def ss_motif(args):
# sanity checks
files = args.files
print "# Finding 3D Single Strand Motifs..."
# assert args.bulges < 3, "# FATAL: cannot do bulges > 2"
ref_pdb = reader.Pdb(args.reference, res_mode=args.res_mode)
ref_len = len(ref_pdb.model.sequence)
ref_mat = (ref_pdb.model.get_gmat(args.cutoff)).reshape(-1)
if args.seq == None:
query = "N" * ref_len
else:
assert len(args.seq) == ref_len, "# FATAL: query structure and sequence length mismatch!"
query = args.seq
pattern = tools.get_pattern(query)
# OK...
fh = open(args.name, "w")
fh.write("# This is a baRNAba run.\n")
for k in sorted(args.__dict__):
s = "# " + str(k) + " " + str(args.__dict__[k]) + "\n"
fh.write(s)
for i in xrange(0, len(files)):
cur_pdb = reader.Pdb(files[i], res_mode=args.res_mode)
cur_pdb.set_xtc(args.xtc)
cur_len = len(cur_pdb.model.sequence)
if cur_len < ref_len:
continue
# return indeces matching query
indeces = tools.get_idx(cur_pdb.model.sequence, query, args.bulges)
idx = 0
while idx >= 0:
gmats = [(cur_pdb.model.get_gmat(args.cutoff, index)).reshape(-1) for index in indeces]
dists = distance.cdist([ref_mat], gmats) / np.sqrt(ref_len)
below_t = (dists < args.treshold).nonzero()[1]
for ss in below_t:
seq = "_".join([cur_pdb.model.sequence_id[p] for p in indeces[ss]])
string = "%8.5f %s %i - %s \n" % (dists[0, ss], files[i], idx, seq)
fh.write(string)
idx = cur_pdb.read()
fh.close()
def ds_motif(args):
# sanity checks
files = args.files
print "# Finding 3D Double Stranded Motifs..."
ref_pdb = reader.Pdb(args.reference,res_mode=args.res_mode)
ref_len1 = args.l1
ref_len2 = args.l2
ref_len = ref_len1+ref_len2
if(args.seq==None):
query1="N"*ref_len1
query2="N"*ref_len2
else:
query1=(args.seq).split("%")[0]
query2=(args.seq).split("%")[1]
assert len(query1)==ref_len1, "# FATAL: query structure and sequence length mismatch!"
assert len(query2)==ref_len2, "# FATAL: query structure and sequence length mismatch!"
# OK...
fh = open(args.name,'w')
fh.write("# This is a baRNAba run.\n")
for k in sorted(args.__dict__):
s = "# " + str(k) + " " + str(args.__dict__[k]) + "\n"
fh.write(s)
ref_mat_tot = ref_pdb.model.get_gmat(args.cutoff).reshape(-1)
indeces1=np.arange(0,ref_len1)
ref_mat1 = ref_pdb.model.get_gmat(args.cutoff,indeces1).reshape(-1)
indeces2=np.arange(ref_len1,ref_len)
ref_mat2 = ref_pdb.model.get_gmat(args.cutoff,indeces2).reshape(-1)
# calculate center of mass distances
# this will be used to prune the search!
ref_com1 = ref_pdb.model.get_lcs_com(indeces1)
ref_com2 = ref_pdb.model.get_lcs_com(indeces2)
diff_com = (ref_com2-ref_com1)**2
dd= np.sqrt(np.sum(diff_com))
for i in xrange(0,len(files)):
cur_pdb = reader.Pdb(files[i],res_mode=args.res_mode)
cur_pdb.set_xtc(args.xtc)
cur_len = len(cur_pdb.model.sequence)
if(cur_len<ref_len): continue
all_idx1 = tools.get_idx(cur_pdb.model.sequence,query1,args.bulges)
all_idx2 = tools.get_idx(cur_pdb.model.sequence,query2,args.bulges)
idx = 0
while(idx>=0):
# get indeces and coms of first half
gmats1 = [(cur_pdb.model.get_gmat(args.cutoff,index)).reshape(-1) for index in all_idx1]
dists1 = distance.cdist([ref_mat1],gmats1)/np.sqrt(ref_len1)
below_t1 = (dists1<args.treshold).nonzero()[1]
idx1 = [all_idx1[j] for j in below_t1]
com1 = [cur_pdb.model.get_lcs_com(all_idx1[j]) for j in below_t1]
# get indeces of second half
gmats2 = [(cur_pdb.model.get_gmat(args.cutoff,index)).reshape(-1) for index in all_idx2]
dists2 = distance.cdist([ref_mat2],gmats2)/np.sqrt(ref_len2)
below_t2 = (dists2<args.treshold).nonzero()[1]
idx2 = [all_idx2[j] for j in below_t2]
com2 = [cur_pdb.model.get_lcs_com(all_idx2[j]) for j in below_t2]
# calculate all distances between center of mass
dmat = distance.cdist(com1,com2)
c_idx = (dmat<1.5*dd).nonzero()
# get combo indeces
dmine = [dmat[c_idx[0][ii],c_idx[1][ii]] for ii in range(len(c_idx[0]))]
idx_combo = [idx1[c_idx[0][ii]] + idx2[c_idx[1][ii]] for ii in range(len(c_idx[0]))]
gmatsf = [(cur_pdb.model.get_gmat(args.cutoff,index)).reshape(-1) for index in idx_combo]
distsf = distance.cdist([ref_mat_tot],gmatsf)/np.sqrt(ref_len)
below_tf = (distsf<args.treshold).nonzero()[1]
for ss in below_tf:
seq = "_".join([cur_pdb.model.sequence_id[p] for p in idx_combo[ss] ])
string = '%8.5f %s %i - %s \n' % (distsf[0,ss],files[i],idx,seq)
fh.write(string)
idx = cur_pdb.read()
fh.close()
def snippet(args):
files = args.files
# check query sequence
for item in args.seq:
if(item not in reader.Names.known_abbrev):
print "# FATAL Error. Symbol ", item, " not known. Use ACGU NYR"
return 1
query = args.seq.split("%")
assert len(args.seq.split("%")) < 2 , "# Fatal error: max 1 strand"
fh = open(args.name,'w')
print "# SPLIT..."
fh.write("# This is a baRNAba run.\n")
for k in sorted(args.__dict__):
s = "# " + str(k) + " " + str(args.__dict__[k]) + "\n"
fh.write(s)
ll = [len(el) for el in query]
for i in xrange(0,len(files)):
try:
cur_pdb = reader.Pdb(files[i],res_mode=args.res_mode,permissive=True)
except:
print "# SKIPPING", files[i]
continue
cur_len = len(cur_pdb.model.sequence)
if(cur_len<sum(ll)): continue
# single strand
indeces = tools.get_idx(cur_pdb.model.sequence,query[0],bulges=0)
# check chain consistency - remove
tools.chain_consistency(indeces,cur_pdb.model.sequence_id)
if(len(query)==2):
indeces2 = tools.get_idx(cur_pdb.model.sequence,query[1],bulges=0)
tools.chain_consistency(indeces2,cur_pdb.model.sequence_id)
# to be done....
idx = 0
new_pdb_r = files[i].split("/")[-1].split(".pdb")[0] + "_"
while(idx>=0):
for index in indeces:
seq_out = "".join([cur_pdb.model.sequence[res] for res in index])
f_res = cur_pdb.model.sequence_id[index[0]]
l_res = cur_pdb.model.sequence_id[index[-1]]
new_pdb = new_pdb_r + seq_out + "_" + f_res + "_" + l_res + ".pdb"
fh_pdb = open(new_pdb,'w')
fh_pdb.write(cur_pdb.model.string_pdb(index,noP=True))
fh_pdb.close()
idx = cur_pdb.read()
fh.close()
return 0
