def get_interface(st, dist):
''' Detects interface residues within a distance(dist)
Assumes two chains, i.e. a unique interface set per chain.
'''
select_ats = []
for at in st.get_atoms():
# Skip Hydrogens to reduce time
if at.element != 'H':
select_ats.append(at)
nbsearch = NeighborSearch(select_ats)
interface = {}
# Sets are more efficient than lists. Use sets when order is not relevant
for ch in st[0]:
interface[ch.id] = set()
for at1, at2 in nbsearch.search_all(dist):
#Only different chains
res1 = at1.get_parent()
ch1 = res1.get_parent()
res2 = at2.get_parent()
ch2 = res2.get_parent()
if ch1 != ch2:
interface[ch1.id].add(res1)
interface[ch2.id].add(res2)
return interface
def test_neighbor_search(self):
"""NeighborSearch: Find nearby randomly generated coordinates.
Based on the self test in Bio.PDB.NeighborSearch.
"""
# This comment stops black style adding a blank line here, which causes flake8 D202.
class RandomAtom:
def __init__(self):
self.coord = 100 * random(3)
def get_coord(self):
return self.coord
for i in range(0, 20):
atoms = [RandomAtom() for j in range(100)]
ns = NeighborSearch(atoms)
hits = ns.search_all(5.0)
self.assertTrue(isinstance(hits, list), hits)
self.assertTrue(len(hits) >= 0, hits)
x = array([250, 250, 250]) # Far away from our random atoms
self.assertEqual([], ns.search(x, 5.0, "A"))
self.assertEqual([], ns.search(x, 5.0, "R"))
self.assertEqual([], ns.search(x, 5.0, "C"))
self.assertEqual([], ns.search(x, 5.0, "M"))
self.assertEqual([], ns.search(x, 5.0, "S"))
def getInterface(struct, useCA=True, res2res_dist=8):
if useCA:
atomList = [
atom for atom in struct[0].get_atoms()
if atom.name.startswith("CA")
]
else:
atomList = [
atom for atom in struct[0].get_atoms()
if not atom.name.startswith("H")
]
chains = struct[0].child_list
searcher = NeighborSearch(atomList)
allNeigs = searcher.search_all(res2res_dist, level="R")
residuesBindingSitePerChain = {
chain.get_id(): {
"bindingSite": []
}
for chain in chains
}
for res1, res2 in allNeigs:
pdbId1, modelId1, chainId1, resId1 = res1.get_full_id()
pdbId2, modelId2, chainId2, resId2 = res2.get_full_id()
if chainId1 != chainId2:
residuesBindingSitePerChain[chainId1]["bindingSite"].append(
res1.get_id())
residuesBindingSitePerChain[chainId2]["bindingSite"].append(
res2.get_id())
return residuesBindingSitePerChain
def compute_interactions(pdb_name):
# Ensure that the PDB name is lowercase
pdb_name = pdb_name.lower()
# Get the pdb structure
s = Structure.objects.get(protein_conformation__protein__entry_name=pdb_name)
# Get the preferred chain
preferred_chain = s.preferred_chain.split(',')[0]
# Get the Biopython structure for the PDB
s = pdb_get_structure(pdb_name)
# Get all atoms
atom_list = Selection.unfold_entities(s.get_chains().__next__(), 'A')
# Search for all neighbouring residues
ns = NeighborSearch(atom_list)
all_neighbors = ns.search_all(4.5, "R")
# Filter all pairs containing non AA residues
all_aa_neighbors = [pair for pair in all_neighbors if is_aa(pair[0]) and is_aa(pair[1])]
# Only include contacts between residues less that NUM_SKIP_RESIDUES sequence steps apart
all_aa_neighbors = [pair for pair in all_aa_neighbors if abs(pair[0].id[1] - pair[1].id[1]) > NUM_SKIP_RESIDUES]
# For each pair of interacting residues, determine the type of interaction
interactions = [InteractingPair(res_pair[0], res_pair[1], get_interactions(res_pair[0], res_pair[1])) for res_pair in all_aa_neighbors]
# Split unto classified and unclassified.
classified = [interaction for interaction in interactions if len(interaction.get_interactions()) > 0]
return classified
def findNeigChains(struct, chainIdL, chainIdR, res2res_dist=6, minContacts=20):
searcher = NeighborSearch([
atom for atom in struct[0].get_atoms()
if atom is not None and not atom.name.startswith("H")
])
allNeigs = searcher.search_all(res2res_dist, level="C")
# print(allNeigs)
chainL = struct[0][chainIdL]
chainR = struct[0][chainIdR]
ligandChains = set([chainL])
receptorChains = set([chainR])
addedChains = ligandChains.union(receptorChains)
for neigsGroup in allNeigs:
searcher = NeighborSearch([
atom for chain in neigsGroup for atom in chain.get_atoms()
if atom is not None and not atom.name.startswith("H")
and atom.get_parent().resname != "HOH"
])
resNeigs = searcher.search_all(res2res_dist, level="R")
numContacts = 0
for r1, r2 in resNeigs:
if r1.get_parent().get_id() != r2.get_parent().get_id():
numContacts += 1
# print(neigsGroup, numContacts)
if numContacts < minContacts:
continue
if chainL in neigsGroup or chainR in neigsGroup:
for chain in neigsGroup:
if chain not in addedChains:
if chainR in neigsGroup and chainL in neigsGroup:
if bool(random.getrandbits(1)):
receptorChains.add(chain)
else:
ligandChains.add(chain)
addedChains.add(chain)
elif chainR in neigsGroup:
receptorChains.add(chain)
addedChains.add(chain)
elif chainL in neigsGroup:
ligandChains.add(chain)
addedChains.add(chain)
return ligandChains, receptorChains
def getPairsOfResiduesInContact(self, structureL, structureR):
'''
Computes which amino acids of ligand are in contact with which amino acids of receptor
@param structureL: Bio.PDB.Structure. Structure of ligand (bound state if available)
@param structureR: Bio.PDB.Structure. Structure of receptor (bound state if available).
@return positiveContacts: Set {(Bio.PDB.Residue.fullResId (from bound structure structureL), Bio.PDB.Residue.fullResId (from bound structure structureR))}
@return chainsNotContactL: Set { str(chainId structureL)}
@return chainsNotContactR: Set { str(chainId structureR)}
'''
try:
atomListL = [
atom for atom in structureL.child_list[0].get_atoms()
if not atom.name.startswith("H")
]
except IndexError:
raise NoValidPDBFile("Problems parsing pdbFile 1")
try:
atomListR = [
atom for atom in structureR.child_list[0].get_atoms()
if not atom.name.startswith("H")
]
except IndexError:
raise NoValidPDBFile("Problems parsing pdbFile 2")
searcher = NeighborSearch(atomListL + atomListR)
allNeigs = searcher.search_all(self.res2res_dist, level="R")
lStructId = structureL.get_id()
rStructId = structureR.get_id()
positiveContacts = set([])
chainsInContactL = set([])
chainsInContactR = set([])
for res1, res2 in allNeigs:
pdbId1, modelId1, chainId1, resId1 = res1.get_full_id()
pdbId2, modelId2, chainId2, resId2 = res2.get_full_id()
fullResId1 = res1.get_full_id()
fullResId2 = res2.get_full_id()
if pdbId1 == lStructId and pdbId2 == rStructId:
positiveContacts.add((fullResId1, fullResId2))
chainsInContactL.add(fullResId1[2])
chainsInContactR.add(fullResId2[2])
elif pdbId1 == rStructId and pdbId2 == lStructId:
positiveContacts.add((fullResId2, fullResId1))
chainsInContactL.add(fullResId2[2])
chainsInContactR.add(fullResId1[2])
if CONSIDER_HOMOOLIG_AS_POS:
positiveContacts, chainsInContactL, chainsInContactR = self.fixHomooligomers(
structureL, structureR, positiveContacts, chainsInContactL,
chainsInContactR)
allChainsL = set([elem.get_id() for elem in structureL[0].get_list()])
allChainsR = set([elem.get_id() for elem in structureR[0].get_list()])
chainsNotContactL = allChainsL.difference(chainsInContactL)
chainsNotContactR = allChainsR.difference(chainsInContactR)
return positiveContacts, chainsNotContactL, chainsNotContactR
def getPairsOfResiduesInContact(self, structureL, structureR):
'''
Computes which amino acids of ligand are in contact with which amino acids of receptor
:param structureL: Bio.PDB.Structure. Structure of ligand unbound state if available
:param structureR: Bio.PDB.Structure. Structure of receptor unbound state if available.
:return positiveContacts, chainsNotContactL, chainsNotContactR
positiveContacts: Set {( Bio.PDB.Residue.fullResId (from bound structure structureL),
Bio.PDB.Residue.fullResId (from bound structure structureR) )
}
chainsNotContactL: Set { Bio.PDB.Chain.get_id()} for ligand chains that are not in contact
chainsNotContactR: Set { Bio.PDB.Chain.get_id()} for receptor chains that are not in contact
'''
try:
atomListL = [
atom for atom in structureL.child_list[0].get_atoms()
if not atom.name.startswith("H")
]
except IndexError:
raise NoValidPDBFile("Problems parsing pdbFile 1")
try:
atomListR = [
atom for atom in structureR.child_list[0].get_atoms()
if not atom.name.startswith("H")
]
except IndexError:
raise NoValidPDBFile("Problems parsing pdbFile 2")
searcher = NeighborSearch(atomListL + atomListR)
allNeigs = searcher.search_all(self.res2res_dist, level="R")
lStructId = structureL.get_id()
rStructId = structureR.get_id()
positiveContactsResidues = set([])
chainsInContactL = set([])
chainsInContactR = set([])
for res1, res2 in allNeigs:
pdbId1, modelId1, chainId1, resId1 = res1.get_full_id()
pdbId2, modelId2, chainId2, resId2 = res2.get_full_id()
if pdbId1 == lStructId and pdbId2 == rStructId:
positiveContactsResidues.add((res1, res2))
chainsInContactL.add(chainId1)
chainsInContactR.add(chainId2)
elif pdbId1 == rStructId and pdbId2 == lStructId:
positiveContactsResidues.add((res2, res1))
chainsInContactL.add(chainId2)
chainsInContactR.add(chainId1)
allChainsL = set([elem.get_id() for elem in structureL[0].get_list()])
allChainsR = set([elem.get_id() for elem in structureR[0].get_list()])
chainsNotContactL = allChainsL.difference(chainsInContactL)
chainsNotContactR = allChainsR.difference(chainsInContactR)
return positiveContactsResidues, chainsNotContactL, chainsNotContactR
def test_neighbor_search(self):
"""NeighborSearch: Find nearby randomly generated coordinates.
Based on the self test in Bio.PDB.NeighborSearch.
"""
class RandomAtom:
def __init__(self):
self.coord = 100 * random(3)
def get_coord(self):
return self.coord
for i in range(0, 20):
atoms = [RandomAtom() for j in range(100)]
ns = NeighborSearch(atoms)
hits = ns.search_all(5.0)
self.assert_(hits >= 0)
def quick_neighbor_search_test():
#Based on the self test in Bio.PDB.NeighborSearch
from numpy.random import random
from Bio.PDB.NeighborSearch import NeighborSearch
class Atom:
def __init__(self):
self.coord=(100*random(3))
def get_coord(self):
return self.coord
for i in range(0, 20):
al = [Atom() for j in range(100)]
ns=NeighborSearch(al)
hits = ns.search_all(5.0)
assert hits >= 0
print "Done"
def test_neighbor_search(self):
"""NeighborSearch: Find nearby randomly generated coordinates.
Based on the self test in Bio.PDB.NeighborSearch.
"""
class RandomAtom:
def __init__(self):
self.coord = 100 * random(3)
def get_coord(self):
return self.coord
for i in range(0, 20):
atoms = [RandomAtom() for j in range(100)]
ns = NeighborSearch(atoms)
hits = ns.search_all(5.0)
self.assertTrue(isinstance(hits, list), hits)
self.assertTrue(len(hits) >= 0, hits)
def quick_neighbor_search_test():
#Based on the self test in Bio.PDB.NeighborSearch
from numpy.random import random
from Bio.PDB.NeighborSearch import NeighborSearch
class Atom:
def __init__(self):
self.coord = (100 * random(3))
def get_coord(self):
return self.coord
for i in range(0, 20):
al = [Atom() for j in range(100)]
ns = NeighborSearch(al)
hits = ns.search_all(5.0)
assert hits >= 0
print "Done"
def test_neighbor_search(self):
"""NeighborSearch: Find nearby randomly generated coordinates.
Based on the self test in Bio.PDB.NeighborSearch.
"""
class RandomAtom:
def __init__(self):
self.coord = 100 * random(3)
def get_coord(self):
return self.coord
for i in range(0, 20):
atoms = [RandomAtom() for j in range(100)]
ns = NeighborSearch(atoms)
hits = ns.search_all(5.0)
self.assertTrue(isinstance(hits, list), hits)
self.assertTrue(len(hits) >= 0, hits)
x = array([250,250,250]) #Far away from our random atoms
self.assertEqual([], ns.search(x, 5.0, "A"))
self.assertEqual([], ns.search(x, 5.0, "R"))
self.assertEqual([], ns.search(x, 5.0, "C"))
self.assertEqual([], ns.search(x, 5.0, "M"))
self.assertEqual([], ns.search(x, 5.0, "S"))
def get_backbone_links(struc, backbone_atoms, covlnk, join_models=True):
""" Get links making the main chain """
# TODO differenciate Protein and NA
cov_links = []
for mod in struc:
bckats = []
for atm in struc[mod.id].get_atoms():
if atm.id in backbone_atoms:
if atm.disordered_flag:
bckats.append(atm.selected_child)
else:
bckats.append(atm)
if bckats:
nbsearch = NeighborSearch(bckats)
for at1, at2 in nbsearch.search_all(covlnk):
if not same_residue(at1, at2) \
and (join_models or same_model(at1.get_parent(), at2.get_parent())):
cov_links.append(
sorted([at1, at2], key=lambda x: x.serial_number))
else:
print("Warning: No backbone atoms defined")
return cov_links
def get_contacts(atoms=[]):
neighbor_search = NeighborSearch(atoms)
neighbors = neighbor_search.search_all(radius=SEARCH_RADIUS)
#print("Size of Neighbhor Map: %d" % len(neighbors))
#neighbors_map: dictionary with tuple(ca_i,ca_j) as keys - eg: [(ca1,ca2):1,(ca21,ca45):1,...]
neighbors_map = {(x[0].get_serial_number(), x[1].get_serial_number()): 1
for x in neighbors}
no_of_native_contacts = len(neighbors_map)
contact_map = {}
n = len(atoms)
#constuct contact map
#total n(n-1)/2 entries, ( which excludes diagonal and symmetric entries from n*n pairs)
for x in range(1, n - 1):
for y in range((x + 1), n):
#put {(Ci,Cj):1} if present if present in neighbors map, 0 otherwise
contact_map[(x, y)] = neighbors_map.get(
(atoms[x].get_serial_number(), atoms[y].get_serial_number()),
0)
# print("In get_contacts:Size of atoms: %d :Length of the contact map: %d" % (n,len(contact_map)))
#print contact_map
return contact_map, no_of_native_contacts
def test_neighbor_search(self):
"""NeighborSearch: Find nearby randomly generated coordinates.
Based on the self test in Bio.PDB.NeighborSearch.
"""
class RandomAtom:
def __init__(self):
self.coord = 100 * random(3)
def get_coord(self):
return self.coord
for i in range(0, 20):
atoms = [RandomAtom() for j in range(100)]
ns = NeighborSearch(atoms)
hits = ns.search_all(5.0)
self.assertIsInstance(hits, list)
self.assertGreaterEqual(len(hits), 0)
x = array([250, 250, 250]) # Far away from our random atoms
self.assertEqual([], ns.search(x, 5.0, "A"))
self.assertEqual([], ns.search(x, 5.0, "R"))
self.assertEqual([], ns.search(x, 5.0, "C"))
self.assertEqual([], ns.search(x, 5.0, "M"))
self.assertEqual([], ns.search(x, 5.0, "S"))
def compute_interactions(pdb_name,save_to_db = False):
do_distances = True
do_interactions = True
distances = []
classified = []
# Ensure that the PDB name is lowercase
pdb_name = pdb_name.lower()
# Get the pdb structure
struc = Structure.objects.get(protein_conformation__protein__entry_name=pdb_name)
pdb_io = StringIO(struc.pdb_data.pdb)
# Get the preferred chain
preferred_chain = struc.preferred_chain.split(',')[0]
# Get the Biopython structure for the PDB
s = PDBParser(PERMISSIVE=True, QUIET=True).get_structure('ref', pdb_io)[0]
#s = pdb_get_structure(pdb_name)[0]
chain = s[preferred_chain]
#return classified, distances
# remove residues without GN and only those matching receptor.
residues = struc.protein_conformation.residue_set.exclude(generic_number=None).all().prefetch_related('generic_number')
dbres = {}
dblabel = {}
for r in residues:
dbres[r.sequence_number] = r
dblabel[r.sequence_number] = r.generic_number.label
ids_to_remove = []
for res in chain:
if not res.id[1] in dbres.keys() and res.get_resname() != "HOH":
ids_to_remove.append(res.id)
for i in ids_to_remove:
chain.detach_child(i)
if do_distances:
for i1,res1 in enumerate(chain,1):
if not is_water(res1):
for i2,res2 in enumerate(chain,1):
if i2>i1 and not is_water(res2):
# Do not calculate twice.
distance = res1['CA']-res2['CA']
distances.append((dbres[res1.id[1]],dbres[res2.id[1]],distance,dblabel[res1.id[1]],dblabel[res2.id[1]]))
if do_interactions:
atom_list = Selection.unfold_entities(s[preferred_chain], 'A')
# Search for all neighbouring residues
ns = NeighborSearch(atom_list)
all_neighbors = ns.search_all(4.5, "R")
# Filter all pairs containing non AA residues
all_aa_neighbors = [pair for pair in all_neighbors if is_aa(pair[0]) and is_aa(pair[1])]
# Only include contacts between residues less that NUM_SKIP_RESIDUES sequence steps apart
all_aa_neighbors = [pair for pair in all_aa_neighbors if abs(pair[0].id[1] - pair[1].id[1]) > NUM_SKIP_RESIDUES]
# For each pair of interacting residues, determine the type of interaction
interactions = [InteractingPair(res_pair[0], res_pair[1], get_interactions(res_pair[0], res_pair[1]),dbres[res_pair[0].id[1]], dbres[res_pair[1].id[1]],struc) for res_pair in all_aa_neighbors]
# Split unto classified and unclassified.
classified = [interaction for interaction in interactions if len(interaction.get_interactions()) > 0]
if save_to_db:
if do_interactions:
# Delete previous for faster load in
InteractingResiduePair.objects.filter(referenced_structure=struc).all().delete()
# bulk_pair = []
# for d in distances:
# pair = InteractingResiduePair(res1=d[0], res2=d[1], referenced_structure=struc)
# bulk_pair.append(pair)
# Create interaction dictionary
interaction_pairs = {}
for pair in classified:
res_1 = pair.get_residue_1()
res_2 = pair.get_residue_2()
key = res_1.get_parent().get_id()+str(res_1.get_id()[1]) + "_" + res_2.get_parent().get_id()+str(res_2.get_id()[1])
interaction_pairs[key] = pair
# POSSIBLE ADDON: support for multiple water-mediated bonds
## Obtain list of water molecules
water_list = { water for residue in s[preferred_chain] if residue.get_resname() == "HOH" for water in residue.get_atoms() }
if len(water_list) > 0:
## Iterate water molecules over residue atom list
water_neighbors = [(water, match_res) for water in water_list
for match_res in ns.search(water.coord, 3.5, "R") if not is_water(match_res)]
# intersect between residues sharing the same interacting water
for index_one in range(len(water_neighbors)):
water_pair_one = water_neighbors[index_one]
for index_two in [ index for index in range(index_one+1, len(water_neighbors)) if water_pair_one[0]==water_neighbors[index][0] ]:
water_pair_two = water_neighbors[index_two]
res_1 = water_pair_one[1]
res_2 = water_pair_two[1]
key = res_1.get_parent().get_id()+str(res_1.get_id()[1]) + "_" + res_2.get_parent().get_id()+str(res_2.get_id()[1])
# Check if interaction is polar - NOTE: this is not capturing every angle
if any(get_polar_interactions(water_pair_one[0].get_parent(), water_pair_one[1])) and any(get_polar_interactions(water_pair_two[0].get_parent(), water_pair_two[1])):
# NOTE: Is splitting of sidechain and backbone-mediated interactions desired?
if key in interaction_pairs:
interaction_pairs[key].interactions.append(WaterMediated())
else:
interaction_pairs[key] = InteractingPair(res_1, res_2, [WaterMediated()], dbres[res_1.id[1]], dbres[res_2.id[1]], struc)
for p in classified:
p.save_into_database()
if do_distances:
# Distance.objects.filter(structure=struc).all().delete()
bulk_distances = []
for i,d in enumerate(distances):
distance = Distance(distance=int(100*d[2]),res1=d[0], res2=d[1],gn1=d[3], gn2=d[4], gns_pair='_'.join([d[3],d[4]]), structure=struc)
bulk_distances.append(distance)
if len(bulk_distances)>1000:
pairs = Distance.objects.bulk_create(bulk_distances)
bulk_distances = []
pairs = Distance.objects.bulk_create(bulk_distances)
return classified, distances
#
# Simple program to search contacts
from Bio.PDB.NeighborSearch import NeighborSearch
from Bio.PDB.PDBParser import PDBParser
HBLNK = 3.5 # Define distance for a contact
parser = PDBParser(PERMISSIVE=1)
st = parser.get_structure('estructura', '1ubq.pdb')
selecc = []
for at in st.get_atoms():
selecc.append(at)
print("ATOM:", at)
#Selecting all atoms.
nbsearch = NeighborSearch(selecc)
print("NBSEARCH:")
#Searching for contacts under HBLNK
ncontact = 1
for at1, at2 in nbsearch.search_all(HBLNK):
print("Contact: ", ncontact)
print("at1", at1, at1.get_serial_number(), at1.get_parent().get_resname())
print("at2", at2, at2.get_serial_number(), at2.get_parent().get_resname())
print()
ncontact += 1
polar_ats = []
for at in st.get_atoms():
res = at.get_parent()
if res.id[0].startswith('H_') or res.id[0].startswith('W') and not args.hetatm:
continue
if at.element in POLAR:
polar_ats.append(at)
print (len(polar_ats), 'POLAR Atoms found')
# Preparing search
nbsearch = NeighborSearch(polar_ats)
at_pairs = nbsearch.search_all(HBLNK)
hbs = {}
for at1, at2 in sorted(at_pairs, key=lambda at_pair: at_pair[0].serial_number):
res1 = at1.get_parent()
res2 = at2.get_parent()
# remove atom pairs from the same residue and next in sequence
if res2.id[1] - res1.id[1] < 2:
continue
# using the contact with the shortest distance between residues
dist = at1 - at2
if res1 not in hbs:
hbs[res1] = {}
if res2 not in hbs[res1] or dist < hbs[res1][res2][2]:
hbs[res1][res2]= (at1, at2, dist)
def _get_contacts(ats_list, d_cutoff):
contact_list = []
nbsearch = NeighborSearch(ats_list)
for at1, at2 in nbsearch.search_all(d_cutoff):
contact_list.append((at1.get_parent(), at2.get_parent(), at1 - at2))
return contact_list
def main():
parser = argparse.ArgumentParser(
prog='polarContacts',
description='Polar contacts detector'
)
parser.add_argument(
'--backonly',
action='store_true',
dest='backonly',
help='Restrict to backbone'
)
parser.add_argument(
'--nowats',
action='store_true',
dest='nowats',
help='Exclude water molecules'
)
parser.add_argument('pdb_path')
args = parser.parse_args()
print ("Settings")
print ("--------")
for k,v in vars(args).items():
print ('{:10}:'.format(k),v)
backonly = args.backonly
nowats =args.nowats
pdb_path = args.pdb_path
if not pdb_path:
parser.print_help()
sys.exit(2)
parser = PDBParser(PERMISSIVE=1)
try:
st = parser.get_structure('st', pdb_path)
except OSError:
print ("#ERROR: loading PDB")
sys.exit(2)
# Checking for models
if len(st) > 1:
print ("#WARNING: Several Models found, using only first")
# Using Model 0 any way
st = st[0]
# Making a list of polar atoms
polats = []
if backonly:
selected_atoms = backbone_polars
else:
selected_atoms = all_polars
for at in st.get_atoms():
if at.id in selected_atoms:
polats.append(at)
#Searching for contacts under HNLNK on diferent residues
nbsearch = NeighborSearch(polats)
hblist = []
for at1, at2 in nbsearch.search_all(HBLNK):
if at1.get_parent() == at2.get_parent():
continue
#Discard covalents and neighbours
if (at1-at2) < COVLNK:
continue
if abs(at2.get_parent().id[1] - at1.get_parent().id[1]) == 1:
continue
# remove waters
if nowats:
if at1.get_parent().get_resname() in waternames \
or at2.get_parent().get_resname() in waternames:
continue
if at1.get_serial_number() < at2.get_serial_number():
hblist.append([StructureWrapper.Atom(at1,1),StructureWrapper.Atom(at2,1)])
else:
hblist.append([StructureWrapper.Atom(at2,1),StructureWrapper.Atom(at1,1)])
print ()
print ("Polar contacts")
print ('{:13} ({:4}, {:6}) {:13} ({:4}, {:6}) {:6} '.format(
'Atom1','Type','Charge','Atom2','type','charge','Dist (A)')
)
for hb in sorted (hblist,key=lambda i: i[0].at.get_serial_number()):
print ('{:14} {:14} {:6.3f} '.format(
hb[0].atid(),
hb[1].atid(),
hb[0].at - hb[1].at
)
)
parser = PDBParser(PERMISSIVE=1)
print ('Parsing', args.pdb_file)
# load structure from PDB file of PDB ifle handler
st = parser.get_structure('STR', args.pdb_file.name)
# collecting atom candidates
bck_atoms=[]
for at in st.get_atoms():
if at.id in PEP_BOND_ATS :
bck_atoms.append(at)
print (len(bck_atoms), 'candidate atoms found')
# Preparing search
nbsearch = NeighborSearch(bck_atoms)
at_pairs = nbsearch.search_all(COVLNK)
# Output sorted by atom,serial_number, nbsearch returns ordered pairs
# Redirect the output with > output_list
for at1, at2 in sorted(at_pairs, key=lambda at_pair: at_pair[0].serial_number):
# Discard same residue
if at1.get_parent() == at2.get_parent():
continue
print ('{:11} : {:11} {:8.3f}'.format(atom_id(at1),atom_id(at2), at1-at2))
def main():
parser = argparse.ArgumentParser(prog='polarContacts',
description='Polar contacts detector')
parser.add_argument('--backonly',
action='store_true',
dest='backonly',
help='Restrict to backbone')
parser.add_argument( #Argument to plot the different number of polar contacts that each residue has
'--plot',
action='store_true',
dest='plotMode',
help='Restrict to sidechain')
parser.add_argument('--nowats',
action='store_true',
dest='nowats',
help='Exclude water molecules')
parser.add_argument('--diel',
type=float,
action='store',
dest='diel',
default=1.0,
help='Relative dielectric constant')
parser.add_argument('--vdw',
action='store',
dest='vdwprm',
help='VDW Paramters file')
parser.add_argument('--rlib',
action='store',
dest='reslib',
help='AminoAcid library')
parser.add_argument(
'--index',
action='store',
dest='index',
help='Only select the molecules with more stable contact by an index')
parser.add_argument(
'--surf',
action='store_true',
dest='surf',
help='Usa ASA',
)
parser.add_argument('pdb_path')
args = parser.parse_args()
print("Settings")
print("--------")
for k, v in vars(args).items():
print('{:10}:'.format(k), v)
backonly = args.backonly
nowats = args.nowats
plotMode = args.plotMode
pdb_path = args.pdb_path
vdwprm = args.vdwprm
reslib = args.reslib
index = args.index
surf = args.surf
diel = args.diel
# Load VDW parameters
vdwParams = VdwParamset(vdwprm)
print("{} atom types loaded".format(vdwParams.ntypes))
# Load AA Library
aaLib = ResiduesDataLib(reslib)
print("{} amino acid atoms loaded".format(aaLib.nres))
if not pdb_path:
parser.print_help()
sys.exit(2)
parser = PDBParser(PERMISSIVE=1)
try:
st = parser.get_structure('st', pdb_path)
except OSError:
print("#ERROR: loading PDB")
sys.exit(2)
# Checking for model
if len(st) > 1:
print("#WARNING: Several Models found, using only first")
# Using Model 0 any way
st = st[0]
# Getting surfaces
if surf:
res_surfaces = NACCESS(
st,
naccess_binary=
'/Users/daniel/Downloads/BioPhysics-energies0/NACCESS/naccess')
at_surfaces = NACCESS_atomic(
st,
naccess_binary=
'/Users/daniel/Downloads/BioPhysics-energies0/NACCESS/naccess')
print("Surfaces obtained from NACCESS")
# Making a list of polar atoms
polats = []
if backonly:
selected_atoms = backbone_polars
else:
selected_atoms = all_polars
for at in st.get_atoms():
if at.id in selected_atoms:
polats.append(at)
#Searching for contacts under HNLNK on diferent residues
nbsearch = NeighborSearch(polats)
hblist = []
for at1, at2 in nbsearch.search_all(HBLNK):
if at1.get_parent() == at2.get_parent():
continue
#Discard covalents and neighbours
if (at1 - at2) < COVLNK:
continue
if abs(at2.get_parent().id[1] - at1.get_parent().id[1]) == 1:
continue
# remove waters
if nowats:
if at1.get_parent().get_resname() in waternames \
or at2.get_parent().get_resname() in waternames:
continue
atom1 = Atom(at1, 1, aaLib, vdwParams)
atom2 = Atom(at2, 1, aaLib, vdwParams)
if at1.get_serial_number() < at2.get_serial_number():
hblist.append([at1, at2])
else:
hblist.append([at2, at1])
print()
print("Polar contacts")
print('{:13} {:13} {:6} '.format('Atom1', 'Atom2', 'Dist (A)'))
if plotMode: #Dictionary to save as a key the residues with polar contacts and as values the number of contacts on sidechain or mainchain
sidecontacts = {}
maincontacts = {}
res = set(
) #A set to store the residues involved on polar contacts. stored as a set to avoid repeated residues
for hb in sorted(hblist, key=lambda i: i[0].get_serial_number()):
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
res.add(str(r1.id[1]) + r1.get_resname())
print('{:14} {:14} {:6.3f} '.format(
r1.get_resname() + ' ' + str(r1.id[1]) + hb[0].id,
r2.get_resname() + ' ' + str(r2.id[1]) + hb[1].id, hb[0] - hb[1]))
if surf:
print('{:14} ({:6.3f}) {:14} ({:6.3f}) {:6.3f} '.format(
hb[0].atid(),
float(hb[0].at.xtra['EXP_NACCESS']), hb[1].atid(),
float(hb[1].at.xtra['EXP_NACCESS']), hb[0].at - hb[1].at))
if plotMode:
if hb[0].id in backbone_polars and hb[1].id in backbone_polars:
if r1.id[1] not in maincontacts:
maincontacts[r1.id[1]] = 1
if r1.id[1] not in sidecontacts:
sidecontacts[r1.id[1]] = 0
else:
maincontacts[r1.id[1]] += 1
else:
if r1.id[1] not in sidecontacts:
if r1.id[1] not in maincontacts:
maincontacts[r1.id[1]] = 0
sidecontacts[r1.id[1]] = 1
else:
sidecontacts[r1.id[1]] += 1
if plotMode: #Plot the number of polar contacts of each residue on a bar graph.
#Contacts on mainchain are blue bars, contacts on sidechain red bars
N = len(res)
nindex = np.arange(N)
main_con = maincontacts.values()
side_con = sidecontacts.values()
fig, ax = plt.subplots()
bar_width = 0.35
rec1 = ax.bar(nindex,
main_con,
bar_width,
color='b',
label='Mainchain contacts')
rec2 = ax.bar(nindex + bar_width,
side_con,
bar_width,
color='r',
label='Sidechain contacts')
ax.set_xlabel('AminoAcid Number')
ax.set_ylabel('Number of polar contacts')
ax.set_xticks(nindex + bar_width / 2, res)
ax.set_xticklabels(res)
ax.legend()
fig.tight_layout()
plt.show()
print()
print("Residue interactions")
# Making list or residue pairs to avoid repeated pairs
respairs = []
for hb in hblist:
r1 = Residue(hb[0].get_parent(), 1, aaLib, vdwParams)
r2 = Residue(hb[1].get_parent(), 1, aaLib, vdwParams)
if [r1, r2] not in respairs:
respairs.append([r1, r2])
l = []
for rpair in sorted(respairs, key=lambda i: i[0].resNum()):
eint = rpair[0].elecInt(rpair[1], diel)
evdw = rpair[0].vdwInt(rpair[1])
print('{:10} {:10} {: 8.4f} {: 8.4f} {: 8.4f}'.format(
rpair[0].resid(), rpair[1].resid(), eint, evdw, eint + evdw))
l.append([
rpair[0].resid(), rpair[0], rpair[1].resid(), rpair[1], eint, evdw,
eint + evdw
])
if index is not None: #Select only the residues with less energy (the most stables ones)
for e, element in enumerate(sorted(l, key=lambda i: i[6])):
if e < int(index):
print(element)
if surf:
srfr1 = float(rpair[0].residue.xtra['EXP_NACCESS']['all_polar_rel'])
srfr2 = float(rpair[1].residue.xtra['EXP_NACCESS']['all_polar_rel'])
# Define 30% threshold for buried
if (srfr1 > 30.) or (srfr2 > 30.):
diel0 = 80.0
else:
diel0 = diel
eint = rpair[0].elecInt(rpair[1], diel0)
print(
'{:10} ({:>8.3f}) {:10} ({:>8.3f}) (e: {:4.1f}) {:>8.4f} {:>8.4f} {:>8.4f}'
.format(rpair[0].resid(), srfr1, rpair[1].resid(), srfr2, diel0,
eint, evdw, eint + evdw))
# load structure from PDB file
st = parser.get_structure('1UBQ', '1ubq.pdb')
select = []
#Select only CA atoms
for at in st.get_atoms():
if at.id == 'CA':
select.append(at)
print("ATOM:",
at.get_parent().get_resname(),
at.get_parent().id[1], at.id)
# Preparing search
nbsearch = NeighborSearch(select)
print("NBSEARCH:")
#Searching for contacts under HBLNK
ncontact = 1
for at1, at2 in nbsearch.search_all(MAXDIST):
print("Contact: ", ncontact)
print("at1", at1, at1.get_serial_number(), at1.get_parent().get_resname())
print("at2", at2, at2.get_serial_number(), at2.get_parent().get_resname())
print()
ncontact += 1
def main():
parser = argparse.ArgumentParser(prog='polarContacts',
description='Polar contacts detector')
parser.add_argument('--backonly',
action='store_true',
dest='backonly',
help='Restrict to backbone')
parser.add_argument('--nowats',
action='store_true',
dest='nowats',
help='Exclude water molecules')
parser.add_argument('--diel',
type=float,
action='store',
dest='diel',
default=1.0,
help='Relative dielectric constant')
parser.add_argument('--vdw',
action='store',
dest='vdwprm',
help='VDW Paramters file')
parser.add_argument('--rlib',
action='store',
dest='reslib',
help='AminoAcid library')
parser.add_argument('pdb_path')
args = parser.parse_args()
print("Settings")
print("--------")
for k, v in vars(args).items():
print('{:10}:'.format(k), v)
backonly = args.backonly
nowats = args.nowats
pdb_path = args.pdb_path
vdwprm = args.vdwprm
reslib = args.reslib
diel = args.diel
# Load VDW parameters
vdwParams = VdwParamset(vdwprm)
print("{} atom types loaded".format(vdwParams.ntypes))
# Load AA Library
aaLib = ResiduesDataLib(reslib)
print("{} amino acid atoms loaded".format(aaLib.nres))
if not pdb_path:
parser.print_help()
sys.exit(2)
parser = PDBParser(PERMISSIVE=1)
try:
st = parser.get_structure('st', pdb_path)
except OSError:
print("#ERROR: loading PDB")
sys.exit(2)
# Checking for models
if len(st) > 1:
print("#WARNING: Several Models found, using only first")
# Using Model 0 any way
st = st[0]
# Making a list of polar atoms
polats = []
if backonly:
selected_atoms = backbone_polars
else:
selected_atoms = all_polars
for at in st.get_atoms():
if at.id in selected_atoms:
polats.append(at)
#Searching for contacts under HNLNK on diferent residues
nbsearch = NeighborSearch(polats)
hblist = []
for at1, at2 in nbsearch.search_all(HBLNK):
if at1.get_parent() == at2.get_parent():
continue
#Discard covalents and neighbours
if (at1 - at2) < COVLNK:
continue
if abs(at2.get_parent().id[1] - at1.get_parent().id[1]) == 1:
continue
# remove waters
if nowats:
if at1.get_parent().get_resname() in waternames \
or at2.get_parent().get_resname() in waternames:
continue
# atom1 = Atom(at1,1,aaLib,vdwParams)
# atom2 = Atom(at2,1,aaLib,vdwParams)
if at1.get_serial_number() < at2.get_serial_number():
hblist.append([at1, at2])
else:
hblist.append([at2, at1])
print()
print("Polar contacts")
print('{:13} {:13} {:6} '.format('Atom1', 'Atom2', 'Dist (A)'))
for hb in sorted(hblist, key=lambda i: i[0].get_serial_number()):
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
print('{:14} {:14} {:6.3f} '.format(
r1.get_resname() + ' ' + str(r1.id[1]) + hb[0].id,
r2.get_resname() + ' ' + str(r2.id[1]) + hb[1].id, hb[0] - hb[1]))
print()
print("Residue interactions")
# Making list or residue pairs to avoid repeated pairs
respairs = []
for hb in hblist:
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
if [r1, r2] not in respairs:
respairs.append([r1, r2])
l = []
for rpair in sorted(respairs, key=lambda i: i[0].id[1]):
eint = 0.
evdw = 0.
for at1 in rpair[0].get_atoms():
resid1 = rpair[0].get_resname()
atid1 = at1.id
atparam1 = aaLib.getParams(resid1, atid1)
vdwprm1 = vdwParams.atTypes[atparam1.atType]
for at2 in rpair[1].get_atoms():
resid2 = rpair[1].get_resname()
atid2 = at2.id
atparam2 = aaLib.getParams(resid2, atid2)
vdwprm2 = vdwParams.atTypes[atparam2.atType]
eint = eint + 332.16 * atparam1.charg * atparam2.charg / diel / (
at1 - at2)
eps = math.sqrt(vdwprm1.eps * vdwprm2.eps)
sig = math.sqrt(vdwprm1.sig * vdwprm2.sig)
evdw = evdw + 4 * eps * ((sig / (at1 - at2))**12 -
(sig / (at1 - at2))**6)
print(resid1, rpair[0].id[1], resid2, rpair[1].id[1], eint, evdw,
eint + evdw)
l.append([
resid1, rpair[0].id[1], resid2, rpair[1].id[1], eint, evdw,
eint + evdw
])
#here we have the code for finding the most stable contacts and plotting each energy component
#(global, electrostatic and vdw) with respect to the residue number involved in these contacts
print("Five most stable contacts")
stable = []
for index, element in enumerate(sorted(l, key=lambda i: i[6])):
if index < 5:
stable.append(element)
print(element)
n_groups = 5
eint = (-96.879048334060371, -89.262401988309293, -63.650369322307412,
-51.488465980661772, -50.345308360049728)
vdw = (-1.0577685827505385, -1.5931226867662258, 1.5038605656892994,
-2.9601475910966375, -2.0108017155800604)
etot = (-97.936816916810912, -90.855524675075515, -62.146508756618111,
-54.448613571758408, -52.356110075629786)
fig, ax = plt.subplots()
index = np.arange(n_groups)
bar_width = 0.15
opacity = 0.5
inf1 = plt.bar(index,
eint,
bar_width,
alpha=opacity,
color='b',
label='electrostatic energies')
inf2 = plt.bar(index + bar_width,
vdw,
bar_width,
alpha=opacity,
color='g',
label='van der waals energies')
inf3 = plt.bar(index + bar_width,
etot,
bar_width,
alpha=opacity,
color='r',
label='total energies')
plt.title('Energies for pair of residues')
plt.xlabel('Contacts')
plt.ylabel('Energy')
plt.xticks(index + bar_width,
('LYS-ASP', 'LYS-GLU', 'GLU-LYS', 'GLU-ARG', 'ASP-ARG'))
plt.legend()
plt.tight_layout()
plt.show()
both_main_x = []
both_main_y = []
both_side_x = []
both_side_y = []
main_side_x = []
main_side_y = []
side_main_x = []
side_main_y = []
for hb in sorted(hblist, key=lambda i: i[0].get_serial_number()):
if hb[0].id in backbone_polars:
where0 = 'main'
else:
where0 = 'side'
if hb[1].id in backbone_polars:
where1 = 'main'
else:
where1 = 'side'
label = where0 + ':' + where1
if label[0] == label[5] and label[0] == 'm':
value = 1
both_main_x.append(hb[0].get_parent().id[1])
both_main_y.append(hb[1].get_parent().id[1])
elif label[0] == label[5] and label[0] == 's':
value = 2
both_side_x.append(hb[0].get_parent().id[1])
both_side_y.append(hb[1].get_parent().id[1])
elif label[0] != label[5] and label[0] == 'm':
value = 3
main_side_x.append(hb[0].get_parent().id[1])
main_side_y.append(hb[1].get_parent().id[1])
elif label[0] != label[5] and label[0] == 's':
value = 4
side_main_x.append(hb[0].get_parent().id[1])
side_main_y.append(hb[1].get_parent().id[1])
linking = [label, value, hb[0].id, hb[1].id, hb[0] - hb[1]]
print('{:14}{:14}{:14}{:14}{:6.3f}'.format(label, value, hb[0].id,
hb[1].id, hb[0] - hb[1]))
plt.figure(figsize=(10, 8))
plt.scatter(both_main_x, both_main_y, c='red', label='both_main')
plt.scatter(both_side_x, both_side_y, c='green', label='both_side')
plt.scatter(main_side_x, main_side_y, c='blue', label='main_side')
plt.scatter(side_main_x, side_main_y, c='yellow', label='side_main')
plt.title('Interaction')
plt.xlabel('Residue1 number')
plt.ylabel('Residue2 number')
plt.legend(loc='upper right')
plt.show()
#The surface residues are the ones with an Area<5 (http://cib.cf.ocha.ac.jp/bitool/ASA/display.php?id=1513152459.2996)
surface_res = [['ILE', 3], ['VAL', 5], ['ILE', 23], ['VAL', 26],
['ILE', 30], ['GLN', 41], ['LEU', 43], ['LEU', 56],
['ILE', 61], ['LEU', 67], ['LEU', 69]]
for rpair in sorted(respairs, key=lambda i: i[0].id[1]):
eint = 0.
for atom1 in rpair[0].get_atoms():
resname1 = rpair[0].get_resname()
atid1 = at1.id
atparam1 = aaLib.getParams(resid1, atid1)
for atom2 in rpair[1].get_atoms():
resname2 = rpair[1].get_resname()
atid2 = at2.id
atparam2 = aaLib.getParams(resid2, atid2)
for values in surface_res:
for values2 in surface_res:
if resname1 == values[0] and rpair[0].id[1] == values[
1]:
if resname2 == values2[0] and rpair[1].id[
1] == values2[1]:
eint = eint + 80 * atparam1.charg * atparam2.charg / diel / (
atom1 - atom2)
if eint != 0:
print(resid1, rpair[0].id[1], resid2, rpair[1].id[1], eint, evdw,
eint + evdw)
def compute_interactions(pdb_name, save_to_db=False):
do_distances = True
do_interactions = True
distances = []
classified = []
# Ensure that the PDB name is lowercase
pdb_name = pdb_name.lower()
# Get the pdb structure
struc = Structure.objects.get(
protein_conformation__protein__entry_name=pdb_name)
pdb_io = StringIO(struc.pdb_data.pdb)
# Get the preferred chain
preferred_chain = struc.preferred_chain.split(',')[0]
# Get the Biopython structure for the PDB
s = PDBParser(PERMISSIVE=True, QUIET=True).get_structure('ref', pdb_io)[0]
#s = pdb_get_structure(pdb_name)[0]
chain = s[preferred_chain]
#return classified, distances
# remove residues without GN and only those matching receptor.
residues = struc.protein_conformation.residue_set.exclude(
generic_number=None).all().prefetch_related('generic_number')
dbres = {}
dblabel = {}
for r in residues:
dbres[r.sequence_number] = r
dblabel[r.sequence_number] = r.generic_number.label
ids_to_remove = []
for res in chain:
if not res.id[1] in dbres.keys() and res.get_resname() != "HOH":
ids_to_remove.append(res.id)
for i in ids_to_remove:
chain.detach_child(i)
if do_distances:
for i1, res1 in enumerate(chain, 1):
if not is_water(res1):
for i2, res2 in enumerate(chain, 1):
if i2 > i1 and not is_water(res2):
# Do not calculate twice.
distance = res1['CA'] - res2['CA']
distances.append(
(dbres[res1.id[1]], dbres[res2.id[1]], distance,
dblabel[res1.id[1]], dblabel[res2.id[1]]))
if do_interactions:
atom_list = Selection.unfold_entities(s[preferred_chain], 'A')
# Search for all neighbouring residues
ns = NeighborSearch(atom_list)
all_neighbors = ns.search_all(6.6, "R")
# Filter all pairs containing non AA residues
all_aa_neighbors = [
pair for pair in all_neighbors if is_aa(pair[0]) and is_aa(pair[1])
]
# Only include contacts between residues less that NUM_SKIP_RESIDUES sequence steps apart
all_aa_neighbors = [
pair for pair in all_aa_neighbors
if abs(pair[0].id[1] - pair[1].id[1]) > NUM_SKIP_RESIDUES
]
# For each pair of interacting residues, determine the type of interaction
interactions = [
InteractingPair(res_pair[0], res_pair[1], dbres[res_pair[0].id[1]],
dbres[res_pair[1].id[1]], struc)
for res_pair in all_aa_neighbors
if not is_water(res_pair[0]) and not is_water(res_pair[1])
]
# Split unto classified and unclassified.
classified = [
interaction for interaction in interactions
if len(interaction.get_interactions()) > 0
]
if save_to_db:
if do_interactions:
# Delete previous for faster load in
InteractingResiduePair.objects.filter(
referenced_structure=struc).all().delete()
# bulk_pair = []
# for d in distances:
# pair = InteractingResiduePair(res1=d[0], res2=d[1], referenced_structure=struc)
# bulk_pair.append(pair)
# Create interaction dictionary
interaction_pairs = {}
for pair in classified:
res_1 = pair.get_residue_1()
res_2 = pair.get_residue_2()
key = res_1.get_parent().get_id() + str(res_1.get_id(
)[1]) + "_" + res_2.get_parent().get_id() + str(
res_2.get_id()[1])
interaction_pairs[key] = pair
# POSSIBLE ADDON: support for multiple water-mediated bonds
## Obtain list of water molecules
water_list = {
water
for residue in s[preferred_chain]
if residue.get_resname() == "HOH"
for water in residue.get_atoms()
}
if len(water_list) > 0:
## Iterate water molecules over residue atom list
water_neighbors = [
(water, match_res) for water in water_list
for match_res in ns.search(water.coord, 3.5, "R")
if not is_water(match_res) and (
is_hba(match_res) or is_hbd(match_res))
]
# intersect between residues sharing the same interacting water
for index_one in range(len(water_neighbors)):
water_pair_one = water_neighbors[index_one]
for index_two in [
index for index in range(index_one +
1, len(water_neighbors))
if water_pair_one[0] == water_neighbors[index][0]
]:
water_pair_two = water_neighbors[index_two]
res_1 = water_pair_one[1]
res_2 = water_pair_two[1]
# TODO: order residues + check minimum spacing between residues
key = res_1.get_parent().get_id() + str(res_1.get_id(
)[1]) + "_" + res_2.get_parent().get_id() + str(
res_2.get_id()[1])
# Verify h-bonds between water and both residues
matches_one = InteractingPair.verify_water_hbond(
water_pair_one[1], water_pair_one[0])
matches_two = InteractingPair.verify_water_hbond(
water_pair_two[1], water_pair_two[0])
if len(matches_one) > 0 and len(matches_two) > 0:
# if not exists, create residue pair without interactions
if not key in interaction_pairs:
interaction_pairs[key] = InteractingPair(
res_1, res_2, dbres[res_1.id[1]],
dbres[res_2.id[1]], struc)
for a, b in zip(matches_one, matches_two):
# HACK: store water ID as part of first atom name
interaction_pairs[key].interactions.append(
WaterMediated(
a +
"|" + str(water_pair_one[0].get_parent(
).get_id()[1]), b))
for p in classified:
p.save_into_database()
if do_distances:
# Distance.objects.filter(structure=struc).all().delete()
bulk_distances = []
for i, d in enumerate(distances):
distance = Distance(distance=int(100 * d[2]),
res1=d[0],
res2=d[1],
gn1=d[3],
gn2=d[4],
gns_pair='_'.join([d[3], d[4]]),
structure=struc)
bulk_distances.append(distance)
if len(bulk_distances) > 1000:
pairs = Distance.objects.bulk_create(bulk_distances)
bulk_distances = []
pairs = Distance.objects.bulk_create(bulk_distances)
return classified, distances
def compute_interactions(pdb_name, save_to_db=False):
do_distances = True
do_interactions = True
do_complexes = True
distances = []
classified = []
classified_complex = []
# Ensure that the PDB name is lowercase
pdb_name = pdb_name.lower()
# Get the pdb structure
struc = Structure.objects.get(
protein_conformation__protein__entry_name=pdb_name)
pdb_io = StringIO(struc.pdb_data.pdb)
# Get the preferred chain
preferred_chain = struc.preferred_chain.split(',')[0]
# Get the Biopython structure for the PDB
s = PDBParser(PERMISSIVE=True, QUIET=True).get_structure('ref', pdb_io)[0]
#s = pdb_get_structure(pdb_name)[0]
chain = s[preferred_chain]
#return classified, distances
# remove residues without GN and only those matching receptor.
residues = struc.protein_conformation.residue_set.exclude(
generic_number=None).all().prefetch_related('generic_number')
dbres = {}
dblabel = {}
for r in residues:
dbres[r.sequence_number] = r
dblabel[r.sequence_number] = r.generic_number.label
ids_to_remove = []
for res in chain:
if not res.id[1] in dbres.keys() and res.get_resname() != "HOH":
ids_to_remove.append(res.id)
for i in ids_to_remove:
chain.detach_child(i)
if do_distances:
for i1, res1 in enumerate(chain, 1):
if not is_water(res1):
for i2, res2 in enumerate(chain, 1):
if i2 > i1 and not is_water(res2):
# Do not calculate twice.
distance = res1['CA'] - res2['CA']
distances.append(
(dbres[res1.id[1]], dbres[res2.id[1]], distance,
dblabel[res1.id[1]], dblabel[res2.id[1]]))
if do_interactions:
atom_list = Selection.unfold_entities(s[preferred_chain], 'A')
# Search for all neighbouring residues
ns = NeighborSearch(atom_list)
all_neighbors = ns.search_all(6.6, "R")
# Filter all pairs containing non AA residues
all_aa_neighbors = [
pair for pair in all_neighbors if is_aa(pair[0]) and is_aa(pair[1])
]
# Only include contacts between residues more than NUM_SKIP_RESIDUES sequence steps apart
all_aa_neighbors = [
pair for pair in all_aa_neighbors
if abs(pair[0].id[1] - pair[1].id[1]) > NUM_SKIP_RESIDUES
]
# For each pair of interacting residues, determine the type of interaction
interactions = [
InteractingPair(res_pair[0], res_pair[1], dbres[res_pair[0].id[1]],
dbres[res_pair[1].id[1]], struc)
for res_pair in all_aa_neighbors
if not is_water(res_pair[0]) and not is_water(res_pair[1])
]
# Split unto classified and unclassified.
classified = [
interaction for interaction in interactions
if len(interaction.get_interactions()) > 0
]
if do_complexes:
try:
# check if structure in signprot_complex
complex = SignprotComplex.objects.get(structure=struc)
# Get all GPCR residue atoms based on preferred chain
gpcr_atom_list = [ atom for residue in Selection.unfold_entities(s[preferred_chain], 'R') if is_aa(residue) \
for atom in residue.get_atoms()]
# Get all residue atoms from the coupled protein (e.g. G-protein)
# NOW: select alpha subnit protein chain using complex model
sign_atom_list = [ atom for residue in Selection.unfold_entities(s[complex.alpha], 'R') if is_aa(residue) \
for atom in residue.get_atoms()]
ns_gpcr = NeighborSearch(gpcr_atom_list)
ns_sign = NeighborSearch(sign_atom_list)
# For each GPCR atom perform the neighbor search on the signaling protein
all_neighbors = {
(gpcr_atom.parent, match_res)
for gpcr_atom in gpcr_atom_list
for match_res in ns_sign.search(gpcr_atom.coord, 4.5, "R")
}
# For each pair of interacting residues, determine the type of interaction
residues_sign = ProteinConformation.objects.get(
protein__entry_name=pdb_name + "_" +
complex.alpha.lower()).residue_set.exclude(
generic_number=None).all().prefetch_related(
'generic_number')
# grab labels from sign protein
dbres_sign = {}
dblabel_sign = {}
for r in residues_sign:
dbres_sign[r.sequence_number] = r
dblabel_sign[r.sequence_number] = r.generic_number.label
# Find interactions
interactions = [
InteractingPair(res_pair[0], res_pair[1],
dbres[res_pair[0].id[1]],
dbres_sign[res_pair[1].id[1]], struc)
for res_pair in all_neighbors if res_pair[0].id[1] in dbres
and res_pair[1].id[1] in dbres_sign
]
# Filter unclassified interactions
classified_complex = [
interaction for interaction in interactions
if len(interaction.get_interactions()) > 0
]
# Convert to dictionary for water calculations
interaction_pairs = {}
for pair in classified_complex:
res_1 = pair.get_residue_1()
res_2 = pair.get_residue_2()
key = res_1.get_parent().get_id() + str(res_1.get_id(
)[1]) + "_" + res_2.get_parent().get_id() + str(
res_2.get_id()[1])
interaction_pairs[key] = pair
# Obtain list of all water molecules in the structure
water_list = {
water
for chain in s for residue in chain
if residue.get_resname() == "HOH"
for water in residue.get_atoms()
}
# If waters are present calculate water-mediated interactions
if len(water_list) > 0:
## Iterate water molecules over coupled and gpcr atom list
water_neighbors_gpcr = {
(water, match_res)
for water in water_list
for match_res in ns_gpcr.search(water.coord, 3.5, "R")
}
water_neighbors_sign = {
(water, match_res)
for water in water_list
for match_res in ns_sign.search(water.coord, 3.5, "R")
}
# TODO: DEBUG AND VERIFY this code as water-mediated interactions were present at this time
# 1. UPDATE complexes to include also mini Gs and peptides (e.g. 4X1H/6FUF/5G53)
# 2. Run and verify water-mediated do_interactions
# 3. Improve the intersection between the two hit lists
## TODO: cleaner intersection between hits from the two Lists
# see new code below
# for water_pair_one in water_neighbors_gpcr:
# for water_pair_two in water_neighbors_sign:
# if water_pair_one[0]==water_pair_two[0]:
# res_1 = water_pair_one[1]
# res_2 = water_pair_two[1]
# key = res_1.get_parent().get_id()+str(res_1.get_id()[1]) + "_" + res_2.get_parent().get_id()+str(res_2.get_id()[1])
# Check if interaction is polar
# if any(get_polar_interactions(water_pair_one[0].get_parent(), water_pair_one[1])) and any(get_polar_interactions(water_pair_two[0].get_parent(), water_pair_two[1])):
# TODO Check if water interaction is already present (e.g. multiple waters)
# TODO Is splitting of sidechain and backbone-mediated interactions desired?
# if not key in interaction_pairs:
# interaction_pairs[key] = InteractingPair(res_1, res_2, dbres[res_1.id[1]], dbres_sign[res_2.id[1]], struc)
# TODO: fix assignment of interacting atom labels (now seems limited to residues)
# interaction_pairs[key].interactions.append(WaterMediated(a + "|" + str(water_pair_one[0].get_parent().get_id()[1]), b))
except SignprotComplex.DoesNotExist:
# print("No complex definition found for", pdb_name)
log = "No complex definition found for " + pdb_name
except ProteinConformation.DoesNotExist:
print(
"No protein conformation definition found for signaling protein of ",
pdb_name)
# log = "No protein conformation definition found for signaling protein of " + pdb_name
if save_to_db:
if do_interactions:
# Delete previous for faster load in
InteractingResiduePair.objects.filter(
referenced_structure=struc).all().delete()
# bulk_pair = []
# for d in distances:
# pair = InteractingResiduePair(res1=d[0], res2=d[1], referenced_structure=struc)
# bulk_pair.append(pair)
# Create interaction dictionary
interaction_pairs = {}
for pair in classified:
res_1 = pair.get_residue_1()
res_2 = pair.get_residue_2()
key = res_1.get_parent().get_id() + str(res_1.get_id(
)[1]) + "_" + res_2.get_parent().get_id() + str(
res_2.get_id()[1])
interaction_pairs[key] = pair
# POSSIBLE ADDON: support for multiple water-mediated bonds
## Obtain list of water molecules
water_list = {
water
for residue in s[preferred_chain]
if residue.get_resname() == "HOH"
for water in residue.get_atoms()
}
if len(water_list) > 0:
## Iterate water molecules over residue atom list
water_neighbors = [
(water, match_res) for water in water_list
for match_res in ns.search(water.coord, 3.5, "R")
if not is_water(match_res) and (
is_hba(match_res) or is_hbd(match_res))
]
# intersect between residues sharing the same interacting water
for index_one in range(len(water_neighbors)):
water_pair_one = water_neighbors[index_one]
for index_two in [
index for index in range(index_one +
1, len(water_neighbors))
if water_pair_one[0] == water_neighbors[index][0]
]:
water_pair_two = water_neighbors[index_two]
res_1 = water_pair_one[1]
res_2 = water_pair_two[1]
# TODO: order residues + check minimum spacing between residues
key = res_1.get_parent().get_id() + str(res_1.get_id(
)[1]) + "_" + res_2.get_parent().get_id() + str(
res_2.get_id()[1])
# Verify h-bonds between water and both residues
matches_one = InteractingPair.verify_water_hbond(
water_pair_one[1], water_pair_one[0])
matches_two = InteractingPair.verify_water_hbond(
water_pair_two[1], water_pair_two[0])
if len(matches_one) > 0 and len(matches_two) > 0:
# if not exists, create residue pair without interactions
if not key in interaction_pairs:
interaction_pairs[key] = InteractingPair(
res_1, res_2, dbres[res_1.id[1]],
dbres[res_2.id[1]], struc)
for a, b in zip(matches_one, matches_two):
# HACK: store water ID as part of first atom name
interaction_pairs[key].interactions.append(
WaterMediated(
a +
"|" + str(water_pair_one[0].get_parent(
).get_id()[1]), b))
for p in classified:
p.save_into_database()
if do_complexes:
for pair in classified_complex:
pair.save_into_database()
if do_distances:
# Distance.objects.filter(structure=struc).all().delete()
bulk_distances = []
for i, d in enumerate(distances):
distance = Distance(distance=int(100 * d[2]),
res1=d[0],
res2=d[1],
gn1=d[3],
gn2=d[4],
gns_pair='_'.join([d[3], d[4]]),
structure=struc)
bulk_distances.append(distance)
if len(bulk_distances) > 1000:
pairs = Distance.objects.bulk_create(bulk_distances)
bulk_distances = []
pairs = Distance.objects.bulk_create(bulk_distances)
return classified, distances
def main():
parser = argparse.ArgumentParser(prog='polarContacts',
description='Polar contacts detector')
parser.add_argument('--backonly',
action='store_true',
dest='backonly',
help='Restrict to backbone')
parser.add_argument('--nowats',
action='store_true',
dest='nowats',
help='Exclude water molecules')
parser.add_argument('--diel',
type=float,
action='store',
dest='diel',
default=1.0,
help='Relative dielectric constant')
parser.add_argument('--vdw',
action='store',
dest='vdwprm',
help='VDW Paramters file')
parser.add_argument('--rlib',
action='store',
dest='reslib',
help='AminoAcid library')
parser.add_argument('pdb_path')
args = parser.parse_args()
print("Settings")
print("--------")
for k, v in vars(args).items():
print('{:10}:'.format(k), v)
backonly = args.backonly
nowats = args.nowats
pdb_path = args.pdb_path
vdwprm = args.vdwprm
reslib = args.reslib
diel = args.diel
# Load VDW parameters
vdwParams = VdwParamset(vdwprm)
print("{} atom types loaded".format(vdwParams.ntypes))
# Load AA Library
aaLib = ResiduesDataLib(reslib)
print("{} amino acid atoms loaded".format(aaLib.nres))
if not pdb_path:
parser.print_help()
sys.exit(2)
parser = PDBParser(PERMISSIVE=1)
try:
st = parser.get_structure('st', pdb_path)
except OSError:
print("#ERROR: loading PDB")
sys.exit(2)
# Checking for models
if len(st) > 1:
print("#WARNING: Several Models found, using only first")
# Using Model 0 any way
st = st[0]
# Making a list of polar atoms
polats = []
if backonly:
selected_atoms = backbone_polars
else:
selected_atoms = all_polars
for at in st.get_atoms():
if at.id in selected_atoms:
polats.append(at)
#Searching for contacts under HNLNK on diferent residues
nbsearch = NeighborSearch(polats)
hblist = []
for at1, at2 in nbsearch.search_all(HBLNK):
if at1.get_parent() == at2.get_parent():
continue
#Discard covalents and neighbours
if (at1 - at2) < COVLNK:
continue
if abs(at2.get_parent().id[1] - at1.get_parent().id[1]) == 1:
continue
# remove waters
if nowats:
if at1.get_parent().get_resname() in waternames \
or at2.get_parent().get_resname() in waternames:
continue
# atom1 = Atom(at1,1,aaLib,vdwParams)
# atom2 = Atom(at2,1,aaLib,vdwParams)
if at1.get_serial_number() < at2.get_serial_number():
hblist.append([at1, at2])
else:
hblist.append([at2, at1])
print()
print("Polar contacts")
print('{:13} {:13} {:6} '.format('Atom1', 'Atom2', 'Dist (A)'))
for hb in sorted(hblist, key=lambda i: i[0].get_serial_number()):
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
print('{:14} {:14} {:6.3f} '.format(
r1.get_resname() + ' ' + str(r1.id[1]) + hb[0].id,
r2.get_resname() + ' ' + str(r2.id[1]) + hb[1].id, hb[0] - hb[1]))
print()
print("Residue interactions")
# Making list or residue pairs to avoid repeated pairs
respairs = []
for hb in hblist:
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
if [r1, r2] not in respairs:
respairs.append([r1, r2])
list5 = []
for rpair in sorted(respairs, key=lambda i: i[0].id[1]):
eint = 0.
evdw = 0.
for at1 in rpair[0].get_atoms():
resid1 = rpair[0].get_resname()
atid1 = at1.id
atparam1 = aaLib.getParams(resid1, atid1)
vdwprm1 = vdwParams.atTypes[atparam1.atType]
for at2 in rpair[1].get_atoms():
resid2 = rpair[1].get_resname()
atid2 = at2.id
atparam2 = aaLib.getParams(resid2, atid2)
vdwprm2 = vdwParams.atTypes[atparam2.atType]
eint = eint + 332.16 * atparam1.charg * atparam2.charg / diel / (
at1 - at2)
eps = math.sqrt(vdwprm1.eps * vdwprm2.eps)
sig = math.sqrt(vdwprm1.sig * vdwprm2.sig)
evdw = evdw + 4 * eps * ((sig / (at1 - at2))**12 -
(sig / (at1 - at2))**6)
list5.append((resid1, rpair[0].id[1], resid2, rpair[1].id[1], eint,
evdw, eint + evdw)) #list all
print(resid1, rpair[0].id[1], resid2, rpair[1].id[1], eint, evdw,
eint +
evdw) # eint= electrostatic, evdw= vanderwaals, eint+evdw= total
i = 0
for element in sorted(list5, key=lambda li: li[6]): #sort list
i += 1
print(element)
if i == 5:
break #gives you the 5 first atoms
for k, v in vars(args).items():
print('{:10}:'.format(k), v)
print("PDB.filename:", args.pdb_file.name)
parser = PDBParser(PERMISSIVE=1)
print('Parsing', args.pdb_file)
# load structure from PDB file of PDB ifle handler
st = parser.get_structure('STR', args.pdb_file.name)
# collecting CA atoms
ca_atoms = []
for at in st.get_atoms():
if at.id == 'CA':
ca_atoms.append(at)
print(len(ca_atoms), 'CA Atoms found')
# Preparing search
nbsearch = NeighborSearch(ca_atoms)
at_pairs = nbsearch.search_all(args.max_dist)
# Output sorted by atom,serial_number, nbsearch returns ordered pairs
# Redirect the output with > output_list
for at1, at2 in sorted(at_pairs, key=lambda at_pair: at_pair[0].serial_number):
print(atom_id(at1), ":", atom_id(at2), at1 - at2)
return res_list, np.concatenate(coo_list).reshape((-1, 3))
# %%
p, c = "4fae", "B"
parser = PDBParser()
if PreProcess.download_pdb(p, f'{p}.pdb'):
structure = parser.get_structure('a', f'{p}.pdb')
chain = structure[0][c]
res_list, coo_list = get_residue_feature(chain)
atom_list = [i for i in chain.get_atoms()]
ns = NeighborSearch(atom_list)
ns_list = ns.search_all(3.3, level='R')
edge, l = [], len(res_list)
for ai, aj in ns_list:
i, j = ai.get_id()[1]-1, aj.get_id()[1]-1
if i < j-1 and j < l:
edge.append([i, j, 1])
#%%
mod = 'train'
out = {'train': {}, 'test':{}, 'valid':{}}
out[mod]['edge'] =
def main():
parser = argparse.ArgumentParser(prog='polarContacts',
description='Polar contacts detector')
parser.add_argument('--backonly',
action='store_true',
dest='backonly',
help='Restrict to backbone')
parser.add_argument('--nowats',
action='store_true',
dest='nowats',
help='Exclude water molecules')
parser.add_argument('--diel',
type=float,
action='store',
dest='diel',
default=1.0,
help='Relative dielectric constant')
parser.add_argument('--vdw',
action='store',
dest='vdwprm',
help='VDW Paramters file')
parser.add_argument('--rlib',
action='store',
dest='reslib',
help='AminoAcid library')
parser.add_argument('pdb_path')
args = parser.parse_args()
print("Settings")
print("--------")
for k, v in vars(args).items():
print('{:10}:'.format(k), v)
backonly = args.backonly
nowats = args.nowats
pdb_path = args.pdb_path
vdwprm = args.vdwprm
reslib = args.reslib
diel = args.diel
# Load VDW parameters
vdwParams = VdwParamset(vdwprm)
print("{} atom types loaded".format(vdwParams.ntypes))
# Load AA Library
aaLib = ResiduesDataLib(reslib)
print("{} amino acid atoms loaded".format(aaLib.nres))
if not pdb_path:
parser.print_help()
sys.exit(2)
parser = PDBParser(PERMISSIVE=1)
try:
st = parser.get_structure('st', pdb_path)
except OSError:
print("#ERROR: loading PDB")
sys.exit(2)
# Checking for models
if len(st) > 1:
print("#WARNING: Several Models found, using only first")
# Using Model 0 any way
st = st[0]
# Making a list of polar atoms
polats = []
if backonly:
selected_atoms = backbone_polars
else:
selected_atoms = all_polars
for at in st.get_atoms():
if at.id in selected_atoms:
polats.append(at)
#Searching for contacts under HNLNK on diferent residues
nbsearch = NeighborSearch(polats)
hblist = []
for at1, at2 in nbsearch.search_all(HBLNK):
if at1.get_parent() == at2.get_parent():
continue
#Discard covalents and neighbours
if (at1 - at2) < COVLNK:
continue
if abs(at2.get_parent().id[1] - at1.get_parent().id[1]) == 1:
continue
# remove waters
if nowats:
if at1.get_parent().get_resname() in waternames \
or at2.get_parent().get_resname() in waternames:
continue
# atom1 = Atom(at1,1,aaLib,vdwParams)
# atom2 = Atom(at2,1,aaLib,vdwParams)
if at1.get_serial_number() < at2.get_serial_number():
hblist.append([at1, at2])
else:
hblist.append([at2, at1])
print()
print()
print("Polar contacts")
print('{:13} {:13} {:6} '.format('Atom1', 'Atom2', 'Dist (A)'))
for hb in sorted(hblist, key=lambda i: i[0].get_serial_number()):
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
print('{:14} {:14} {:6.3f} '.format(
r1.get_resname() + ' ' + str(r1.id[1]) + hb[0].id,
r2.get_resname() + ' ' + str(r2.id[1]) + hb[1].id, hb[0] - hb[1]))
print()
print("Residue interactions")
# Making list or residue pairs to avoid repeated pairs
respairs = []
for hb in hblist:
r1 = hb[0].get_parent()
r2 = hb[1].get_parent()
if [r1, r2] not in respairs:
respairs.append([r1, r2])
print('Exercise A')
l = []
for rpair in sorted(respairs, key=lambda i: i[0].id[1]):
eint = 0.
evdw = 0.
for at1 in rpair[0].get_atoms():
resid1 = rpair[0].get_resname()
atid1 = at1.id
atparam1 = aaLib.getParams(resid1, atid1)
vdwprm1 = vdwParams.atTypes[atparam1.atType]
for at2 in rpair[1].get_atoms():
resid2 = rpair[1].get_resname()
atid2 = at2.id
atparam2 = aaLib.getParams(resid2, atid2)
vdwprm2 = vdwParams.atTypes[atparam2.atType]
eint = eint + 332.16 * atparam1.charg * atparam2.charg / diel / (
at1 - at2)
eps = math.sqrt(vdwprm1.eps * vdwprm2.eps)
sig = math.sqrt(vdwprm1.sig * vdwprm2.sig)
evdw = evdw + 4 * eps * ((sig / (at1 - at2))**12 -
(sig / (at1 - at2))**6)
#print (resid1,rpair[0].id[1],resid2,rpair[1].id[1],eint,evdw, eint+evdw)
l.append([
resid1, rpair[0].id[1], resid2, rpair[1].id[1], eint, evdw,
eint + evdw
])
for index, element in enumerate(sorted(l, key=lambda i: i[6])):
if index < 5:
print(element)
#Exercise B 1
print('Exercise B.1')
mainmain = []
mainside = []
sidemain = []
sideside = []
to_main = []
to_side = []
for hb in sorted(hblist, key=lambda i: i[0].get_serial_number()):
resid1 = hb[0].get_parent()
resid2 = hb[1].get_parent()
if hb[0].id in backbone_polars:
a = 'main'
else:
a = 'side'
if hb[1].id in backbone_polars:
b = 'main'
else:
b = 'side'
label = a + '-' + b
if label == 'main-main':
mainmain.append([
resid1.get_resname(), resid1.id[1],
resid2.get_resname(), resid2.id[1], label, hb[0].id, hb[1].id,
hb[0] - hb[1]
])
if (str(resid1.get_resname()) + ' ' +
str(resid1.id[1])) not in to_main:
to_main.append(
str(resid1.get_resname()) + ' ' + str(resid1.id[1]))
if (str(resid2.get_resname()) + ' ' +
str(resid2.id[1])) not in to_main:
to_main.append(
str(resid2.get_resname()) + ' ' + str(resid2.id[1]))
elif label == 'main-side':
mainside.append([
resid1.get_resname(), resid1.id[1],
resid2.get_resname(), resid2.id[1], label, hb[0].id, hb[1].id,
hb[0] - hb[1]
])
if (str(resid2.get_resname()) + ' ' +
str(resid2.id[1])) not in to_main:
to_main.append(
str(resid2.get_resname()) + ' ' + str(resid2.id[1]))
if (str(resid1.get_resname()) + ' ' +
str(resid1.id[1])) not in to_side:
to_side.append(
str(resid1.get_resname()) + ' ' + str(resid1.id[1]))
elif label == 'side-main':
sidemain.append([
resid1.get_resname(), resid1.id[1],
resid2.get_resname(), resid2.id[1], label, hb[0].id, hb[1].id,
hb[0] - hb[1]
])
if (str(resid2.get_resname()) + ' ' +
str(resid2.id[1])) not in to_side:
to_side.append(
str(resid2.get_resname()) + ' ' + str(resid2.id[1]))
if (str(resid1.get_resname()) + ' ' +
str(resid1.id[1])) not in to_main:
to_main.append(
str(resid1.get_resname()) + ' ' + str(resid1.id[1]))
else:
sideside.append([
resid1.get_resname(), resid1.id[1],
resid2.get_resname(), resid2.id[1], label, hb[0].id, hb[1].id,
hb[0] - hb[1]
])
if (str(resid1.get_resname()) + ' ' +
str(resid1.id[1])) not in to_side:
to_side.append(
str(resid1.get_resname()) + ' ' + str(resid1.id[1]))
if (str(resid2.get_resname()) + ' ' +
str(resid2.id[1])) not in to_side:
to_side.append(
str(resid2.get_resname()) + ' ' + str(resid2.id[1]))
for i in mainmain:
print(i)
for i in mainside:
print(i)
for i in sidemain:
print(i)
for i in sideside:
print(i)
nmain = []
nummain = []
nside = []
numside = []
for i in range(len(to_main)):
nmain.append('to_main')
nummain.append(i)
for i in range(len(to_side)):
nside.append('to_side')
numside.append(i + len(to_main))
x = np.array(nummain + numside)
y = np.array(nmain + nside)
res = to_main + to_side
plt.xticks(x, res)
plt.plot(x, y, 'ro')
plt.show()
#It is generated a plot indicating if each residue is interacting with one or more elements either in main chain or in side chain
#End of exercise B 1
print()
print('Exercise B', 2)
## From http://cib.cf.ocha.ac.jp/bitool/ASA/ I have obtained that the residues in the surface are:
surface = [['ILE', 3], ['VAL', 5], ['ILE', 23], ['VAL', 26], ['ILE', 30],
['GLN', 41], ['LEU', 43], ['LEU', 56], ['ILE', 61], ['LEU', 67],
['LEU', 69]]
l = []
for rpair in sorted(respairs, key=lambda i: i[0].id[1]):
eint = 0.
for at1 in rpair[0].get_atoms():
resid1 = rpair[0].get_resname()
resid1id = rpair[0].id[1]
atid1 = at1.id
atparam1 = aaLib.getParams(resid1, atid1)
for at2 in rpair[1].get_atoms():
resid2 = rpair[1].get_resname()
resid2id = rpair[1].id[1]
atid2 = at2.id
atparam2 = aaLib.getParams(resid2, atid2)
for i in surface:
for j in surface:
if resid1 == i[0] and resid1id == i[1] and resid2 == j[
0] and resid2id == j[1]:
eint = eint + 80 * atparam1.charg * atparam2.charg / diel / (
at1 - at2)
if eint != 0:
l.append([resid1, resid1id, resid2, resid2id, eint])
for i in l:
print(i)
