CCParams/CCParamsLib.py at master · biocryst/CCParams · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
import cPickle
import numpy as np
from Bio.SVDSuperimposer import SVDSuperimposer
from scipy.linalg import sqrtm, inv


def sym(w):
    return w.dot(inv(sqrtm(w.T.dot(w))))

def params2cc(parameters, return_steps=False):
    steps = []

    h2_tran = parameters[-3:]

    h2_rot = np.reshape(parameters[-12:-3], (3,3))
    helical_params = np.reshape(parameters[:-12], (2,3))

    h2_rot = sym(h2_rot)

    h1_ref = du_mean_helix
    if return_steps:
        step_coords = np.append(h1_ref,h1_ref,axis=0)
        steps.append(step_coords)

    h1_dev, h2_dev = du_pca_helix.inverse_transform(helical_params)
    h1_dev = np.reshape(h1_dev, (h1_ref.shape[0],h1_ref.shape[1]))
    h2_dev = np.reshape(h2_dev, (h1_ref.shape[0],h1_ref.shape[1]))

    h1 = h1_ref+h1_dev
    h2 = h1_ref+h2_dev

    if return_steps:
        step_coords = np.append(h1,h2,axis=0)
        steps.append(step_coords)

    h2_new = np.dot(h2, h2_rot)
    if return_steps:
        step_coords = np.append(h1,h2_new,axis=0)
        steps.append(step_coords)

    h2_new = h2_new + h2_tran
    if return_steps:
        step_coords = np.append(h1,h2_new,axis=0)
        steps.append(step_coords)

    orig_coords = np.append(h1,h2_new,axis=0)

    if return_steps:
        return steps

    return orig_coords


def cc2params(coords):

    sup=SVDSuperimposer()

    n_atoms_mono = int(coords.shape[0]/2)

    h1 = coords[:n_atoms_mono]
    h2 = coords[n_atoms_mono:]
    h1_ref = du_mean_helix

    # align h1 and h2 with mean angles to the ref helix

    sup.set(h1_ref, h1)
    sup.run()
    h1_aligned_ref = sup.get_transformed()

    sup.set(h1_ref, h2)
    sup.run()
    h2_aligned_ref = sup.get_transformed()

    # estimate parameters from pca
    # center to h1_ref
    h1_aligned_ref = h1_aligned_ref - h1_ref
    h2_aligned_ref = h2_aligned_ref - h1_ref
    # unwrap
    h1_aligned_ref = np.reshape(h1_aligned_ref, (h1_aligned_ref.shape[0]*h1_aligned_ref.shape[1]))
    h2_aligned_ref = np.reshape(h2_aligned_ref, (h2_aligned_ref.shape[0]*h2_aligned_ref.shape[1]))
    # get params
    helical_params =du_pca_helix.transform([h1_aligned_ref, h2_aligned_ref])
    h1_aligned_ref, h2_aligned_ref = du_pca_helix.inverse_transform(helical_params)
    h1_aligned_ref = np.reshape(h1_aligned_ref, (h1_ref.shape[0],h1_ref.shape[1]))
    h2_aligned_ref = np.reshape(h2_aligned_ref, (h1_ref.shape[0],h1_ref.shape[1]))

    # construct ideal helices

    h1_transformed = h1_ref+h1_aligned_ref
    h2_transformed = h1_ref+h2_aligned_ref

    # adjust hi_helix1 and hi_helix2 by the parameters

    # align h1 to h1 ideal and transform all coords

    sup.set(h1, h1_transformed)
    sup.run()
    h1_ideal = sup.get_transformed()

    # align h2_ideal to h2

    sup.set(h2, h2_transformed)
    sup.run()
    h2_ideal = sup.get_transformed()

    coords_ideal = np.append(h1_ideal,h2_ideal,axis=0)

    # align ideal coords to the ref helix
    sup.set(h1_transformed,h1_ideal)
    sup.run()
    (rot_ref, tran_ref) = sup.get_rotran()

    coords_ideal = np.dot(coords_ideal,rot_ref) + tran_ref

    h1_new = coords_ideal[:n_atoms_mono]
    h2_new = coords_ideal[n_atoms_mono:]

    sup.set(h2_new, h2_transformed)
    sup.run()
    (rot2, tran2) = sup.get_rotran()
    h2_rot = rot2.flatten()
    helical_params = helical_params.flatten()
    transform_params = np.append(helical_params,np.append(h2_rot, tran2))

    return transform_params


du_pca_helix, du_mean_helix = cPickle.load(open('helix_template.pkl', "rb"))