Explore the data structure of a dynophore

A dynophore (defined in the Dynophore class) is a collection of so-called superfeatures (defined in the SuperFeature class). A superfeature is defined as a pharmacophore feature on ligand site (defined by a feature type, e.g. HBA, and one or more ligand atom numbers/serials) that occurs at least once during and MD simulation.
A superfeature can have one or more interaction partner(s) on macromolecule-side. These interaction partners are called environmental partners (defined in the EnvPartner class).
Each superfeature is described in 3D with a chemical feature point cloud (ChemicalFeatureCloud3D class).
Additionally, the dynophore contains information about the bound ligand in the Ligand class.

In this notebook, we will explore the Dynophore, Ligand, SuperFeature, EnvPartner, and ChemicalFeatureCloud3D classes.

[1]:

%load_ext autoreload
%autoreload 2

[2]:

from pathlib import Path
import logging

from dynophores import Dynophore

[3]:

logger = logging.getLogger("dynophores")
logger.setLevel(logging.DEBUG)

Set path to `DynophoreApp` output data folder

[4]:

DATA = Path("../../dynophores/tests/data")
dyno_path = DATA / "out"

Load data as `Dynophore` object

[5]:

dynophore = Dynophore.from_dir(dyno_path)

List object attributes

`Dynophore` object attributes

[6]:

dynophore.__dict__

[6]:

{'id': 'dynophore_1KE7',
 'ligand': <dynophores.core.ligand.Ligand at 0x7efe50678d90>,
 'superfeatures': {'HBA[4618]': <dynophores.core.superfeature.SuperFeature at 0x7efe50673cd0>,
  'AR[4605,4607,4603,4606,4604]': <dynophores.core.superfeature.SuperFeature at 0x7efe506202e0>,
  'HBD[4598]': <dynophores.core.superfeature.SuperFeature at 0x7efe50620490>,
  'HBA[4606]': <dynophores.core.superfeature.SuperFeature at 0x7efe506203d0>,
  'AR[4622,4615,4623,4613,4614,4621]': <dynophores.core.superfeature.SuperFeature at 0x7efdee98ce50>,
  'HBD[4612]': <dynophores.core.superfeature.SuperFeature at 0x7efe40bd4730>,
  'HBA[4619]': <dynophores.core.superfeature.SuperFeature at 0x7efe40bd57f0>,
  'HBA[4596]': <dynophores.core.superfeature.SuperFeature at 0x7efe40bd77c0>,
  'H[4615,4623,4622,4613,4621,4614]': <dynophores.core.superfeature.SuperFeature at 0x7efe40f19880>,
  'H[4599,4602,4601,4608,4609,4600]': <dynophores.core.superfeature.SuperFeature at 0x7efe40efe220>}}

[7]:

dynophore.__dict__.keys()
# NBVAL_CHECK_OUTPUT

[7]:

dict_keys(['id', 'ligand', 'superfeatures'])

A Dynophore object contains:

id: Dynophore identifier (name)
ligand: Ligand information
superfeatures: Superfeature data (SuperFeature objects)

[8]:

print(f"Number of superfeatures: {len(dynophore.superfeatures)}")
# NBVAL_CHECK_OUTPUT

Number of superfeatures: 10

`Ligand` object attributes

[11]:

dynophore.ligand.__dict__.keys()
# NBVAL_CHECK_OUTPUT

[11]:

dict_keys(['name', 'smiles', 'mdl_mol_buffer', 'atom_serials'])

[12]:

dynophore.ligand.name
# NBVAL_CHECK_OUTPUT

[12]:

'LS3'

[13]:

dynophore.ligand.smiles
# NBVAL_CHECK_OUTPUT

[13]:

'S5(=O)(=O)CC4C(=CC=C(NC=C3C(=O)NC2C3=CC(C1OC=NC=1)=CC=2)C=4)C5'

[15]:

print(dynophore.ligand.atom_serials)
# NBVAL_CHECK_OUTPUT

[4617, 4604, 4618, 4619, 4596, 4606, 4598, 4612, 4605, 4603, 4597, 4599, 4607, 4613, 4602, 4609, 4615, 4621, 4600, 4614, 4623, 4608, 4601, 4622, 4610, 4616, 4620, 4611, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]

Note: “-1” in case of atoms without serials (e.g. H atoms)

[19]:

dynophore.ligand.rdkit_molecule()

[19]:

../_images/tutorials_explore_data_20_0.png

`SuperFeature` object attributes

Let’s take a look at one example SuperFeature object.

[9]:

superfeature_id = list(dynophore.superfeatures.keys())[0]
superfeature_id

[9]:

'HBA[4618]'

[10]:

dynophore.superfeatures

[10]:

{'HBA[4618]': <dynophores.core.superfeature.SuperFeature at 0x7f050f639f10>,
 'AR[4605,4607,4603,4606,4604]': <dynophores.core.superfeature.SuperFeature at 0x7f050f639dc0>,
 'HBD[4598]': <dynophores.core.superfeature.SuperFeature at 0x7f055986d040>,
 'HBA[4606]': <dynophores.core.superfeature.SuperFeature at 0x7f055986d190>,
 'AR[4622,4615,4623,4613,4614,4621]': <dynophores.core.superfeature.SuperFeature at 0x7f055986d640>,
 'HBD[4612]': <dynophores.core.superfeature.SuperFeature at 0x7f055986dee0>,
 'HBA[4619]': <dynophores.core.superfeature.SuperFeature at 0x7f055986efa0>,
 'HBA[4596]': <dynophores.core.superfeature.SuperFeature at 0x7f0559871f70>,
 'H[4615,4623,4622,4613,4621,4614]': <dynophores.core.superfeature.SuperFeature at 0x7f0559b72070>,
 'H[4599,4602,4601,4608,4609,4600]': <dynophores.core.superfeature.SuperFeature at 0x7f0559b9c9d0>}

[11]:

dynophore.superfeatures[superfeature_id].__dict__.keys()
# NBVAL_CHECK_OUTPUT

[11]:

dict_keys(['id', 'feature_type', 'atom_numbers', 'occurrences', 'envpartners', 'color', 'cloud'])

A SuperFeature object contains:

id: Superfeature identifier (nomenclature: <feature_type><list of atom numbers>)
feature_type: Feature type (e.g. HBA, HBD, H, AR, …)
atom_numbers: Number(s) of ligand atom(s) that are involved in feature
occurrences: Superfeature occurrences during an MD simulation (0/1 for absent/present)
envpartners: Environmental partners on the macromolecule-side that involved in the superfeature (either at the same time or not)
cloud: Chemical feature cloud in 3D (coordinates of each occurring feature during an MD simulation that belongs to the superfeature)

[12]:

n_envpartners = sum(
    [len(superfeature.envpartners) for _, superfeature in dynophore.superfeatures.items()]
)
print(f"Number of environmental partners: {n_envpartners}")
# NBVAL_CHECK_OUTPUT

Number of environmental partners: 28

`EnvPartner` object attributes

Let’s take a look at one example EnvPartner object.

[13]:

envpartner_id = list(dynophore.superfeatures[superfeature_id].envpartners.keys())[0]
envpartner_id

[13]:

'LYS-20-A[316]'

[14]:

dynophore.superfeatures[superfeature_id].envpartners[envpartner_id].__dict__.keys()
# NBVAL_CHECK_OUTPUT

[14]:

dict_keys(['id', 'residue_name', 'residue_number', 'chain', 'atom_numbers', 'occurrences', 'distances'])

A EnvPartner object contains:

id: environmental partner identifier (nomenclature: <residue name>-<residue number>-<chain><list of atom numbers>)
residue_name: residue name
residue_number: residue number
chain: chain ID
atom_numbers: number(s) of residue atom(s) that are involved in feature
occurrences: interaction occurrences during an MD (0/1 for absent/present) between ligand and residue atoms
distances: interaction distances between the involved atoms on ligand- and macromolecule-side during an MD

`ChemicalFeatureCloud3D` object attributes

[15]:

dynophore.superfeatures[superfeature_id].cloud.__dict__

[15]:

{'center': array([-18.507637 ,  -8.405735 ,   1.5362723]),
 'points': [<dynophores.core.chemicalfeaturecloud3dpoint.ChemicalFeatureCloud3DPoint at 0x7f050f639bb0>,
  <dynophores.core.chemicalfeaturecloud3dpoint.ChemicalFeatureCloud3DPoint at 0x7f050f639fd0>]}

[16]:

dynophore.superfeatures[superfeature_id].cloud.__dict__.keys()
# NBVAL_CHECK_OUTPUT

[16]:

dict_keys(['center', 'points'])

A ChemicalFeatureCloud3D object contains:

center: The coordinates of the geometric center of all points in the point cloud
points: The coordiantes of all points in the point cloud

Dynophore basics

Dynophore identifier

[17]:

print(f"Dynophore name: {dynophore.id}")

Dynophore name: dynophore_1KE7

Number of frames

[18]:

print(f"Number of MD simulation frames: {dynophore.n_frames}")

Number of MD simulation frames: 1002

Number of superfeatures

[19]:

print(f"Number of superfeatures: {dynophore.n_superfeatures}")

Number of superfeatures: 10

Superfeatures monitoring (over trajectory)

Superfeature occurrences

[20]:

dynophore.superfeatures_occurrences

[20]:

	H[4599,4602,4601,4608,4609,4600]	H[4615,4623,4622,4613,4621,4614]	HBA[4596]	HBA[4619]	HBD[4612]	AR[4622,4615,4623,4613,4614,4621]	HBA[4606]	HBD[4598]	HBA[4618]	AR[4605,4607,4603,4606,4604]
0	1	1	1	1	0	0	0	0	0	0
1	1	1	1	1	0	0	0	0	0	0
2	1	1	0	0	0	0	0	0	0	0
3	1	1	0	0	0	0	0	0	0	0
4	1	1	0	0	1	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...
997	1	1	1	0	0	0	0	0	0	0
998	1	1	1	0	0	0	0	0	0	0
999	1	1	1	0	0	0	0	0	0	0
1000	1	1	1	0	0	0	0	0	0	0
1001	1	1	1	0	0	0	0	0	0	0

1002 rows × 10 columns

Environmental partners monitoring (over trajectory)

Interaction occurrences for example superfeature

[21]:

dynophore.envpartners_occurrences_by_superfeature(superfeature_id)

[21]:

	LYS-20-A[316]
0	0
1	0
2	0
3	0
4	0
...	...
997	0
998	0
999	0
1000	0
1001	0

1002 rows × 1 columns

[22]:

dynophore.envpartners_occurrences.keys()

[22]:

dict_keys(['HBA[4618]', 'AR[4605,4607,4603,4606,4604]', 'HBD[4598]', 'HBA[4606]', 'AR[4622,4615,4623,4613,4614,4621]', 'HBD[4612]', 'HBA[4619]', 'HBA[4596]', 'H[4615,4623,4622,4613,4621,4614]', 'H[4599,4602,4601,4608,4609,4600]'])

Interaction distances for example superfeature

[23]:

dynophore.envpartners_distances_by_superfeature(superfeature_id)

[23]:

	LYS-20-A[316]
0	11.076880
1	11.076880
2	10.654148
3	9.423526
4	12.006125
...	...
997	4.339370
998	5.333941
999	5.336269
1000	4.156338
1001	6.207083

1002 rows × 1 columns

[24]:

dynophore.envpartners_distances.keys()

[24]:

dict_keys(['HBA[4618]', 'AR[4605,4607,4603,4606,4604]', 'HBD[4598]', 'HBA[4606]', 'AR[4622,4615,4623,4613,4614,4621]', 'HBD[4612]', 'HBA[4619]', 'HBA[4596]', 'H[4615,4623,4622,4613,4621,4614]', 'H[4599,4602,4601,4608,4609,4600]'])

Superfeatures vs. environmental partners

Occurrence count

[25]:

dynophore.count

[25]:

	HBA[4618]	AR[4605,4607,4603,4606,4604]	HBD[4598]	HBA[4606]	AR[4622,4615,4623,4613,4614,4621]	HBD[4612]	HBA[4619]	HBA[4596]	H[4615,4623,4622,4613,4621,4614]	H[4599,4602,4601,4608,4609,4600]
ALA-144-A[2263,2266]	0	0	0	0	0	0	0	0	0	992
ALA-31-A[488,491]	0	0	0	0	0	0	0	0	0	216
ASP-86-A[1313]	0	0	0	0	0	0	2	0	0	0
ASP-86-A[1319]	0	0	0	0	0	18	0	0	0	0
ASP-86-A[1320]	0	0	0	0	0	20	0	0	0	0
GLN-131-A[2057]	0	0	0	0	0	1	0	0	0	0
GLN-131-A[2061]	0	0	0	0	0	8	0	0	0	0
GLN-131-A[2062]	0	0	0	2	0	0	0	0	0	0
GLU-81-A[1228]	0	0	8	0	0	0	0	0	0	0
HIS-84-A[1284,1285,1286,1287,1288]	0	0	0	0	1	0	0	0	0	0
ILE-10-A[165]	0	0	0	0	1	0	116	0	0	0
ILE-10-A[169,171,172]	0	0	0	0	0	0	0	0	995	959
ILE-10-A[169,171]	0	0	0	0	0	0	0	0	945	972
LEU-134-A[2109,2110,2111]	0	0	0	0	0	0	0	0	0	999
LEU-83-A[1260]	0	0	0	0	0	0	0	862	0	0
LEU-83-A[1263]	0	0	2	0	0	33	0	0	0	0
LYS-129-A[2026]	0	1	0	18	0	0	0	0	0	0
LYS-20-A[308]	0	0	0	0	0	0	5	0	0	0
LYS-20-A[316]	2	0	0	0	0	0	3	0	0	0
LYS-89-A[1374]	0	0	0	0	38	0	0	0	0	0
PHE-82-A[1245,1246,1247,1248,1249,1250]	0	0	0	0	0	0	0	0	57	0
VAL-18-A[275,276,277]	0	0	0	0	0	0	0	0	0	955
any	2	1	10	20	40	80	123	862	1001	1002

Occurrence frequency

[26]:

dynophore.frequency

[26]:

	HBA[4618]	AR[4605,4607,4603,4606,4604]	HBD[4598]	HBA[4606]	AR[4622,4615,4623,4613,4614,4621]	HBD[4612]	HBA[4619]	HBA[4596]	H[4615,4623,4622,4613,4621,4614]	H[4599,4602,4601,4608,4609,4600]
ALA-144-A[2263,2266]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0.00	99.00
ALA-31-A[488,491]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0.00	21.56
ASP-86-A[1313]	0.0	0.0	0.0	0.0	0.00	0.00	0.20	0.00	0.00	0.00
ASP-86-A[1319]	0.0	0.0	0.0	0.0	0.00	1.80	0.00	0.00	0.00	0.00
ASP-86-A[1320]	0.0	0.0	0.0	0.0	0.00	2.00	0.00	0.00	0.00	0.00
GLN-131-A[2057]	0.0	0.0	0.0	0.0	0.00	0.10	0.00	0.00	0.00	0.00
GLN-131-A[2061]	0.0	0.0	0.0	0.0	0.00	0.80	0.00	0.00	0.00	0.00
GLN-131-A[2062]	0.0	0.0	0.0	0.2	0.00	0.00	0.00	0.00	0.00	0.00
GLU-81-A[1228]	0.0	0.0	0.8	0.0	0.00	0.00	0.00	0.00	0.00	0.00
HIS-84-A[1284,1285,1286,1287,1288]	0.0	0.0	0.0	0.0	0.10	0.00	0.00	0.00	0.00	0.00
ILE-10-A[165]	0.0	0.0	0.0	0.0	0.10	0.00	11.58	0.00	0.00	0.00
ILE-10-A[169,171,172]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	99.30	95.71
ILE-10-A[169,171]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	94.31	97.01
LEU-134-A[2109,2110,2111]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0.00	99.70
LEU-83-A[1260]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	86.03	0.00	0.00
LEU-83-A[1263]	0.0	0.0	0.2	0.0	0.00	3.29	0.00	0.00	0.00	0.00
LYS-129-A[2026]	0.0	0.1	0.0	1.8	0.00	0.00	0.00	0.00	0.00	0.00
LYS-20-A[308]	0.0	0.0	0.0	0.0	0.00	0.00	0.50	0.00	0.00	0.00
LYS-20-A[316]	0.2	0.0	0.0	0.0	0.00	0.00	0.30	0.00	0.00	0.00
LYS-89-A[1374]	0.0	0.0	0.0	0.0	3.79	0.00	0.00	0.00	0.00	0.00
PHE-82-A[1245,1246,1247,1248,1249,1250]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	5.69	0.00
VAL-18-A[275,276,277]	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0.00	95.31
any	0.2	0.1	1.0	2.0	3.99	7.98	12.28	86.03	99.90	100.00

Superfeature clouds

[27]:

dynophore.cloud_by_superfeature(superfeature_id)

[27]:

	x	y	z	frame_ix	weight
0	-18.598375	-8.370245	2.017743	959	1.0
1	-18.416897	-8.441224	1.054801	964	1.0

[28]:

dynophore.clouds.keys()

[28]:

dict_keys(['HBA[4618]', 'AR[4605,4607,4603,4606,4604]', 'HBD[4598]', 'HBA[4606]', 'AR[4622,4615,4623,4613,4614,4621]', 'HBD[4612]', 'HBA[4619]', 'HBA[4596]', 'H[4615,4623,4622,4613,4621,4614]', 'H[4599,4602,4601,4608,4609,4600]'])