sys.path.append('/home/people/tc/svn/tc_sandbox/pdb/')
import biounit,parse_pdb
sys.path.append('/home/people/tc/svn/tc_sandbox/misc/')
import combinatorics
path_pdb = '/data/pdb-v3.2'
d_radii_vdw = {
'H':1.20,
'C':1.70,
'N':1.55,
'O':1.52,
'S':1.80,
}
l_translations = combinatorics.permutation_w_rep([-1,0,1,],3)
l_translations.remove([0,0,0,])
s_alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' # 0123456789
## Give the cutoff for "NEAR" contacts (Angstrom)
if '-sympar' in sys.argv:
sympar = float(sys.argv[sys.argv.index('-sympar')+1])
else:
sympar = 5.
def main(pdb):
if not os.path.isfile('%s.pdb' %(pdb)):
os.system('cp %s/%s/pdb%s.ent %s.pdb' %(path_pdb,pdb[1:3],pdb,pdb,))
def add_probe_atoms(
d_coords,
d_dimensions,
dist_min_sq,
dist_max_sq,
):
dist_max = math.sqrt(dist_max_sq)
dist_min = math.sqrt(dist_min_sq)
bool_test = True
bool_test = False
l_atom_nos = d_coords.keys()
l_atom_nos.sort()
## ## avoid placing probe next to terminal mobile residues (should be by residue number)
## l_atom_nos = l_atom_nos[20:-20]
l_coords_probe = []
l_pdb_coords_solvent = []
x_min = int(d_dimensions['x']['min']) - shell
x_max = int(d_dimensions['x']['max']) + shell + 1
y_min = int(d_dimensions['y']['min']) - shell
y_max = int(d_dimensions['y']['max']) + shell + 1
z_min = int(d_dimensions['z']['min']) - shell
z_max = int(d_dimensions['z']['max']) + shell + 1
## ##
## ## faster method but doesnt calculate angle
## ##
## d_class = {}
## for x in range(x_min,x_max,grid):
## d_class[x] = {}
## for y in range(y_min,y_max,grid):
## d_class[x][y] = {}
## for z in range(z_min,z_max,grid):
## d_class[x][y][z] = None
## for k,v in d_coords.items():
##
## atom_name_protein = v['atom_name']
## if atom_name_protein[0] == 'H':
## continue
##
## coord = v['coord']
##
## x_min_local = int(coord[0]-coord[0]%grid+x_min%grid-dist_max-grid)
## x_max_local = int(coord[0]-coord[0]%grid+x_min%grid+dist_max+grid)
## y_min_local = int(coord[1]-coord[1]%grid+y_min%grid-dist_max-grid)
## y_max_local = int(coord[1]-coord[1]%grid+y_min%grid+dist_max+grid)
## z_min_local = int(coord[2]-coord[2]%grid+z_min%grid-dist_max-grid)
## z_max_local = int(coord[2]-coord[2]%grid+z_min%grid+dist_max+grid)
## x_max_local = min(x_max,x_max_local)
## y_max_local = min(y_max,y_max_local)
## z_max_local = min(z_max,z_max_local)
## x_min_local = max(x_min,x_min_local)
## y_min_local = max(y_min,y_min_local)
## z_min_local = max(z_min,z_min_local)
##
## for x in range(x_min_local,x_max_local,grid):
## for y in range(y_min_local,y_max_local,grid):
## for z in range(z_min_local,z_max_local,grid):
## ## if too close once, then always too close
## if d_class[x][y][z] == 'tooclose':
## continue
## coord_grid = numpy.array([x,y,z])
## dist_sq = sum((coord-coord_grid)**2)
## if dist_sq > dist_max_sq:
## d_class[x][y][z] = 'toofar'
#### print k, 'a'
## elif dist_sq < dist_min_sq:
## d_class[x][y][z] = 'tooclose'
#### print k, 'b'
## else:
## d_class[x][y][z] = 'extra'
## l_coords = []
## for x in range(x_min,x_max,grid):
## for y in range(y_min,y_max,grid):
## for z in range(z_min,z_max,grid):
## if d_class[x][y][z] == 'extra':
## coord = numpy.array([x,y,z])
## l_coords += [coord]
## print len(l_coords)
## l_overlaps = [0]*len(l_coords)
## write_pdb(l_overlaps,l_coords,'2exo','A',suffix='_test_distmin%s_distmax%s' %(dist_min,dist_max,))
## stop
d_class = {}
for x in range(x_min, x_max, grid):
print x, x_min, x_max
d_class[x] = {}
for y in range(y_min, y_max, grid):
d_class[x][y] = {}
for z in range(z_min, z_max, grid):
d_class[x][y][z] = {}
bool_vicinal = False
bool_not_distant = False
## bool_not_distant = 0
l_coords_not_distant = []
for atom_no_protein in l_atom_nos:
d = d_coords[atom_no_protein]
atom_name_protein = d['atom_name']
coord_protein = d['coord']
## skip if hydrogen atoms
if atom_name_protein[0] == 'H':
continue
## if not atom_name_protein in l_atoms:
## continue
dist_sq = ((x - coord_protein[0])**2 +
(y - coord_protein[1])**2 +
(z - coord_protein[2])**2)
if dist_sq < dist_min_sq:
bool_vicinal = True
## if dist_sq < dist_max_sq:
## if atom_name_protein == 'CA' and dist_sq < dist_max_sq:
## if atom_name_protein in ['N','CA','CB','C','O',] and dist_sq < dist_max_sq:
if atom_name_protein in l_atoms and dist_sq < dist_max_sq:
bool_not_distant = True
## only do angle between alpha carbon atoms
if atom_name_protein == 'CA':
l_coords_not_distant += [coord_protein]
## bool_not_distant += 1
if bool_vicinal == True:
break
if bool_vicinal == False:
if bool_not_distant == True:
## if bool_test != True:
##
## l_coords_probe += [numpy.array([x,y,z,])]
## d_class[x][y][z]['class'] = 'possible_pocket'
##
## else:
d_class[x][y][z]['class'] = 'not_pocket'
if len(l_coords_not_distant) > 1:
for i in range(len(l_coords_not_distant) - 1):
coord1 = l_coords_not_distant[i]
v1 = coord1 - numpy.array([
x,
y,
z,
])
for j in range(i + 1,
len(l_coords_not_distant)):
coord2 = l_coords_not_distant[j]
v2 = coord2 - numpy.array([
x,
y,
z,
])
## angle larger than xxx'
if numpy.dot(v1, v2) < 0: ## angle > 90
d_class[x][y][z][
'class'] = 'possible_pocket'
l_coords_probe += [[
x,
y,
z,
]]
break
if d_class[x][y][z][
'class'] == 'possible_pocket':
break
elif bool_not_distant == False:
d_class[x][y][z]['class'] = 'distant'
elif bool_vicinal == True:
d_class[x][y][z]['class'] = 'protein'
if bool_test == True:
sys.path.append('/home/people/tc/svn/tc_sandbox/misc/')
import combinatorics
l_translations = combinatorics.permutation_w_rep([
-1,
0,
1,
], 3)
## l_translations = combinatorics.permutation_w_rep([-2,-1,0,1,2,],5)
l_translations.remove([
0,
0,
0,
])
l_overlaps = []
d_class_grid = {}
for x in range(
int(d_dimensions['x']['min']) - shell,
int(d_dimensions['x']['max']) + shell + 1, grid):
print x
d_class_grid[x] = {}
for y in range(
int(d_dimensions['y']['min']) - shell,
int(d_dimensions['y']['max']) + shell + 1, grid):
d_class_grid[x][y] = {}
for z in range(
int(d_dimensions['z']['min']) - shell,
int(d_dimensions['z']['max']) + shell + 1, grid):
count_pocket = 1 ## point itself is pocket
count_protein = 0
count_distant = 0
if d_class[x][y][z]['class'] != 'possible_pocket':
continue
for translation in l_translations:
try:
Class = d_class[x + translation[0] * grid][
y + translation[1] * grid][z + translation[2] *
grid]['class']
if Class == 'possible_pocket':
count_pocket += 1
elif Class == 'protein':
count_protein += 1
elif Class == 'distant':
count_distant += 1
except:
None
count_pocket_min = 16
count_protein_min = 18
count_distant_max = 27
if (count_pocket >= count_pocket_min
or count_protein >= count_protein_min
) and count_distant <= count_distant_max:
print count_distant
## near pocket and semi buried in protein (not surface)
if count_pocket >= count_pocket_min and count_protein > count_protein_min / 2.:
count_pocket = 13 ## green, include
## not vicinal to many pocket grid points, but very buried in protein
elif count_protein >= count_protein_min:
count_pocket = 27 ## blue, include
## not near other pocket points
elif count_pocket <= count_pocket_min / 2.:
count_pocket = 24 ## light blue, exclude
## ## surface of protein
## elif count_protein <= count_protein_min/2.:
## count_pocket = 6 ## orange, exclude
## not buried = in a large pocket = possibly ligand binding site
else:
count_pocket = 21 ## cyan
else:
## print count_pocket+count_protein+count_distant, count_pocket, count_protein, count_distant
count_pocket = 0 ## red, exclude
print x, y, z
## count_pocket_min = 25
## count_protein_min = 70
## print count_protein, count_pocket
## if count_pocket >= count_pocket_min or count_protein >= count_protein_min:
## ## not vicinal to many pocket grid points, but very buried in protein
## if count_pocket < count_pocket_min and count_protein >= count_protein_min:
## count_pocket = 27 ## blue, include
## ## near pocket grid points, but surface of protein
## elif count_pocket >= count_pocket_min and count_protein <= count_protein_min/2.:
## count_pocket = 6 ## orange, exclude
## ## near pocket and semi buried in protein (not surface)
## elif count_pocket >= count_pocket_min and count_protein > count_protein_min/2.:
## count_pocket = 13 ## green, include
## else:
## stopstop
## else:
## count_pocket = 0 ## red, exclude
l_overlaps += [count_pocket / 27.]
## d_class_grid[x][y][z] = count_pocket
## if count > 0:
## print x,y,z,d_class_grid[x][y][z]
## stop
## print d_class_grid[-10][-1]
## stop
print 'first coord', l_coords_probe[0]
print 'last coord', l_coords_probe[-1]
print 'coords', len(l_coords_probe)
print 'bfactors', len(l_overlaps)
if len(l_coords_probe) != len(l_overlaps):
print len(l_coords_probe)
print len(l_overlaps)
stop
return l_coords_probe
def main():
pdb = "2lzt"
import Numeric
import sys
sys.path.append("/home/people/tc/svn/tc_sandbox/misc/")
import combinatorics
l_translations = combinatorics.permutation_w_rep([-1, 0, 1], 3)
l_translations.remove([0, 0, 0])
d_coordinates, d_290, l_coordinates, cryst1 = parse_pdb(pdb)
l_chains = [
"B",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"J",
"K",
"L",
"M",
"N",
"O",
"P",
"Q",
"R",
"S",
"T",
"U",
"V",
"W",
"X",
"Y",
"Z",
"b",
"c",
"d",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"o",
"p",
"q",
"r",
"s",
"t",
"u",
"v",
"w",
"x",
"y",
"z",
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
]
chain_index = -1
atom_no = 1001
prev_residue = ["N/A", "N/A", "N/A"]
lines = []
for operator in d_290.keys():
matrix = d_290[operator]
for i in range(len(l_translations)):
translation = Numeric.array(l_translations[i]) * cryst1
print operator, i, chain_index
for chain in d_coordinates.keys():
l_res_nos = d_coordinates[chain].keys()
l_res_nos.sort()
for res_no in l_res_nos:
for atom_name in d_coordinates[chain][res_no]["atoms"].keys():
coordinate2 = d_coordinates[chain][res_no]["atoms"][atom_name]["coordinate"]
coordinate2 = Numeric.array(list(coordinate2))
coordinate2 += translation
vicinal = False
for coordinate1 in l_coordinates:
sqdist = sum((coordinate2 - coordinate1) ** 2)
if sqdist < 225:
vicinal = True
break
if vicinal == True:
break
if vicinal == True or prev_residue == [i, res_no - 1]:
## new chain ID
if prev_residue not in [[i, res_no - 3], [i, res_no - 2], [i, res_no - 1]]:
print i, res_no, prev_residue
chain_index += 1
## add previous residue
if (
prev_residue not in [[i, res_no - 2], [i, res_no - 1]]
and res_no - 1 in d_coordinates[chain].keys()
):
lines, atom_no = newline(
atom_no,
translation,
lines,
l_chains,
chain_index,
d_coordinates,
pdb,
chain,
res_no - 1,
)
## add current residue
if prev_residue not in [[i, res_no - 1]]:
lines, atom_no = newline(
atom_no, translation, lines, l_chains, chain_index, d_coordinates, pdb, chain, res_no
)
## add next residue
if res_no + 1 in d_coordinates[chain].keys():
lines, atom_no = newline(
atom_no,
translation,
lines,
l_chains,
chain_index,
d_coordinates,
pdb,
chain,
res_no + 1,
)
if vicinal == True:
prev_residue = [i, res_no]
fd = open("%s_contacts.pdb" % (pdb), "w")
fd.writelines(lines)
fd.close()
print len(lines), len(set(lines))
return
def main():
pdb = '2lzt'
import Numeric
import sys
sys.path.append('/home/people/tc/svn/tc_sandbox/misc/')
import combinatorics
l_translations = combinatorics.permutation_w_rep([
-1,
0,
1,
], 3)
l_translations.remove([
0,
0,
0,
])
d_coordinates, d_290, l_coordinates, cryst1 = parse_pdb(pdb)
l_chains = [
'B',
'C',
'D',
'E',
'F',
'G',
'H',
'I',
'J',
'K',
'L',
'M',
'N',
'O',
'P',
'Q',
'R',
'S',
'T',
'U',
'V',
'W',
'X',
'Y',
'Z',
'b',
'c',
'd',
'e',
'f',
'g',
'h',
'i',
'j',
'k',
'l',
'm',
'n',
'o',
'p',
'q',
'r',
's',
't',
'u',
'v',
'w',
'x',
'y',
'z',
'0',
'1',
'2',
'3',
'4',
'5',
'6',
'7',
'8',
'9',
]
chain_index = -1
atom_no = 1001
prev_residue = [
'N/A',
'N/A',
'N/A',
]
lines = []
for operator in d_290.keys():
matrix = d_290[operator]
for i in range(len(l_translations)):
translation = Numeric.array(l_translations[i]) * cryst1
print operator, i, chain_index
for chain in d_coordinates.keys():
l_res_nos = d_coordinates[chain].keys()
l_res_nos.sort()
for res_no in l_res_nos:
for atom_name in d_coordinates[chain][res_no][
'atoms'].keys():
coordinate2 = d_coordinates[chain][res_no]['atoms'][
atom_name]['coordinate']
coordinate2 = Numeric.array(list(coordinate2))
coordinate2 += translation
vicinal = False
for coordinate1 in l_coordinates:
sqdist = sum((coordinate2 - coordinate1)**2)
if sqdist < 225:
vicinal = True
break
if vicinal == True:
break
if vicinal == True or prev_residue == [
i,
res_no - 1,
]:
## new chain ID
if prev_residue not in [
[
i,
res_no - 3,
],
[
i,
res_no - 2,
],
[
i,
res_no - 1,
],
]:
print i, res_no, prev_residue
chain_index += 1
## add previous residue
if prev_residue not in [
[
i,
res_no - 2,
],
[
i,
res_no - 1,
],
] and res_no - 1 in d_coordinates[chain].keys():
lines, atom_no = newline(
atom_no,
translation,
lines,
l_chains,
chain_index,
d_coordinates,
pdb,
chain,
res_no - 1,
)
## add current residue
if prev_residue not in [
[
i,
res_no - 1,
],
]:
lines, atom_no = newline(
atom_no,
translation,
lines,
l_chains,
chain_index,
d_coordinates,
pdb,
chain,
res_no,
)
## add next residue
if res_no + 1 in d_coordinates[chain].keys():
lines, atom_no = newline(
atom_no,
translation,
lines,
l_chains,
chain_index,
d_coordinates,
pdb,
chain,
res_no + 1,
)
if vicinal == True:
prev_residue = [
i,
res_no,
]
fd = open('%s_contacts.pdb' % (pdb), 'w')
fd.writelines(lines)
fd.close()
print len(lines), len(set(lines))
return
def add_probe_atoms(d_coords,d_dimensions,dist_min_sq,dist_max_sq,):
dist_max = math.sqrt(dist_max_sq)
dist_min = math.sqrt(dist_min_sq)
bool_test = True
bool_test = False
l_atom_nos = d_coords.keys()
l_atom_nos.sort()
## ## avoid placing probe next to terminal mobile residues (should be by residue number)
## l_atom_nos = l_atom_nos[20:-20]
l_coords_probe = []
l_pdb_coords_solvent = []
x_min = int(d_dimensions['x']['min'])-shell
x_max = int(d_dimensions['x']['max'])+shell+1
y_min = int(d_dimensions['y']['min'])-shell
y_max = int(d_dimensions['y']['max'])+shell+1
z_min = int(d_dimensions['z']['min'])-shell
z_max = int(d_dimensions['z']['max'])+shell+1
## ##
## ## faster method but doesnt calculate angle
## ##
## d_class = {}
## for x in range(x_min,x_max,grid):
## d_class[x] = {}
## for y in range(y_min,y_max,grid):
## d_class[x][y] = {}
## for z in range(z_min,z_max,grid):
## d_class[x][y][z] = None
## for k,v in d_coords.items():
##
## atom_name_protein = v['atom_name']
## if atom_name_protein[0] == 'H':
## continue
##
## coord = v['coord']
##
## x_min_local = int(coord[0]-coord[0]%grid+x_min%grid-dist_max-grid)
## x_max_local = int(coord[0]-coord[0]%grid+x_min%grid+dist_max+grid)
## y_min_local = int(coord[1]-coord[1]%grid+y_min%grid-dist_max-grid)
## y_max_local = int(coord[1]-coord[1]%grid+y_min%grid+dist_max+grid)
## z_min_local = int(coord[2]-coord[2]%grid+z_min%grid-dist_max-grid)
## z_max_local = int(coord[2]-coord[2]%grid+z_min%grid+dist_max+grid)
## x_max_local = min(x_max,x_max_local)
## y_max_local = min(y_max,y_max_local)
## z_max_local = min(z_max,z_max_local)
## x_min_local = max(x_min,x_min_local)
## y_min_local = max(y_min,y_min_local)
## z_min_local = max(z_min,z_min_local)
##
## for x in range(x_min_local,x_max_local,grid):
## for y in range(y_min_local,y_max_local,grid):
## for z in range(z_min_local,z_max_local,grid):
## ## if too close once, then always too close
## if d_class[x][y][z] == 'tooclose':
## continue
## coord_grid = numpy.array([x,y,z])
## dist_sq = sum((coord-coord_grid)**2)
## if dist_sq > dist_max_sq:
## d_class[x][y][z] = 'toofar'
#### print k, 'a'
## elif dist_sq < dist_min_sq:
## d_class[x][y][z] = 'tooclose'
#### print k, 'b'
## else:
## d_class[x][y][z] = 'extra'
## l_coords = []
## for x in range(x_min,x_max,grid):
## for y in range(y_min,y_max,grid):
## for z in range(z_min,z_max,grid):
## if d_class[x][y][z] == 'extra':
## coord = numpy.array([x,y,z])
## l_coords += [coord]
## print len(l_coords)
## l_overlaps = [0]*len(l_coords)
## write_pdb(l_overlaps,l_coords,'2exo','A',suffix='_test_distmin%s_distmax%s' %(dist_min,dist_max,))
## stop
d_class = {}
for x in range(x_min,x_max,grid):
print x, x_min, x_max
d_class[x] = {}
for y in range(y_min,y_max,grid):
d_class[x][y] = {}
for z in range(z_min,z_max,grid):
d_class[x][y][z] = {}
bool_vicinal = False
bool_not_distant = False
## bool_not_distant = 0
l_coords_not_distant = []
for atom_no_protein in l_atom_nos:
d = d_coords[atom_no_protein]
atom_name_protein = d['atom_name']
coord_protein = d['coord']
## skip if hydrogen atoms
if atom_name_protein[0] == 'H':
continue
## if not atom_name_protein in l_atoms:
## continue
dist_sq = (
(x-coord_protein[0])**2
+
(y-coord_protein[1])**2
+
(z-coord_protein[2])**2
)
if dist_sq < dist_min_sq:
bool_vicinal = True
## if dist_sq < dist_max_sq:
## if atom_name_protein == 'CA' and dist_sq < dist_max_sq:
## if atom_name_protein in ['N','CA','CB','C','O',] and dist_sq < dist_max_sq:
if atom_name_protein in l_atoms and dist_sq < dist_max_sq:
bool_not_distant = True
## only do angle between alpha carbon atoms
if atom_name_protein == 'CA':
l_coords_not_distant += [coord_protein]
## bool_not_distant += 1
if bool_vicinal == True:
break
if bool_vicinal == False:
if bool_not_distant == True:
## if bool_test != True:
##
## l_coords_probe += [numpy.array([x,y,z,])]
## d_class[x][y][z]['class'] = 'possible_pocket'
##
## else:
d_class[x][y][z]['class'] = 'not_pocket'
if len(l_coords_not_distant) > 1:
for i in range(len(l_coords_not_distant)-1):
coord1 = l_coords_not_distant[i]
v1 = coord1-numpy.array([x,y,z,])
for j in range(i+1,len(l_coords_not_distant)):
coord2 = l_coords_not_distant[j]
v2 = coord2-numpy.array([x,y,z,])
## angle larger than xxx'
if numpy.dot(v1,v2) < 0: ## angle > 90
d_class[x][y][z]['class'] = 'possible_pocket'
l_coords_probe += [[x,y,z,]]
break
if d_class[x][y][z]['class'] == 'possible_pocket':
break
elif bool_not_distant == False:
d_class[x][y][z]['class'] = 'distant'
elif bool_vicinal == True:
d_class[x][y][z]['class'] = 'protein'
if bool_test == True:
sys.path.append('/home/people/tc/svn/tc_sandbox/misc/')
import combinatorics
l_translations = combinatorics.permutation_w_rep([-1,0,1,],3)
## l_translations = combinatorics.permutation_w_rep([-2,-1,0,1,2,],5)
l_translations.remove([0,0,0,])
l_overlaps = []
d_class_grid = {}
for x in range(int(d_dimensions['x']['min'])-shell,int(d_dimensions['x']['max'])+shell+1,grid):
print x
d_class_grid[x] = {}
for y in range(int(d_dimensions['y']['min'])-shell,int(d_dimensions['y']['max'])+shell+1,grid):
d_class_grid[x][y] = {}
for z in range(int(d_dimensions['z']['min'])-shell,int(d_dimensions['z']['max'])+shell+1,grid):
count_pocket = 1 ## point itself is pocket
count_protein = 0
count_distant = 0
if d_class[x][y][z]['class'] != 'possible_pocket':
continue
for translation in l_translations:
try:
Class = d_class[x+translation[0]*grid][y+translation[1]*grid][z+translation[2]*grid]['class']
if Class == 'possible_pocket':
count_pocket += 1
elif Class == 'protein':
count_protein += 1
elif Class == 'distant':
count_distant += 1
except:
None
count_pocket_min = 16
count_protein_min = 18
count_distant_max = 27
if (count_pocket >= count_pocket_min or count_protein >= count_protein_min) and count_distant <= count_distant_max:
print count_distant
## near pocket and semi buried in protein (not surface)
if count_pocket >= count_pocket_min and count_protein > count_protein_min/2.:
count_pocket = 13 ## green, include
## not vicinal to many pocket grid points, but very buried in protein
elif count_protein >= count_protein_min:
count_pocket = 27 ## blue, include
## not near other pocket points
elif count_pocket <= count_pocket_min/2.:
count_pocket = 24 ## light blue, exclude
## ## surface of protein
## elif count_protein <= count_protein_min/2.:
## count_pocket = 6 ## orange, exclude
## not buried = in a large pocket = possibly ligand binding site
else:
count_pocket = 21 ## cyan
else:
## print count_pocket+count_protein+count_distant, count_pocket, count_protein, count_distant
count_pocket = 0 ## red, exclude
print x,y,z
## count_pocket_min = 25
## count_protein_min = 70
## print count_protein, count_pocket
## if count_pocket >= count_pocket_min or count_protein >= count_protein_min:
## ## not vicinal to many pocket grid points, but very buried in protein
## if count_pocket < count_pocket_min and count_protein >= count_protein_min:
## count_pocket = 27 ## blue, include
## ## near pocket grid points, but surface of protein
## elif count_pocket >= count_pocket_min and count_protein <= count_protein_min/2.:
## count_pocket = 6 ## orange, exclude
## ## near pocket and semi buried in protein (not surface)
## elif count_pocket >= count_pocket_min and count_protein > count_protein_min/2.:
## count_pocket = 13 ## green, include
## else:
## stopstop
## else:
## count_pocket = 0 ## red, exclude
l_overlaps += [count_pocket/27.]
## d_class_grid[x][y][z] = count_pocket
## if count > 0:
## print x,y,z,d_class_grid[x][y][z]
## stop
## print d_class_grid[-10][-1]
## stop
print 'first coord', l_coords_probe[0]
print 'last coord', l_coords_probe[-1]
print 'coords', len(l_coords_probe)
print 'bfactors', len(l_overlaps)
if len(l_coords_probe) != len(l_overlaps):
print len(l_coords_probe)
print len(l_overlaps)
stop
return l_coords_probe