def GetUniqueChains(self, pdir, pdbID, chains_to_check):
""" Returns a List Unique Chains based on the C-alpha atom information.
Structure based, not sequence based """
e = 'pdb' + self.pdbID + '.ent'
BioParser = PDBParser(PERMISSIVE=True, QUIET=True)
BioStructure = BioParser.get_structure(
self.pdbID, pdir + 'pdb' + self.pdbID + '.ent')
BioModel = BioStructure[0]
Chain_AtomSeq = []
listMatches = []
for item in chains_to_check:
pdbid_chain = e[3:7] + '_' + item
BioChain = BioModel[item]
residues = []
for residue in BioChain:
for atom in residue:
if atom.name == 'CA':
aa1 = amino_dict.replace_all(residue.resname,
amino_dict.one_letter)
residues.append(aa1)
req_res = [x for x in residues if x in amino_dict.amino]
atom = "".join(req_res)
Chain_AtomSeq.append((pdbid_chain, atom))
Chain_Dict = {}
for k, v in Chain_AtomSeq:
Chain_Dict.setdefault(k, v)
# print (Chain_Dict)
allChains = [i for i in Chain_Dict.values()]
set_allChains = list(set(allChains))
# print (set_allChains)
groups = {}
for k, v in Chain_Dict.items():
groups.setdefault(v, []).append(k)
matches = {k: v for k, v in groups.items()}
list_of_matches = [i for i in matches.values()]
# print (list_of_matches)
listMatches.append(list_of_matches)
req_matches = [i[0] for i in matches.values()]
return sorted(req_matches), sorted(list_of_matches)
def AEI(AAAB14):
""" Returns the Reduced Amino Acid Representation for the
amino acid vertices participating in the Delaunay
Tessellation.
Specific for Ion Environments project."""
Data = []
Head = AAAB14[0]
Tail = AAAB14[1:]
pos = GetInd(Head, 'QuadB')
Head.insert(pos + 1, 'AEI_Group')
Head.insert(pos + 2, 'Sort_AEI')
Data.append(Head)
for item in Tail:
point = item[pos]
point = point[0:3]
Vais = amino_dict.replace_all(point, amino_dict.Red_Wang)
Vais = Vais.upper()
Vais_sort = ''.join(sorted(Vais))
item.insert(pos + 1, Vais)
item.insert(pos + 2, Vais_sort)
Data.append(item)
return Data
def ProteinDelaunay(pdbid, chain):
""" Generate the Delaunay Tessellation of all the points in the given point cloud.
The point cloud is basically the Calpha coordinates of a three dimensional protein.
The point, vertices, simplices and neighbors in the entire point cloud are obtained
as arrays.
"""
Data = []
Head = ['PDBID', 'Quad', 'SortedQuad', 'RedAlpha', 'SortRedAlpha', 'V1', 'V2', 'V3', 'V4', 'L1', 'L2', 'L3', 'L4',
'L5', 'L6', 'SumL', 'AvgL', 'DevL', 'DevTetra', 'Vol', 'TF1', 'TF2', 'TF3', 'TF4', 'SumTF', 'AvgTF', 'hullArea', 'hullVolume']
Data.append(Head)
pointcloud, bf, resname = PointCloudData(pdbid, chainid)
print "Given PDB ID: ", pdbid
print "Given Chain ID:", chain
print "Number of C-alpha points: ", len(pointcloud)
# Convex Hull.
ConvxHull = ConvexHull(pointcloud)
hullArea = round(ConvxHull.area, 4)
hullVolume = round(ConvxHull.volume, 4)
# Delaunay Tessellation
delaunay_hull = Delaunay(pointcloud, furthest_site=False, incremental=False, qhull_options='Qc') # noqa E501
delaunay_points = delaunay_hull.points
delaunay_vertices = delaunay_hull.vertices
delaunay_simplices = delaunay_hull.simplices
delaunay_neighbors = delaunay_hull.neighbors
print "Number of Delaunay Simplices: ", len(delaunay_simplices)
for i in delaunay_vertices:
# Obtain the indices of the vertices.
one, two, three, four = i[2], i[1], i[3], i[0]
# Obtain the coordinates based on the indices.
cordA = pointcloud[one]
cordB = pointcloud[two]
cordC = pointcloud[three]
cordD = pointcloud[four]
# Get three letter amino acid names based on indices.
a = resname[one]
b = resname[two]
c = resname[three]
d = resname[four]
# Get the temprature factors for the amino acids.
a_tf = bf[one]
b_tf = bf[two]
c_tf = bf[three]
d_tf = bf[four]
# Get the string of three letter amino acids
# forming the vertices of the tetrahedra.
amino = [a, b, c, d]
sortAmino = sorted(amino)
amino = '-'.join(amino)
sortAmino = '-'.join(sortAmino)
# Get one letter code of the amino acids
oneA = amino_dict.replace_all(a, amino_dict.one_letter)
oneB = amino_dict.replace_all(b, amino_dict.one_letter)
oneC = amino_dict.replace_all(c, amino_dict.one_letter)
oneD = amino_dict.replace_all(d, amino_dict.one_letter)
oneLet = [oneA, oneB, oneC, oneD]
sortOneLet = sorted(oneLet)
oneLet = ''.join(oneLet)
sortOneLet = ''.join(sortOneLet)
# Get Reduced Amino Acid Representations.
flpA = amino_dict.replace_all(oneA, amino_dict.FLP)
flpB = amino_dict.replace_all(oneB, amino_dict.FLP)
flpC = amino_dict.replace_all(oneC, amino_dict.FLP)
flpD = amino_dict.replace_all(oneD, amino_dict.FLP)
flp = [flpA, flpB, flpC, flpD]
sortflp = sorted(flp)
flp = (''.join(flp)).upper()
sortflp = (''.join(sortflp)).upper()
# Calculate distances between the tetrahedra vertices.
AB = np.linalg.norm(cordA - cordB)
AC = np.linalg.norm(cordA - cordC)
AD = np.linalg.norm(cordA - cordD)
BC = np.linalg.norm(cordB - cordC)
BD = np.linalg.norm(cordB - cordD)
CD = np.linalg.norm(cordC - cordD)
# Calculate the tetrahedra Volume.
A_prime = cordA - cordD
B_prime = cordB - cordD
C_prime = cordC - cordD
primes = [A_prime, B_prime, C_prime]
primes = np.asarray(primes)
det = np.linalg.det(primes)
Vol = round((abs(det) / 6), 4)
# Sum of Edge Lengths.
SumL = (AB + AC + AD + BC + BD + CD)
SumL = round(SumL, 4)
# Average Edge Lengths.
AvgL = round((SumL / 6), 4)
# Deviation in Edge Lengths.
devLp = (AB - AvgL) ** 2
devLq = (AC - AvgL) ** 2
devLr = (AD - AvgL) ** 2
devLs = (BC - AvgL) ** 2
devLt = (BD - AvgL) ** 2
devLu = (CD - AvgL) ** 2
devLy = [devLp, devLq, devLr, devLs, devLt, devLu]
sumDevL = sum(devLy)
DevL = round(math.sqrt(sumDevL / 6.0), 4)
# Deviation in Tetrahedrality
lenArr = [AB, AC, AD, BC, BD, CD]
DevT = DevTetra(lenArr)
# Sum and Average Temperature Factors.
SumTF = round((a_tf + b_tf + c_tf + d_tf), 4)
AvgTF = round(SumTF / 4, 4)
# Data List
line = [pdbid, oneLet, sortOneLet, flp, sortflp, one, two, three, four, AB, AC, AD, BC, BD, CD, SumL, AvgL, DevL, DevT, Vol, a_tf, b_tf, c_tf, d_tf, SumTF, AvgTF, hullArea, hullVolume]
Data.append(line)
## Get coordinates based on the vertices.
## vertices_coords store the x, y, z coordinates for the delaunay_vertices.
vertices_coords = pointcloud[delaunay_vertices]
## delaunay_indices store the indices for the delaunay_points.
delaunay_indices = np.arange(len(delaunay_points))
## Get ready for mayavi plot.
fig = mlab.figure(1, bgcolor=(0, 0, 0))
fig.scene.disable_render = True
## Get a 3d scatter plot for the delaunay_points.
mlab.points3d(delaunay_points[:,0], delaunay_points[:,1], delaunay_points[:,2], scale_factor=0.40, color=(0.99, 0.00, 0.00))
ion_c_alpha_scatter = mlab.pipeline.scalar_scatter(delaunay_points[:,0], delaunay_points[:,1], delaunay_points[:,2], delaunay_indices)
ion_c_alpha_delaunay = mlab.pipeline.delaunay3d(ion_c_alpha_scatter)
ion_c_alpha_edges = mlab.pipeline.extract_edges(ion_c_alpha_delaunay)
mlab.pipeline.surface(ion_c_alpha_edges, colormap='winter', opacity=0.4)
mlab.savefig(pdbid + '_MayaviViz.x3d')
mlab.show()
return Data
def ProteinDelaunay(pdbid, chain):
""" Generate the Delaunay Tessellation of all the points in the given point cloud.
The point cloud is basically the Calpha coordinates of a three dimensional protein.
The point, vertices, simplices and neighbors in the entire point cloud are obtained
as arrays.
"""
Data = []
Head = [
'PDBID', 'Quad', 'SortedQuad', 'RedAlpha', 'SortRedAlpha', 'V1', 'V2',
'V3', 'V4', 'L1', 'L2', 'L3', 'L4', 'L5', 'L6', 'SumL', 'AvgL', 'DevL',
'DevTetra', 'Vol', 'TF1', 'TF2', 'TF3', 'TF4', 'SumTF', 'AvgTF'
]
Data.append(Head)
pointcloud, bf, resname = PointCloudData(pdbid, chainid)
# Convex Hull.
ConvxHull = ConvexHull(pointcloud)
hullArea = round(ConvxHull.area, 4)
hullVolume = round(ConvxHull.volume, 4)
# Delaunay Tessellation
delaunay_hull = Delaunay(pointcloud,
furthest_site=False,
incremental=False,
qhull_options='Qc') # noqa E501
delaunay_points = delaunay_hull.points
delaunay_vertices = delaunay_hull.vertices
delaunay_simplices = delaunay_hull.simplices
delaunay_neighbors = delaunay_hull.neighbors
for i in delaunay_vertices:
# Obtain the indices of the vertices.
one, two, three, four = i[2], i[1], i[3], i[0]
# Obtain the coordinates based on the indices.
cordA = pointcloud[one]
cordB = pointcloud[two]
cordC = pointcloud[three]
cordD = pointcloud[four]
# Get three letter amino acid names based on indices.
a = resname[one]
b = resname[two]
c = resname[three]
d = resname[four]
# Get the temprature factors for the amino acids.
a_tf = bf[one]
b_tf = bf[two]
c_tf = bf[three]
d_tf = bf[four]
# Get the string of three letter amino acids
# forming the vertices of the tetrahedra.
amino = [a, b, c, d]
sortAmino = sorted(amino)
amino = '-'.join(amino)
sortAmino = '-'.join(sortAmino)
# Get one letter code of the amino acids
oneA = amino_dict.replace_all(a, amino_dict.one_letter)
oneB = amino_dict.replace_all(b, amino_dict.one_letter)
oneC = amino_dict.replace_all(c, amino_dict.one_letter)
oneD = amino_dict.replace_all(d, amino_dict.one_letter)
oneLet = [oneA, oneB, oneC, oneD]
sortOneLet = sorted(oneLet)
oneLet = ''.join(oneLet)
sortOneLet = ''.join(sortOneLet)
# Get Reduced Amino Acid Representations.
flpA = amino_dict.replace_all(oneA, amino_dict.FLP)
flpB = amino_dict.replace_all(oneB, amino_dict.FLP)
flpC = amino_dict.replace_all(oneC, amino_dict.FLP)
flpD = amino_dict.replace_all(oneD, amino_dict.FLP)
flp = [flpA, flpB, flpC, flpD]
sortflp = sorted(flp)
flp = (''.join(flp)).upper()
sortflp = (''.join(sortflp)).upper()
# Calculate distances between the tetrahedra vertices.
AB = np.linalg.norm(cordA - cordB)
AC = np.linalg.norm(cordA - cordC)
AD = np.linalg.norm(cordA - cordD)
BC = np.linalg.norm(cordB - cordC)
BD = np.linalg.norm(cordB - cordD)
CD = np.linalg.norm(cordC - cordD)
# Calculate the tetrahedra Volume.
A_prime = cordA - cordD
B_prime = cordB - cordD
C_prime = cordC - cordD
primes = [A_prime, B_prime, C_prime]
primes = np.asarray(primes)
det = np.linalg.det(primes)
Vol = round((abs(det) / 6), 4)
# Sum of Edge Lengths.
SumL = (AB + AC + AD + BC + BD + CD)
SumL = round(SumL, 4)
# Average Edge Lengths.
AvgL = round((SumL / 6), 4)
# Deviation in Edge Lengths.
devLp = (AB - AvgL)**2
devLq = (AC - AvgL)**2
devLr = (AD - AvgL)**2
devLs = (BC - AvgL)**2
devLt = (BD - AvgL)**2
devLu = (CD - AvgL)**2
devLy = [devLp, devLq, devLr, devLs, devLt, devLu]
sumDevL = sum(devLy)
DevL = round(math.sqrt(sumDevL / 6.0), 4)
# Deviation in Tetrahedrality
lenArr = [AB, AC, AD, BC, BD, CD]
DevT = DevTetra(lenArr)
# Sum and Average Temperature Factors.
SumTF = round((a_tf + b_tf + c_tf + d_tf), 4)
AvgTF = round(SumTF / 4, 4)
# Data List
line = [
pdbid, oneLet, sortOneLet, flp, sortflp, one, two, three, four, AB,
AC, AD, BC, BD, CD, SumL, AvgL, DevL, DevT, Vol, a_tf, b_tf, c_tf,
d_tf, SumTF, AvgTF
]
Data.append(line)
return Data