100', 'E', 'PHE'], ['103', 'E', 'LEU'], ['104', 'E', 'VAL'], ['105', 'E', 'LYS'], ['106', 'E', 'LEU']], 'KAF': [['123', 'E', 'VAL'], ['124', 'E', 'ALA'], ['175', 'E', 'ASP'], ['176', 'E', 'GLN'], ['181', 'E', 'GLN']], 'KAG': [['34', 'E', 'SER'], ['37', 'E', 'THR'], ['96', 'E', 'GLN'], ['106', 'E', 'LEU'], ['107', 'E', 'GLU'], ['108', 'E', 'PHE'], ['109', 'E', 'SER']], 'KAH': [['49', 'E', 'LEU'], ['50', 'E', 'GLY'], ['51', 'E', 'THR'], ['52', 'E', 'GLY'], ['53', 'E', 'SER'], ['54', 'E', 'PHE'], ['55', 'E', 'GLY'], ['56', 'E', 'ARG'], ['57', 'E', 'VAL'], ['70', 'E', 'ALA'], ['72', 'E', 'LYS'], ['74', 'E', 'LEU'], ['84', 'E', 'GLN'], ['87', 'E', 'HIS'], ['88', 'E', 'THR'], ['91', 'E', 'GLU'], ['104', 'E', 'VAL'], ['120', 'E', 'MET'], ['121', 'E', 'GLU'], ['122', 'E', 'TYR'], ['123', 'E', 'VAL'], ['127', 'E', 'GLU'], ['166', 'E', 'ASP'], ['168', 'E', 'LYS'], ['170', 'E', 'GLU'], ['171', 'E', 'ASN'], ['173', 'E', 'LEU'], ['183', 'E', 'THR'], ['184', 'E', 'ASP'], ['186', 'E', 'GLY'], ['187', 'E', 'PHE'], ['201', 'E', 'THR'], ['327', 'E', 'PHE']], 'KAI': [['131', 'E', 'HIS'], ['138', 'E', 'PHE'], ['142', 'E', 'HIS'], ['146', 'E', 'TYR'], ['174', 'E', 'ILE'], ['314', 'E', 'PHE']], 'KAJ': [['33', 'E', 'PRO'], ['89', 'E', 'LEU'], ['92', 'E', 'LYS'], ['93', 'E', 'ARG'], ['96', 'E', 'GLN'], ['349', 'E', 'GLU'], ['350', 'E', 'PHE']], 'KAK': [['157', 'E', 'LEU'], ['162', 'E', 'LEU'], ['163', 'E', 'ILE'], ['164', 'E', 'TYR'], ['185', 'E', 'PHE'], ['188', 'E', 'ALA']], 'KAL': [['49', 'E', 'LEU'], ['127', 'E', 'GLU'], ['129', 'E', 'PHE'], ['130', 'E', 'SER'], ['326', 'E', 'ASN'], ['327', 'E', 'PHE'], ['328', 'E', 'ASP'], ['330', 'E', 'TYR']], 'KAM': [['51', 'E', 'THR'], ['55', 'E', 'GLY'], ['56', 'E', 'ARG'], ['73', 'E', 'ILE'], ['74', 'E', 'LEU'], ['75', 'E', 'ASP'], ['115', 'E', 'ASN'], ['335', 'E', 'ILE'], ['336', 'E', 'ARG']], 'KAN': [['165', 'E', 'ARG'], ['166', 'E', 'ASP'], ['167', 'E', 'LEU'], ['199', 'E', 'CYS'], ['200', 'E', 'GLY'], ['201', 'E', 'THR'], ['204', 'E', 'TYR'], ['205', 'E', 'LEU'], ['206', 'E', 'ALA'], ['209', 'E', 'ILE'], ['219', 'E', 'VAL'], ['220', 'E', 'ASP'], ['223', 'E', 'ALA']], 'KAO': [['48', 'E', 'THR'], ['51', 'E', 'THR'], ['56', 'E', 'ARG'], ['330', 'E', 'TYR'], ['331', 'E', 'GLU']], 'KAP': [['222', 'E', 'TRP'], ['238', 'E', 'PHE'], ['253', 'E', 'GLY'], ['254', 'E', 'LYS'], ['255', 'E', 'VAL'], ['273', 'E', 'LEU']], 'KAQ': [['207', 'E', 'PRO'], ['208', 'E', 'GLU'], ['211', 'E', 'LEU'], ['213', 'E', 'LYS'], ['275', 'E', 'VAL'], ['277', 'E', 'LEU']], 'KAR': [['237', 'E', 'PRO'], ['238', 'E', 'PHE'], ['249', 'E', 'LYS'], ['254', 'E', 'LYS'], ['255', 'E', 'VAL'], ['256', 'E', 'ARG']]}
>>> results.frequencies
{'KAA': {'RESIDUES': {'GLU': 1, 'ILE': 1, 'LEU': 2, 'LYS': 1, 'PHE': 2, 'SER': 1, 'TRP': 1, 'TYR': 2, 'VAL': 1}, 'CLASS': {'R1': 4, 'R2': 5, 'R3': 1, 'R4': 1, 'R5': 1, 'RX': 0}}, 'KAB': {'RESIDUES': {'ALA': 2, 'ASN': 1, 'ASP': 1, 'LYS': 1, 'PHE': 2, 'TYR': 1, 'VAL': 1}, 'CLASS': {'R1': 3, 'R2': 3, 'R3': 1, 'R4': 1, 'R5': 1, 'RX': 0}}, 'KAC': {'RESIDUES': {'ALA': 1, 'ARG': 1, 'GLU': 1, 'HIS': 1, 'ILE': 1, 'PHE': 1, 'PRO': 2, 'THR': 2, 'VAL': 1}, 'CLASS': {'R1': 5, 'R2': 1, 'R3': 2, 'R4': 1, 'R5': 2, 'RX': 0}}, 'KAD': {'RESIDUES': {'ALA': 1, 'ASP': 1, 'GLN': 1, 'GLY': 2, 'PHE': 1, 'PRO': 1, 'TYR': 1}, 'CLASS': {'R1': 4, 'R2': 2, 'R3': 1, 'R4': 1, 'R5': 0, 'RX': 0}}, 'KAE': {'RESIDUES': {'ASN': 1, 'LEU': 3, 'LYS': 1, 'PHE': 1, 'VAL': 2}, 'CLASS': {'R1': 5, 'R2': 1, 'R3': 1, 'R4': 0, 'R5': 1, 'RX': 0}}, 'KAF': {'RESIDUES': {'ALA': 1, 'ASP': 1, 'GLN': 2, 'VAL': 1}, 'CLASS': {'R1': 2, 'R2': 0, 'R3': 2, 'R4': 1, 'R5': 0, 'RX': 0}}, 'KAG': {'RESIDUES': {'GLN': 1, 'GLU': 1, 'LEU': 1, 'PHE': 1, 'SER': 2, 'THR': 1}, 'CLASS': {'R1': 1, 'R2': 1, 'R3': 4, 'R4': 1, 'R5': 0, 'RX': 0}}, 'KAH': {'RESIDUES': {'ALA': 1, 'ARG': 1, 'ASN': 1, 'ASP': 2, 'GLN': 1, 'GLU': 4, 'GLY': 4, 'HIS': 1, 'LEU': 3, 'LYS': 2, 'MET': 1, 'PHE': 3, 'SER': 1, 'THR': 4, 'TYR': 1, 'VAL': 3}, 'CLASS': {'R1': 11, 'R2': 4, 'R3': 8, 'R4': 6, 'R5': 4, 'RX': 0}}, 'KAI': {'RESIDUES': {'HIS': 2, 'ILE': 1, 'PHE': 2, 'TYR': 1}, 'CLASS': {'R1': 1, 'R2': 3, 'R3': 0, 'R4': 0, 'R5': 2, 'RX': 0}}, 'KAJ': {'RESIDUES': {'ARG': 1, 'GLN': 1, 'GLU': 1, 'LEU': 1, 'LYS': 1, 'PHE': 1, 'PRO': 1}, 'CLASS': {'R1': 2, 'R2': 1, 'R3': 1, 'R4': 1, 'R5': 2, 'RX': 0}}, 'KAK': {'RESIDUES': {'ALA': 1, 'ILE': 1, 'LEU': 2, 'PHE': 1, 'TYR': 1}, 'CLASS': {'R1': 4, 'R2': 2, 'R3': 0, 'R4': 0, 'R5': 0, 'RX': 0}}, 'KAL': {'RESIDUES': {'ASN': 1, 'ASP': 1, 'GLU': 1, 'LEU': 1, 'PHE': 2, 'SER': 1, 'TYR': 1}, 'CLASS': {'R1': 1, 'R2': 3, 'R3': 2, 'R4': 2, 'R5': 0, 'RX': 0}}, 'KAM': {'RESIDUES': {'ARG': 2, 'ASN': 1, 'ASP': 1, 'GLY': 1, 'ILE': 2, 'LEU': 1, 'THR': 1}, 'CLASS': {'R1': 4, 'R2': 0, 'R3': 2, 'R4': 1, 'R5': 2, 'RX': 0}}, 'KAN': {'RESIDUES': {'ALA': 2, 'ARG': 1, 'ASP': 2, 'CYS': 1, 'GLY': 1, 'ILE': 1, 'LEU': 2, 'THR': 1, 'TYR': 1, 'VAL': 1}, 'CLASS': {'R1': 7, 'R2': 1, 'R3': 2, 'R4': 2, 'R5': 1, 'RX': 0}}, 'KAO': {'RESIDUES': {'ARG': 1, 'GLU': 1, 'THR': 2, 'TYR': 1}, 'CLASS': {'R1': 0, 'R2': 1, 'R3': 2, 'R4': 1, 'R5': 1, 'RX': 0}}, 'KAP': {'RESIDUES': {'GLY': 1, 'LEU': 1, 'LYS': 1, 'PHE': 1, 'TRP': 1, 'VAL': 1}, 'CLASS': {'R1': 3, 'R2': 2, 'R3': 0, 'R4': 0, 'R5': 1, 'RX': 0}}, 'KAQ': {'RESIDUES': {'GLU': 1, 'LEU': 2, 'LYS': 1, 'PRO': 1, 'VAL': 1}, 'CLASS': {'R1': 4, 'R2': 0, 'R3': 0, 'R4': 1, 'R5': 1, 'RX': 0}}, 'KAR': {'RESIDUES': {'ARG': 1, 'LYS': 2, 'PHE': 1, 'PRO': 1, 'VAL': 1}, 'CLASS': {'R1': 2, 'R2': 1, 'R3': 0, 'R4': 0, 'R5': 3, 'RX': 0}}}

However, users may opt to perform cavity detection in a segmented space through ligand adjustment and/or box adjustment modes.

The cavity detection can be limited around the target ligand(s), which will be passed to pyKVFinder through a .pdb or a .xyz files. Thus, the detected cavities are limited within a radius (ligand_cutoff) of the target ligand(s).

>>> ligand = os.path.join(os.path.dirname(pyKVFinder.__file__), 'data', 'tests', 'ADN.pdb')
>>> results = pyKVFinder.run_workflow(pdb, ligand)
>>> results
<pyKVFinderResults object>
>>> results.cavities
array([[[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]],
       ...,
       [[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int32)
>>> results.surface
array([[[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]],
       ...,
       [[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int32)
>>> results.ncav
>>> 18
>>> results.volume
{'KAA': 365.04, 'KAB': 16.85}
>>> results.area
{'KAA': 328.79, 'KAB': 23.15}
>>> results.residues
{'KAA': [['49', 'E', 'LEU'], ['50', 'E', 'GLY'], ['51', 'E', 'THR'], ['52', 'E', 'GLY'], ['53', 'E', 'SER'], ['55', 'E', 'GLY'], ['56', 'E', 'ARG'], ['57', 'E', 'VAL'], ['70', 'E', 'ALA'], ['72', 'E', 'LYS'], ['104', 'E', 'VAL'], ['120', 'E', 'MET'], ['121', 'E', 'GLU'], ['122', 'E', 'TYR'], ['123', 'E', 'VAL'], ['127', 'E', 'GLU'], ['166', 'E', 'ASP'], ['168', 'E', 'LYS'], ['170', 'E', 'GLU'], ['171', 'E', 'ASN'], ['173', 'E', 'LEU'], ['183', 'E', 'THR'], ['184', 'E', 'ASP'], ['327', 'E', 'PHE']], 'KAB': [['49', 'E', 'LEU'], ['127', 'E', 'GLU'], ['130', 'E', 'SER'], ['326', 'E', 'ASN'], ['327', 'E', 'PHE'], ['328', 'E', 'ASP'], ['330', 'E', 'TYR']]}
>>> results.frequencies
{'KAA': {'RESIDUES': {'ALA': 1, 'ARG': 1, 'ASN': 1, 'ASP': 2, 'GLU': 3, 'GLY': 3, 'LEU': 2, 'LYS': 2, 'MET': 1, 'PHE': 1, 'SER': 1, 'THR': 2, 'TYR': 1, 'VAL': 3}, 'CLASS': {'R1': 9, 'R2': 2, 'R3': 5, 'R4': 5, 'R5': 3, 'RX': 0}}, 'KAB': {'RESIDUES': {'ASN': 1, 'ASP': 1, 'GLU': 1, 'LEU': 1, 'PHE': 1, 'SER': 1, 'TYR': 1}, 'CLASS': {'R1': 1, 'R2': 2, 'R3': 2, 'R4': 2, 'R5': 0, 'RX': 0}}}

Further, we can also perform cavity detection on a custom 3D grid, where we can explore closed regions with a custom box, which can be defined by a .toml file (see Box configuration file template).

>>> fn = os.path.join(os.path.dirname(pyKVFinder.__file__), 'data', 'tests', 'custom-box.toml')
>>> with open(fn, 'r') as f:
...     print(f.read())
[box]
p1 = [3.11, 7.34, 1.59]
p2 = [11.51, 7.34, 1.59]
p3 = [3.11, 10.74, 1.59]
p4 = [3.11, 7.34, 6.19]
>>> results = pyKVFinder.run_workflow(pdb, box=fn)
>>> results
<pyKVFinderResults object>
>>> results.cavities
array([[[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]],
       ...,
       [[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int32)
>>> results.surface
array([[[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]],
       ...,
       [[-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        ...,
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1],
        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int32)
>>> results.ncav
>>> 1
>>> results.volume
{'KAA': 115.78}
>>> results.area
{'KAA': 33.91}
>>> results.residues
{'KAA': [['49', 'E', 'LEU'], ['50', 'E', 'GLY'], ['51', 'E', 'THR'], ['57', 'E', 'VAL'], ['70', 'E', 'ALA'], ['104', 'E', 'VAL'], ['121', 'E', 'GLU'], ['122', 'E', 'TYR'], ['123', 'E', 'VAL'], ['127', 'E', 'GLU'], ['170', 'E', 'GLU'], ['171', 'E', 'ASN'], ['173', 'E', 'LEU'], ['183', 'E', 'THR'], ['327', 'E', 'PHE']]}
>>> results.frequencies
{'KAA': {'RESIDUES': {'ALA': 1, 'ASN': 1, 'GLU': 3, 'GLY': 1, 'LEU': 2, 'PHE': 1, 'THR': 2, 'TYR': 1, 'VAL': 3}, 'CLASS': {'R1': 7, 'R2': 2, 'R3': 3, 'R4': 3, 'R5': 0, 'RX': 0}}}

However, users may opt to perform the full workflow for cavity detection with spatial (surface points, volume and area), constitutional (interface residues and their frequencies), hydropathy and depth characterization. This full workflow can be run with one command by setting some parameters of run_workflow function:

>>> results = pyKVFinder.run_workflow(pdb, include_depth=True, include_hydropathy=True, hydrophobicity_scale='EisenbergWeiss')
>>> results.depths
array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],
       ...,
       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]])
>>> results.scales
array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],
       ...,
       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]])
>>> results.avg_depth
{'KAA': 1.35, 'KAB': 0.91, 'KAC': 0.68, 'KAD': 0.32, 'KAE': 0.99, 'KAF': 0.24, 'KAG': 0.1, 'KAH': 3.91, 'KAI': 0.0, 'KAJ': 0.96, 'KAK': 0.0, 'KAL': 1.07, 'KAM': 0.24, 'KAN': 0.0, 'KAO': 0.29, 'KAP': 0.7, 'KAQ': 0.22, 'KAR': 0.12}
>>> results.max_depth
{'KAA': 3.79, 'KAB': 2.68, 'KAC': 2.62, 'KAD': 0.85, 'KAE': 3.0, 'KAF': 0.85, 'KAG': 0.6, 'KAH': 10.73, 'KAI': 0.0, 'KAJ': 2.24, 'KAK': 0.0, 'KAL': 3.0, 'KAM': 1.2, 'KAN': 0.0, 'KAO': 1.04, 'KAP': 2.08, 'KAQ': 0.85, 'KAR': 0.6}
>>> results.avg_hydropathy
{'KAA': -0.73, 'KAB': -0.05, 'KAC': -0.07, 'KAD': -0.62, 'KAE': -0.81, 'KAF': -0.14, 'KAG': -0.33, 'KAH': -0.17, 'KAI': -0.4, 'KAJ': 0.62, 'KAK': -0.99, 'KAL': 0.36, 'KAM': -0.33, 'KAN': 0.18, 'KAO': 0.88, 'KAP': -0.96, 'KAQ': 0.48, 'KAR': 0.24, 'EisenbergWeiss': [-1.42, 2.6]}