/
MyPDB_funcs.py
executable file
·139 lines (114 loc) · 4.6 KB
/
MyPDB_funcs.py
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#!/usr/bin/env python3.5
"""
"""
import copy
from MyPDB import MyPDB, parse_PDB
import numpy as np
import scipy.linalg as linalg
import MyPDB as mp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def find_helix_vector(pdb: MyPDB, start: int, end: int):
xs, ys, zs = [], [], []
for i in range(start, end+1):
res_i = pdb.get_res(i)
for bb_atom in res_i.iter_bb():
xs.append(bb_atom.xyz.x)
ys.append(bb_atom.xyz.y)
zs.append(bb_atom.xyz.z)
xs_ = np.array(xs)
ys_ = np.array(ys)
zs_ = np.array(zs)
dist = np.sqrt((xs[-1]-xs[0])**2 + (ys[-1]-ys[0])**2 + (zs[-1]-zs[0])**2 )
data = np.concatenate((xs_[:, np.newaxis], ys_[:, np.newaxis], zs_[:, np.newaxis]), axis=1)
datamean = data.mean(axis=0)
uu, dd, vv = np.linalg.svd(data - datamean)
linepts = vv[0] * np.mgrid[-dist/2:dist/2:2j][:, np.newaxis]
linepts += datamean
return linepts, data
def find_points_of_closest_distance(l1, l2) -> tuple:
u = l1[1] - l1[0]
v = l2[1] - l2[0]
w = l1[0] - l2[0]
a = np.dot(u, u)
b = np.dot(u, v)
c = np.dot(v, v)
d = np.dot(u, w)
e = np.dot(v, w)
D = (a*c) - (b*b)
sc, tc = 0.0, 0.0
if D < 0.000001:
sc = 0.0
tc = d / b if b > c else e / c
else:
sc = (b*e - c*d) / D
tc = (a*e - b*d) / D
# sc = l1[0] + l1*sc
# tc = l2[0] + l2*tc
sc = l1[0] + sc * (l1[1] - l1[0])
tc = l2[0] + tc * (l2[1] - l2[0])
return sc, tc
def calc_dihedral(p1, p2, p3, p4) -> float:
p = np.array([p1, p2, p3, p4])
b = p[:-1] - p[1:]
b[0] *= -1
v = np.array([ v - (v.dot(b[1])/b[1].dot(b[1])) * b[1] for v in [b[0], b[2]]])
return np.degrees(np.arccos( v[0].dot(v[1])/(np.linalg.norm(v[0]) * np.linalg.norm(v[1])) ))
def translate_and_rotate_res_to_xy_plane( res: mp.Residue, atom_list: list ) -> mp.Residue:
xyz = copy.deepcopy(res[ atom_list[0] ].xyz)
xyz = xyz.scalar_multi( -1 )
res.translate_xyz( xyz )
# rotate a1 to xy plane
proj_a2_xy = copy.deepcopy( res[ atom_list[1] ].xyz )
proj_a2_xy.z = 0
a2_copy = copy.deepcopy( res[ atom_list[1] ].xyz )
ang_a2_xy = np.arccos( a2_copy.unit().dot( proj_a2_xy.unit() ) )
axis_a2_xy = a2_copy.unit().cross( proj_a2_xy.unit() ).unit()
rotation_matrix = rotation_matrix_around_vec( axis_a2_xy, ang_a2_xy)
res.dot_matrix_me( rotation_matrix )
# rotate a2 to xy plane
proj_a2_xy = copy.deepcopy( res[ atom_list[2] ].xyz )
proj_a2_xy.z = 0
a2_copy = copy.deepcopy( res[ atom_list[2] ].xyz )
ang_a2_xy = np.arccos( a2_copy.unit().dot( proj_a2_xy.unit()) )
closest_point = point_on_normed_vec_closest_to_point( proj_a2_xy.as_nparray(), res[atom_list[1]].xyz.unit().as_nparray() )
ang_a2_xy = angle_between_3_XYZs( a2_copy, closest_point, proj_a2_xy )
axis = copy.deepcopy( res[ atom_list[1] ].xyz )
rotation_matrix = rotation_matrix_around_vec( axis, -ang_a2_xy )
res.dot_matrix_me( rotation_matrix )
# rotate so that a2 and 3 are on both sides of the Y axis
a1_copy = res[ atom_list[0] ].xyz
a2_copy = res[ atom_list[1] ].xyz
a3_copy = res[ atom_list[2] ].xyz
ang_312 = angle_between_3_XYZs(a3_copy, a1_copy, a2_copy)
ang_y12 = angle_between_3_XYZs( mp.XYZ(0, 1, 0), a1_copy, a2_copy )
axis = mp.XYZ(0, 0, 1)
rotation_matrix = rotation_matrix_around_vec( axis, -( ang_y12 + 0.5 * ang_312 ) )
res.dot_matrix_me( rotation_matrix )
def rotation_matrix_around_vec( axis: mp.XYZ, theta: float ):
"""
calcualted the rotation matrix to roatate an boject around axis theta radian degrees
based on:
http://www.euclideanspace.com/maths/algebra/vectors/angleBetween/
and
http://stackoverflow.com/questions/6802577/python-rotation-of-3d-vector
"""
return linalg.expm3(np.cross(np.eye(3), axis.as_list()/np.float64(linalg.norm(axis.as_list()))*theta))
def write_residues_to_file(residues: list, file_name: str) -> None:
with open(file_name, 'w+') as fout:
for res in residues:
for a in res.atoms.values():
fout.write('%s\n' % a)
def angle_between_3_XYZs( p1: mp.XYZ, p2: mp.XYZ, p3: mp.XYZ ) -> float:
"""
return the angle between 3 points, in radians:w
"""
ba = p1.as_nparray() - p2.as_nparray()
bc = p3.as_nparray() - p2.as_nparray()
cosine_angle = np.dot( ba, bc ) / ( np.linalg.norm( ba ) * np.linalg.norm( bc ) )
return np.arccos( cosine_angle )
def point_on_normed_vec_closest_to_point( p: np.array, v: np.array ) -> mp.XYZ:
vec = v * np.dot( p, v )
return mp.XYZ( vec[0], vec[1], vec[2] )
if __name__ == '__main__':
main()