Hydrogen Bond Statistics

This module enables Hydrogen Bond geometries assessment based on CSD statistics. The model encapsulates information regarding the number of hydrogen bond interactions present in the crystal structures and based on statistical information from CSD. It provides information about hydrogen bond geometries, including assessment of whether the bond distance and angles are usual or not unusual. To run it we will import the appropriate module:

>>> from ccdc import io
>>> from ccdc.interaction import HydrogenBondStatisticsSearch

Basic operation

First, let us create an ccdc.interaction.HydrogenBondStatisticsSearch instance:

>>> hbs = HydrogenBondStatisticsSearch()

The first thing to do is to set a target crystal; here we have chosen ADRNL which is the refcode of the skeleton molecule in Adrenaline Adrenaline, a hormone and medication which is involved in regulating visceral functions.

>>> csd = io.EntryReader('CSD')
>>> myentry = csd.entry('ADRENL')

Hydrogen Bond interactions search

Having set a target, the ccdc.interaction.HydrogenBondStatisticsSearch now has a search attribute ccdc.interaction.HydrogenBondStatisticsSearch.search from which the number of hydrogen bond interactions can be called. Let’s now print the amount of hydrogen bond interactions found during the HBS search.

>>> stats = hbs.search(myentry)
>>> print('Number of Hydrogen bonds identified:', len(stats))
Number of Hydrogen bonds identified: 4

The statistical information for hydrogen interactions can be inspected using the various attributes under search function. For example we can retrieve the list of hydrogen bonds and the functional groups used in the search.

>>> for hb in stats:
...     print(f'Donor: {hb.donor_atom} Acceptor: {hb.acceptor_atom} Acceptor functional group: {hb.acceptor_functional_group_name} Donor functional group: {hb.donor_functional_group_name}')
Donor: Atom(N1) Acceptor: Atom(O1) Acceptor functional group: ar_CsingleO Donor functional group: al_sec_ammonium
Donor: Atom(N1) Acceptor: Atom(O1) Acceptor functional group: ar_CsingleO Donor functional group: al_sec_ammonium
Donor: Atom(O3) Acceptor: Atom(O1) Acceptor functional group: ar_CsingleO Donor functional group: al_hydroxy_2
Donor: Atom(O2) Acceptor: Atom(O3) Acceptor functional group: al_hydroxy_2 Donor functional group: ar_oh

Other attributes that can be called are the distance between the donor and acceptor groups, the angle between D-H…A as well as an assessment for both distance and angle observations using distance_unusual and angle_unusual.

>>> for hb in stats:
...     print(f'Donor: {hb.donor_atom} Acceptor: {hb.acceptor_atom} Distance D-A: {round(hb.distance, 2)} Angle D-H…A: {round(hb.angle, 2)} Distance unusual: {hb.distance_unusual} Angle unusual: {hb.angle_unusual}')
Donor: Atom(N1) Acceptor: Atom(O1) Distance D-A: 2.73 Angle D-H…A: 164.95 Distance unusual: False Angle unusual: False
Donor: Atom(N1) Acceptor: Atom(O1) Distance D-A: 2.7 Angle D-H…A: 162.55 Distance unusual: False Angle unusual: False
Donor: Atom(O3) Acceptor: Atom(O1) Distance D-A: 2.66 Angle D-H…A: 159.22 Distance unusual: False Angle unusual: False
Donor: Atom(O2) Acceptor: Atom(O3) Distance D-A: 2.81 Angle D-H…A: 157.63 Distance unusual: False Angle unusual: False

Statistical information can also be called for a specific interaction in the list. Here is an example of how information for the third hydrogen bond interaction for ADRNL can be called.

>>> hb = stats[3]
>>> print('Acceptor:', hb.acceptor_atom, 'Donor:', hb.donor_atom)
Acceptor: Atom(O3) Donor: Atom(O2)
>>> print('Acceptor functional group:', hb.acceptor_functional_group_name, 'Donor functional group:', hb.donor_functional_group_name)
Acceptor functional group: al_hydroxy_2 Donor functional group: ar_oh
>>> print('Distance D-A:', round(hb.distance, 2), 'Angle D-H…A:', round(hb.angle, 2))
Distance D-A: 2.81 Angle D-H…A: 157.63
>>> print('Distance unusual:', hb.distance_unusual, 'Angle unusual:', hb.distance_unusual)
Distance unusual: False Angle unusual: False
>>> print('Number of observed hits:', len(hb.observations))
Number of observed hits: 132

The entries used for each interaction can be called using the observations attribute, here we call the first five observed hits for the third interaction.

>>> for ob in hb.observations[:5]:
...    print(ob.identifier)
ADRENL
ADRTAR
ADRTAR
ANUVEM
ASOTIM