pytraj.nastruct(traj=None, ref=0, resrange=None, resmap=None, hbcut=3.5, frame_indices=None, pucker_method='altona', dtype='nupars', baseref=None, groove_3dna=True, top=None)¶compute nucleic acid parameters. (adapted from cpptraj doc)
| Parameters: | traj : Trajectory-like ref : {Frame, int}, default 0 (first frame) resrange : None, str or array-like of integers resmap : residue map, example: ‘AF2:A’ hbcut : float, default=3.5 Angstrong
pucker_method : str, {‘altona’, ‘cremer’}, default ‘altona’
frame_indices : array-like, default None (all frames) baseref : {None, str}, default None
groove_3dna : bool, default True
dtype : str, {‘nupars’, ‘cpptraj_dataset’}, default ‘nupars’ |
|---|---|
| Returns: | out : nupars object. One can assess different values (major groove width, xdips values ...) by accessing its attribute. See example below. |
Examples
>>> import pytraj as pt
>>> import numpy as np
>>> traj = pt.datafiles.load_rna()
>>> data = pt.nastruct(traj, groove_3dna=False)
>>> data.keys()[:5]
['buckle', 'minor', 'major', 'xdisp', 'stagger']
>>> # get minor groove width values for each pairs for each snapshot
>>> # data.minor is a tuple, first value is a list of basepairs, seconda value is
>>> # numpy array, shape=(n_frames, n_pairs)
>>> data.minor
(['1G16C', '2G15C', '3G14C', '4C13G', '5G12C', '6C11G', '7C10G', '8C9G'],
array([[ 13.32927036, 13.403409 , 13.57159901, ..., 13.26655865,
13.43054485, 13.4557209 ],
[ 13.32002068, 13.45918751, 13.63253593, ..., 13.27066231,
13.42743683, 13.53450871],
[ 13.34087658, 13.53778553, 13.57062435, ..., 13.29017353,
13.38542843, 13.46101475]]))
>>> data.twist
(['1G16C-2G15C', '2G15C-3G14C', '3G14C-4C13G', '4C13G-5G12C', '5G12C-6C11G', '6C11G-7C10G', '7C10G-8C9G'],
array([[ 34.77773666, 33.98158646, 30.18647003, ..., 35.14608765,
33.9628334 , 33.13056946],
[ 33.39176178, 32.68476105, 28.36385536, ..., 36.59774399,
30.20827484, 26.48732948],
[ 36.20665359, 32.58955002, 27.47707367, ..., 33.42843246,
30.90047073, 33.73724365]]))
>>> # change dtype
>>> data = pt.nastruct(traj, dtype='cpptraj_dataset')
In [1]: import pytraj as pt
In [2]: traj = pt.iterload('data/adh026.3.pdb')
In [3]: traj
Out[3]:
pytraj.TrajectoryIterator, 3 frames:
Size: 0.000035 (GB)
<Topology: 518 atoms, 16 residues, 2 mols, non-PBC>
# check unique residue names
In [4]: print(set(residue.name for residue in traj.top.residues))
{'G5', 'G', 'C3', 'C'}
In [5]: na = pt.nastruct(traj)
In [6]: na
Out[6]: <nupars, keys = ['open', 'tilt', 'zp', 'pucker', 'minor', 'hb', 'major', 'stagger', 'incl', 'prop', 'tip', 'hrise', 'twist', 'ydisp', 'slide', 'rise', 'bp', 'stretch', 'xdisp', 'shear', 'roll', 'htwist', 'buckle', 'shift']>
# get values for major groove width
In [7]: na.major
Out[7]:
(['3G14C-4C13G', '4C13G-5G12C', '5G12C-6C11G'],
array([[ 19.9015, 20.751 , 19.9822],
[ 19.5528, 20.6965, 19.6335],
[ 19.2871, 21.201 , 19.9055]]))
# get values for several parameters
In [8]: for key in ['minor', 'twist', 'incl']:
...: print(na[key])
...:
(['3G14C-4C13G', '4C13G-5G12C', '5G12C-6C11G'], array([[ 15.8085, 15.818 , 15.4856],
[ 15.9407, 16.2324, 16.3369],
[ 15.9514, 16.7085, 16.6283]]))
(['1G16C-2G15C', '2G15C-3G14C', '3G14C-4C13G', '4C13G-5G12C', '5G12C-6C11G', '6C11G-7C10G', '7C10G-8C9G'], array([[ 34.7777, 33.9816, 30.1865, ..., 35.1461, 33.9628, 33.1306],
[ 33.3918, 32.6848, 28.3639, ..., 36.5977, 30.2083, 26.4873],
[ 36.2067, 32.5896, 27.4771, ..., 33.4284, 30.9005, 33.7372]]))
(['1G16C-2G15C', '2G15C-3G14C', '3G14C-4C13G', '4C13G-5G12C', '5G12C-6C11G', '6C11G-7C10G', '7C10G-8C9G'], array([[ 15.1276, 5.5395, 10.4794, ..., 1.663 , -8.9116, 12.9009],
[ 0.8921, 18.6342, 3.6122, ..., -8.3858, 19.0858, 5.6582],
[ 3.5335, 14.6935, 11.2296, ..., 10.8699, -3.0495, 21.6089]]))
# get some statistics (syntax might be changed in future)
In [9]: import numpy as np
# get mean
In [10]: na._summary(np.mean)
Out[10]:
{'bp': array([ 1., 1., 1.]),
'buckle': array([ 3.9745, 4.4635, 3.8478]),
'hb': array([ 3., 3., 3.]),
'hrise': array([ 2.9544, 2.8557, 2.8099]),
'htwist': array([ 32.8285, 31.2766, 32.1462]),
'incl': array([ 7.2931, 7.5347, 10.0523]),
'major': array([ 20.2115, 19.9609, 20.1312]),
'minor': array([ 15.704 , 16.17 , 16.4294]),
'open': array([ 1.608 , -0.2505, -1.071 ]),
'prop': array([-8.377 , -7.3107, -5.6306]),
'pucker': array([ 76.5285, 98.5826, 101.2006]),
'rise': array([ 3.2847, 3.2277, 3.2468]),
'roll': array([ 3.8703, 3.8303, 5.5281]),
'shear': array([ 0.0847, -0.2052, 0.0952]),
'shift': array([-0.3018, 0.1676, -0.1152]),
'slide': array([-1.9909, -2.0282, -2.2355]),
'stagger': array([ 0.1386, 0.1052, 0.0765]),
'stretch': array([-0.0427, -0.1148, -0.0769]),
'tilt': array([-2.3159, -0.8674, -1.2469]),
'tip': array([ 5.1296, 1.7297, 2.8354]),
'twist': array([ 31.8104, 30.5342, 31.0631]),
'xdisp': array([-4.3154, -4.6259, -5.0389]),
'ydisp': array([-0.0041, -0.5068, -0.1015]),
'zp': array([ 2.4829, 2.6598, 2.6448])}
# get std
In [11]: na._summary(np.std)
Out[11]:
{'bp': array([ 0., 0., 0.]),
'buckle': array([ 6.7023, 5.5069, 5.8921]),
'hb': array([ 0., 0., 0.]),
'hrise': array([ 0.5058, 0.391 , 0.4398]),
'htwist': array([ 3.5496, 3.7633, 3.9124]),
'incl': array([ 8.007 , 9.3024, 7.303 ]),
'major': array([ 0.3828, 0.5212, 0.7975]),
'minor': array([ 0.1545, 0.1677, 0.3396]),
'open': array([ 3.1167, 2.5142, 2.3933]),
'prop': array([ 5.4986, 6.2782, 6.6499]),
'pucker': array([ 131.9281, 146.2912, 146.136 ]),
'rise': array([ 0.1861, 0.2306, 0.219 ]),
'roll': array([ 4.4903, 5.2958, 4.3097]),
'shear': array([ 0.4224, 0.2623, 0.308 ]),
'shift': array([ 0.334 , 0.5723, 0.443 ]),
'slide': array([ 0.4734, 0.5112, 0.2721]),
'stagger': array([ 0.2288, 0.2353, 0.2485]),
'stretch': array([ 0.1633, 0.0791, 0.1337]),
'tilt': array([ 4.3807, 2.1819, 4.2355]),
'tip': array([ 10.0093, 3.8594, 8.9471]),
'twist': array([ 4.4756, 3.6107, 4.1022]),
'xdisp': array([ 1.7488, 1.8645, 1.3394]),
'ydisp': array([ 1.1157, 1.2389, 0.579 ]),
'zp': array([ 0.3308, 0.375 , 0.2435])}
# only interested in some parameters?
In [12]: na._summary(np.mean, keys=['major', 'minor', 'twist'])
Out[12]:
{'major': array([ 20.2115, 19.9609, 20.1312]),
'minor': array([ 15.704 , 16.17 , 16.4294]),
'twist': array([ 31.8104, 30.5342, 31.0631])}
# explain keywords
In [13]: print(na._explain())
[shear] Base pair shear.
[stretch] Base pair stretch.
[stagger] Base pair stagger.
[buckle] Base pair buckle.
[prop] Base pair propeller.
[open] Base pair opening.
[hb] Number of hydrogen bonds between bases in base pair.
[pucker] Base sugar pucker.
[major] Rough estimate of major groove width, calculated between P atoms of each
base.
[minor] Rough estimate of minor groove width, calculated between O4 atoms of
each base.
[shift] Base pair step shift.
[slide] Base pair step slide.
[rise] Base pair step rise.
[title] Base pair step tilt.
[roll] Base pair step roll.
[twist] Base pair step twist.
[xdisp] Helical X displacement.
[ydisp] Helical Y displacement.
[hrise] Helical rise.
[incl] Helical inclination.
[tip] Helical tip.
[htwist] Helical twist.
# if we have long analysis, we can temporarily save ``na`` to disk by `pytraj.to_pickle`` and load back later.
In [14]: pt.to_pickle(na, 'na_.pk')
# load back pickle object
In [15]: na2 = pt.read_pickle('na_.pk')
In [16]: na2
Out[16]: <nupars, keys = ['open', 'tilt', 'zp', 'pucker', 'minor', 'hb', 'major', 'stagger', 'incl', 'prop', 'htwist', 'hrise', 'twist', 'ydisp', 'slide', 'rise', 'bp', 'stretch', 'xdisp', 'shear', 'roll', 'tip', 'buckle', 'shift']>
In [17]: na2._summary(np.mean, ['major', 'twist'])
Out[17]:
{'major': array([ 20.2115, 19.9609, 20.1312]),
'twist': array([ 31.8104, 30.5342, 31.0631])}