def dopotential(kbp_atomlist, residues_list, potential_file, seq_dist_co = 0, grof = None, xtcf = None, pdbf = None, uni = None, pdb = None, dofullmatrix = True, kbT=1.0):
residues_list = ["ALA","ARG","ASN","ASP","CYS","GLN","GLU","HIS", "ILE","LEU","LYS","MET","PHE","PRO","SER","THR","TRP","TYR","VAL"] # Residues for which the potential is defined: all except G
log.info("Loading potential definition . . .")
sparses = parse_sparse(potential_file)
log.info("Loading input files...")
if not pdb or not uni:
if not pdbf or not grof or not xtcf:
raise ValueError
pdb,uni = loadsys(pdbf,grof,xtcf)
ok_residues = []
discarded_residues = set()
residue_pairs = []
atom_selections = []
ordered_sparses = []
numframes = len(uni.trajectory)
for i in range(len(uni.residues)):
if uni.residues[i].name in residues_list:
ok_residues.append(i)
else:
discarded_residues.add(uni.residues[i])
continue
for j in ok_residues[:-1]:
ii = i
if not (abs(i-j) < seq_dist_co or uni.residues[ii].segment.name != uni.residues[j].segment.name):
if uni.residues[j].name < uni.residues[ii].name:
ii,j = j,ii
this_sparse = sparses[uni.residues[ii].name][uni.residues[j].name]
this_atoms = (kbp_atomlist[uni.residues[ii].name][this_sparse.p1_1],
kbp_atomlist[uni.residues[ii].name][this_sparse.p1_2],
kbp_atomlist[uni.residues[j].name][this_sparse.p2_1],
kbp_atomlist[uni.residues[j].name][this_sparse.p2_2])
try:
selected_atoms = mda.core.AtomGroup.AtomGroup((uni.residues[ii].atoms[uni.residues[ii].atoms.names().index(this_atoms[0])],
uni.residues[ii].atoms[uni.residues[ii].atoms.names().index(this_atoms[1])],
uni.residues[j].atoms[uni.residues[j].atoms.names().index(this_atoms[2])],
uni.residues[j].atoms[uni.residues[j].atoms.names().index(this_atoms[3])]))
except:
log.warning("could not identify essential atoms for the analysis (%s%s, %s%s)" % ( uni.residues[ii].name, uni.residues[ii].id, uni.residues[j].name, uni.residues[j].id ))
continue
residue_pairs.append((ii,j))
atom_selections.append(selected_atoms)
ordered_sparses.append(this_sparse)
scores = np.zeros((len(residue_pairs)), dtype=float)
a=0
#log.info("the following residues will not be considered: %s" % (", ".join(["%s %s"%(res.name,res.id) for res in discarded_residues] )))
coords = None
#coords = atom_selections[0].coordinates()
#for sel in atom_selections[1:]:
#coords = np.concatenate((coords, sel.coordinates()))
for ts in uni.trajectory:
tmp_coords = []
sys.stdout.write( "now analyzing: frame %d / %d (%3.1f%%)\r" % (a,numframes,float(a)/float(numframes)*100.0) )
sys.stdout.flush()
a+=1
for sel in atom_selections:
#coords = np.concatenate((coords, sel.coordinates()))
tmp_coords.append(sel.coordinates())
coords = np.array(np.concatenate(tmp_coords),dtype=np.float64)
#if coords == None:
#coords = tmp_coords
#else:
#coords = np.concatenate([coords] + [tmp_coords])
#coords = np.array(coords, dtype=np.float64)
#coords = np.array(np.concatenate(coords),dtype=np.float64)
inner_loop = LoopDistances(coords, coords, None)
distances = inner_loop.run_potential_distances(len(atom_selections), 4, 1)
scores += calc_potential(distances, ordered_sparses, pdb, uni, seq_dist_co, kbT=kbT)
scores /= float(len(uni.trajectory))
outstr = ""
for i,s in enumerate(scores):
if abs(s) > 0.000001:
outstr += "%s-%s%s:%s-%s%s\t%.3f\n" % (pdb.residues[residue_pairs[i][0]].segment.name, pdb.residues[residue_pairs[i][0]].name, pdb.residues[residue_pairs[i][0]].id, pdb.residues[residue_pairs[i][1]].segment.name, pdb.residues[residue_pairs[i][1]].name, pdb.residues[residue_pairs[i][1]].id, s)
dm = None
if dofullmatrix:
dm = np.zeros((len(pdb.residues), len(pdb.residues)))
for i,k in enumerate(residue_pairs):
dm[k[0],k[1]] = scores[i]
dm[k[1],k[0]] = scores[i]
return (outstr, dm)
