def setUp(self):
self.a2c = FullAtomModel.Angles2Coords()
self.c2tc = FullAtomModel.Coords2TypedCoords()
self.c2cc = FullAtomModel.CoordsTransform.Coords2CenteredCoords(
rotate=True, translate=True)
self.error = 0.0
self.N = 0
def __init__(self, device='cpu', num_sequences=32, seq_length=350):
self.module = FullAtomModel.Angles2Coords()
self.sequences = [
gen_rand_seq(seq_length) for i in range(num_sequences)
]
# print(self.sequences)
self.angles = torch.randn(len(self.sequences),
7,
len(self.sequences[-1]),
dtype=torch.double,
device='cpu',
requires_grad=True)
import torch
from TorchProteinLibrary import FullAtomModel
import numpy as np
import matplotlib.pylab as plt
import mpl_toolkits.mplot3d.axes3d as p3
if __name__ == '__main__':
a2c = FullAtomModel.Angles2Coords()
sequences = ['GGMLGWAHFGY']
#Setting conformation to alpha-helix
angles = torch.zeros(len(sequences),
7,
len(sequences[-1]),
dtype=torch.double,
device='cpu')
angles.data[:, 0, :] = -1.047
angles.data[:, 1, :] = -0.698
angles.data[:, 2:, :] = 110.4 * np.pi / 180.0
#Converting angles to coordinates
coords, res_names, atom_names, num_atoms = a2c(angles, sequences)
#Making a mask on CA, C, N atoms
is0C = torch.eq(atom_names[:, :, 0], 67).squeeze()
is1A = torch.eq(atom_names[:, :, 1], 65).squeeze()
is20 = torch.eq(atom_names[:, :, 2], 0).squeeze()
is0N = torch.eq(atom_names[:, :, 0], 78).squeeze()
is10 = torch.eq(atom_names[:, :, 1], 0).squeeze()
isCA = is0C * is1A * is20
isC = is0C * is10
def setUp(self): self.a2c = FullAtomModel.Angles2Coords() self.c2tc = FullAtomModel.Coords2TypedCoords()
def setUp(self):
self.a2c = FullAtomModel.Angles2Coords()
