for res2 in struct.residues[left:i + 1]:
for atom in res2.atoms:
colors[atom.idx, :] = np.array(
[216.0 / 255.0, 179.0 / 255.0, 101.0 / 255.0])
return colors
if __name__ == '__main__':
mols = "b2 b2_a a2a a2a_a".split()
numbers = "A) B) C) D) E) F) G) H)".split()
fig = plt.figure(1, figsize=(8, 12))
for i, mol in enumerate(mols):
xray = gpcr_lib.load_xray(mol)
pdb = xray.pdbfile
# Make colour pattern due to pattern
colors = _make_cra_colors(pdb)
colors = _make_ccm_colors(pdb, colors)
a = fig.add_subplot(len(mols), 2, i * 2 + 1, aspect="equal")
xray.plot(a,
"low",
reverseY=False,
sidechain=True,
colorscheme=colors,
drawchol=False)
a.text(-35, 33, numbers[(i * 2)])
a.text(-25, 25, "Intra.", size=14)
parser.add_argument('-p',
'--probes',
nargs="+",
type=float,
help="the probe sizes",
default=[1.4, 1.8, 2.2, 2.6, 3.0])
args = parser.parse_args()
mols = "b2 b2_a a2a a2a_a".split()
numbers = "A) B) C) D) E) F) G) H)".split()
fig = plt.figure(1, figsize=(8, 12))
# Setup and calculate the partition for each molecule
parts = [None] * len(mols)
for i, mol in enumerate(mols):
xray, aastruct = gpcr_lib.load_xray(mol, loadsigma=True, loadaa=True)
parts[i] = StructurePartition(xray, aastruct, args.npies)
parts[i].calc_surf(args.probes)
parts[i].calc_fractal()
parts[i].clean_up()
# Find the lowest and maxium fractal among the different molecules
minval = min(2.0, [p.minval for p in parts])
maxval = np.around(max([p.maxval for p in parts]), 1)
# Plot each of the pie charts
for i, (part, mol) in enumerate(zip(parts, mols)):
part.minval = minval
part.maxval = maxval
a1 = fig.add_subplot(len(mols), 2, i * 2 + 1)
a2 = fig.add_subplot(len(mols), 2, i * 2 + 2)
restocolor = gpcr_lib.read_rescontacts(args.folder, mol)
import calc_surf
if __name__ == '__main__' :
# Command-line input
parser = argparse.ArgumentParser(description="Analysing residue exposure")
parser.add_argument('-f','--folder', help="the folder with the residue contacts")
parser.add_argument('-p','--probe',type=float,help="the probe size",default=2.4)
parser.add_argument('-c','--percentile',type=float,help="the percentile to consider",default=50)
args = parser.parse_args()
mols = "b2 b2_a a2a a2a_a".split()
data = {}
for mol in mols:
xray, aastruct = gpcr_lib.load_xray(mol, loadsigma=True, loadaa=True)
radii = np.asarray([calc_surf.bornradii[atom.element().upper()] for atom in aastruct.atoms])
xyzrname = calc_surf.write_xyzr(aastruct.xyz,radii)
surf = calc_surf.calc_surf(xyzrname, aastruct.xyz.shape[0], args.probe)
res, contactprob = gpcr_lib.read_rescontacts(args.folder, mol, percentile=args.percentile, returnprobs=True)
print len(res)
mdata = {}
for tres, s in zip(xray.template.residues, surf):
if tres in res :
mdata[tres] = (s[0], contactprob[res==tres][0])
data[mol] = mdata
with open("ses_%s"%mol,"w") as f :
for s, r in zip(surf,xray.template.residues):
f.write("%d %.3f\n"%(r, s[0]))
diff = []
# If user specified only one mol, assume the same for the other densities
if len(args.mol) == 1 : args.mol.append(args.mol[0])
# Make up pre-defined labels
labels = [args.label[0]+"/"+gpcr_lib.side_name["low"]]
labels.append(args.label[1]+"/"+gpcr_lib.side_name["low"])
labels.append(args.label[0]+"/"+gpcr_lib.side_name["upp"])
labels.append(args.label[1]+"/"+gpcr_lib.side_name["upp"])
labels.append(args.label[0]+"/mid("+gpcr_lib.side_name["low"]+")")
labels.append(args.label[0]+"/mid("+gpcr_lib.side_name["upp"]+")")
labels.append(args.label[1]+"/mid("+gpcr_lib.side_name["low"]+")")
labels.append(args.label[1]+"/mid("+gpcr_lib.side_name["upp"]+")")
# Read the densities and Xray structures from file
densities1 = gpcr_lib.standard_read_and_process(args.files1,args.density[0],countnam[0])
xray1 = gpcr_lib.load_xray(args.mol[0])
densities2 = gpcr_lib.standard_read_and_process(args.files2,args.density[1],countnam[1])
xray2 = gpcr_lib.load_xray(args.mol[1])
# Write out the processed densities to file for debugging
densities1.write(args.plot,args.out+"_savez1.npz")
densities2.write(args.plot,args.out+"_savez2.npz")
# Introduce a cut-off when plotting averages
if args.plot == "average" :
densities1.cutoff_av(cutoff=0.15)
densities2.cutoff_av(cutoff=0.15)
# Find the maximum and minimum values
if args.max is None :
maxval = max(densities1.max(args.plot),densities2.max(args.plot))
if __name__ == '__main__' :
# Command-line input
parser = argparse.ArgumentParser(description="Plotting fractal pies")
parser.add_argument('-n','--npies',type=int,help="the number of pies",default=12)
parser.add_argument('-p','--probes',nargs="+",type=float,help="the probe sizes",default=[2.0,2.5,3.0,3.5])
args = parser.parse_args()
mols = "b2 b2_a a2a a2a_a".split()
numbers = "A) B) C) D) E) F) G) H)".split()
fig = plt.figure(1,figsize=(8,12))
# Setup and calculate the partition for each molecule
parts = [None]*len(mols)
for i,mol in enumerate(mols) :
xray = gpcr_lib.load_xray(mol,loadsigma=True)
parts[i] = StructurePartition(xray,args.npies)
parts[i].calc_surf(args.probes)
parts[i].calc_fractal()
parts[i].clean_up()
# Find the lowest and maxium fractal among the different molecules
minval = min(2.0,[p.minval for p in parts])
maxval = np.around(max([p.maxval for p in parts]),1)
# Plot each of the pie charts
for i,part in enumerate(parts) :
part.minval = minval
part.maxval = maxval
a1 = fig.add_subplot(len(mols),2,i*2+1)
a2 = fig.add_subplot(len(mols),2,i*2+2)
part.plot([a1,a2],numbers[(i*2):(i+1)*2])
# Setup a parser of the command-line arguments
parser = argparse.ArgumentParser(description="Plotting pairs of GPCR densities")
parser.add_argument('-f','--files',nargs="+",help="a list of input files.",default=[])
parser.add_argument('-o','--out',help="the output png-file.",default="densities_series")
parser.add_argument('-d','--density',help="the name of the densities.")
parser.add_argument('-m','--mol',choices=["b2","a2a","b2_a","a2a_a"],help="the protein molecules")
parser.add_argument('-p','--plot',choices=["dg","average"],help="the density to plot",default="dg")
parser.add_argument('-r','--replacement',nargs=4,help="the replacement strings",default=["densities2","densities3","densities4","densities5"])
parser.add_argument('-l','--label',nargs=4,help="the labels of the densities",default=[r'$10 \mu s$',r'$20 \mu s$',r'$30 \mu s$',r'$40 \mu s$'])
parser.add_argument('--max',type=float)
parser.add_argument('--min',type=float)
args = parser.parse_args()
# Read Xray structure and densities
xray = gpcr_lib.load_xray(args.mol)
densities = [gpcr_lib.standard_read_and_process(args.files,args.density)]
for r in args.replacement[1:] :
filenames = [f.replace(args.replacement[0],r) for f in args.files]
densities.append(gpcr_lib.standard_read_and_process(filenames,args.density))
if args.plot == "average" :
for d in densities : d.cutoff_av(cutoff=0.15)
if args.max is None :
maxval = max(*[d.max(args.plot) for d in densities])
else :
maxval = args.max
if args.min is None :
minval = min(*[d.min(args.plot) for d in densities])
else :
# Command-line input
parser = argparse.ArgumentParser(
description="Analyzing GPCR grid interactions")
parser.add_argument('-p',
'--top',
help="a topology file",
default="system.top")
parser.add_argument('-m',
'--mol',
choices=["b2", "a2a", "b2_a", "a2a_a"],
help="the protein molecule")
args = parser.parse_args()
# Read a Xrya structure file
xray = gpcr_lib.load_xray(args.mol, loadsigma=True)
pdb = xray.pdbfile
# Remove cholesterols at the end
while pdb.residues[-1].resname == "CHOL":
for atom in pdb.residues[-1].atoms:
pdb.atoms.remove(atom)
del pdb.residues[-1]
pdb.xyz = np.array([atom.xyz for atom in pdb.atoms])
# Read a Gromacs topology file
top = gmx.TopFile(args.top)
# Store away all interesting LJ parameter pairs
roh_type = "SP1" #"SC1"
roh_pairs = []
'--replacement',
nargs=4,
help="the replacement strings",
default=["densities2", "densities3", "densities4", "densities5"])
parser.add_argument(
'-l',
'--label',
nargs=4,
help="the labels of the densities",
default=[r'$10 \mu s$', r'$20 \mu s$', r'$30 \mu s$', r'$40 \mu s$'])
parser.add_argument('--max', type=float)
parser.add_argument('--min', type=float)
args = parser.parse_args()
# Read Xray structure and densities
xray = gpcr_lib.load_xray(args.mol)
densities = [gpcr_lib.standard_read_and_process(args.files, args.density)]
for r in args.replacement[1:]:
filenames = [f.replace(args.replacement[0], r) for f in args.files]
densities.append(
gpcr_lib.standard_read_and_process(filenames, args.density))
if args.plot == "average":
for d in densities:
d.cutoff_av(cutoff=0.15)
if args.max is None:
maxval = max(*[d.max(args.plot) for d in densities])
else:
maxval = args.max
if args.min is None:
print "CCM: "+" ".join(res2.resname for res2 in struct.residues[left:i+1])
for res2 in struct.residues[left:i+1] :
for atom in res2.atoms :
colors[atom.idx,:] = np.array([216.0/255.0,179.0/255.0,101.0/255.0])
return colors
if __name__ == '__main__' :
mols = "b2 b2_a a2a a2a_a".split()
numbers = "A) B) C) D) E) F) G) H)".split()
fig = plt.figure(1,figsize=(8,12))
for i,mol in enumerate(mols) :
xray = gpcr_lib.load_xray(mol)
pdb = xray.pdbfile
# Make colour pattern due to pattern
colors = _make_cra_colors(pdb)
colors = _make_ccm_colors(pdb,colors)
a = fig.add_subplot(len(mols),2,i*2+1,aspect="equal")
xray.plot(a,"low",reverseY=False,sidechain=True,colorscheme=colors,drawchol=False)
a.text(-35,33,numbers[(i*2)])
a.text(-25,25,"Intra.",size=14)
a.set_xticklabels([])
a.set_yticklabels([])
a.set_xlim((-30,30))
a.set_ylim((-30,30))
if i == 0 :
f.write('object "regular positions regular connections" class field\n')
f.write('component "positions" value 1\n')
f.write('component "connections" value 2\n')
f.write('component "data" value 3\n')
f.close()
if __name__ == '__main__' :
# Command-line input
parser = argparse.ArgumentParser(description="Analyzing GPCR grid interactions")
parser.add_argument('-p','--top',help="a topology file",default="system.top")
parser.add_argument('-m','--mol',choices=["b2","a2a","b2_a","a2a_a"],help="the protein molecule")
args = parser.parse_args()
# Read a Xrya structure file
xray = gpcr_lib.load_xray(args.mol,loadsigma=True)
pdb = xray.pdbfile
# Remove cholesterols at the end
while pdb.residues[-1].resname == "CHOL" :
for atom in pdb.residues[-1].atoms :
pdb.atoms.remove(atom)
del pdb.residues[-1]
pdb.xyz = np.array([atom.xyz for atom in pdb.atoms])
# Read a Gromacs topology file
top = gmx.TopFile(args.top)
# Store away all interesting LJ parameter pairs
roh_type = "SP1" #"SC1"
roh_pairs = []
parser.add_argument('-p',
'--probes',
nargs="+",
type=float,
help="the probe sizes",
default=[2.0, 2.5, 3.0, 3.5])
args = parser.parse_args()
mols = "b2 b2_a a2a a2a_a".split()
numbers = "A) B) C) D) E) F) G) H)".split()
fig = plt.figure(1, figsize=(8, 12))
# Setup and calculate the partition for each molecule
parts = [None] * len(mols)
for i, mol in enumerate(mols):
xray = gpcr_lib.load_xray(mol, loadsigma=True)
parts[i] = StructurePartition(xray, args.npies)
parts[i].calc_surf(args.probes)
parts[i].calc_fractal()
parts[i].clean_up()
# Find the lowest and maxium fractal among the different molecules
minval = min(2.0, [p.minval for p in parts])
maxval = np.around(max([p.maxval for p in parts]), 1)
# Plot each of the pie charts
for i, part in enumerate(parts):
part.minval = minval
part.maxval = maxval
a1 = fig.add_subplot(len(mols), 2, i * 2 + 1)
a2 = fig.add_subplot(len(mols), 2, i * 2 + 2)
part.plot([a1, a2], numbers[(i * 2):(i + 1) * 2])
