def calc_span_dg(pdb: MyPDB, start: int, end: int, rosetta_splines: dict) -> float:
"""
calculate a span's dG from structure
"""
dg = 0.0
for i in range(start, end+1):
dg += interpolate.splev( pdb.get_res(i).memb_z, rosetta_splines[ pdb.get_res(i).res_type ], der=0 )
return dg
def find_spans_pdb(pdb: MyPDB) -> list:
memb_vec = []
for res in pdb.iter_all_res():
memb_vec.append( res.memb_z is not None )
spans = []
chain, start, end, ori = pdb.get_res(1).chain, -1, -1, 'fwd'
tm_open = False
for i in range(len(memb_vec)):
if pdb.get_res(i+1).chain != chain and tm_open:
end = i
ori = 'fwd' if pdb.get_res(start).memb_z < 0 and pdb.get_res(end) > 0 else 'rev'
spans.append([start, end, ori])
tm_open = False
if memb_vec[i] and not tm_open:
start = i + 1
tm_open = True
if not memb_vec[i] and tm_open:
end = i-1
ori = 'fwd' if pdb.get_res(start).memb_z < 0 and pdb.get_res(end) > 0 else 'rev'
spans.append([start, end, ori])
tm_open = False
chain = pdb.get_res(i+1).chain
if tm_open:
spans.append([start, i+1, ori])
return spans
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_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
