def main(modeldir, genfile, type, write=False):
data=dict()
pops=numpy.loadtxt('%s/Populations.dat' % modeldir)
map=numpy.loadtxt('%s/Mapping.dat' % modeldir)
frames=numpy.where(map!=-1)[0]
unbound=numpy.loadtxt('%s/tpt-rmsd-%s/unbound_%s_states.txt' % (modeldir, type, type), dtype=int)
bound=numpy.loadtxt('%s/tpt-rmsd-%s/bound_%s_states.txt' % (modeldir, type, type), dtype=int)
dir=modeldir.split('Data')[0]
name=glob.glob('%s/fkbp*xtal*pdb' % dir)
pdb=Trajectory.load_from_pdb(name[0])
paths=io.loadh('%s/tpt-rmsd-%s/Paths.h5' % (modeldir, type))
committors=numpy.loadtxt('%s/commitor_states.txt' % modeldir, dtype=int)
colors=['red', 'orange', 'green', 'cyan', 'blue', 'purple']
colors=colors*40
if type=='strict':
ref=5
elif type=='super-strict':
ref=3
elif type=='medium':
ref=10
elif type=='loose':
ref=15
#for p in range(0, 3):
for p in range(0, 1):
path=paths['Paths'][p]
print "Bottleneck", paths['Bottlenecks'][p]
flux=paths['fluxes'][p]/paths['fluxes'][0]
if flux < 0.2:
break
print "flux %s" % flux
frames=numpy.where(path!=-1)[0]
path=numpy.array(path[frames], dtype=int)
print path
if write==True:
size=(paths['fluxes'][p]/paths['fluxes'][0])*1000
traj=Trajectory.load_from_xtc('%s/tpt-rmsd-%s/path%s_sample20.xtc' % (modeldir, type, p), Conf=pdb)
data=build_metric(dir, pdb, traj)
dir=modeldir.split('Data')[0]
for op in sorted(data.keys()):
#for op in residues:
pylab.figure()
pylab.scatter(data['rmsd'], data[op], c=colors[p], alpha=0.7) #, s=size)
for j in paths['Bottlenecks'][p]:
frame=numpy.where(paths['Paths'][p]==j)[0]
pylab.scatter(data['rmsd'][frame*20], data[op][frame*20], marker='x', c='k', alpha=0.7, s=50)
location=numpy.where(committors==paths['Paths'][p][frame])[0]
if location.size:
print "path %s state %s bottleneck in committors" % (p, j)
print data['rmsd'][frame*20], data[op][frame*20]
pylab.title('path %s' % p)
pylab.xlabel('P-L RMSD')
#pylab.xlabel('P-L COM')
pylab.ylabel(op)
pylab.xlim(0,max(data['rmsd'])+5)
#pylab.ylim(0,max(data[op])+5)
pylab.show()
def test_asa_3():
traj_ref = np.loadtxt( os.path.join(reference_dir(),'g_sas_ref.dat'))
Conf = Trajectory.load_from_pdb(os.path.join( fixtures_dir(), 'native.pdb'))
traj = Trajectory.load_trajectory_file( os.path.join(fixtures_dir(), 'trj0.xtc') , Conf=Conf)
traj_asa = calculate_asa(traj, probe_radius=0.14, n_sphere_points = 960)
# the algorithm used by gromacs' g_sas is slightly different than the one
# used here, so the results are not exactly the same -- see the comments
# in src/python/geomtry/asa.py or the readme file src/ext/asa/README.txt
# for details
npt.assert_array_almost_equal(traj_asa, traj_ref, decimal=2)
logger.setLevel(logging.INFO)
sh = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(fmt="%(asctime)s - %(message)s", datefmt="%H:%M:%S")
sh.setFormatter(formatter)
logger.addHandler(sh)
logger.propagate = False
logger.info("start")
Proj = Project.load_from(args.proj_FN)
logger.info("loaded project info")
try:
Ass = io.loadh(args.ass_FN)["arr_0"]
except:
Ass = io.loadh(args.ass_FN)["Data"]
pdb = Trajectory.load_from_pdb(Proj.conf_filename)
which = np.loadtxt(args.which).astype(int)
distance_cutoff = 0.32
angle_cutoff = 120
def get_hb(traj):
# get accH - donor distance:
dists = contact.atom_distances(traj["XYZList"], atom_contacts=which[:, 1:])
# get angles
angles = angle.compute_angles(traj, which)
residue_pairs=numpy.loadtxt('./residuepairs_0based_select_structure1.dat', dtype=int)
pairmetric2=metrics.ContinuousContact(contacts=residue_pairs, scheme='closest-heavy')
#pairmetric min 0.00255089723477 max 10.8130541983
residue_pairs=numpy.loadtxt('./residuepairs_select_0based.dat', dtype=int)
pairmetric1=metrics.ContinuousContact(contacts=residue_pairs, scheme='closest-heavy')
#pairmetric1 min 0.00447584821743 max 22.0643260692
#pairmetric=metrics.BooleanContact(metric='matching', contacts=residue_pairs, cutoff=0.5, scheme='closest-heavy')
ligand_inds = numpy.loadtxt('./p53_Calpha_indices.dat', dtype='int')
prot_inds = numpy.loadtxt('./s100b_Calpha_indices.dat', dtype='int')
rmsdmetric2=metrics.RMSD(ligand_inds)
rmsdmetric1 = lprmsd.LPRMSD(atomindices=prot_inds, permuteindices=None, altindices=ligand_inds)
pdb=Trajectory.load_from_pdb('structure_1.pdb')
#project = Project.load_from('/home/kkappel/p53/s100b_md/msm_80ns_p53CA/ProjectInfo.yaml')
project = Project.load_from('ProjectInfo.yaml')
hybrid1 = metrics.Hybrid([rmsdmetric2, pairmetric1], weights=[1/1.16, 1/22.1])
hybrid2 = metrics.Hybrid([rmsdmetric2, pairmetric2], weights=[1/1.16, 1/10.8])
data=dict()
# check pair metric for traj0
metrics=[pairmetric1, pairmetric2, hybrid1, hybrid2 ]
names=['pair-selectall', 'pair-selectstructure1', 'hybrid-selectall', 'hybrid-selectstructure1']
for (metric, name) in zip(metrics, names):
data[name]=dict()
data[name]['min']=1000000
data[name]['max']=0
def main(system, genfile, lag, volume, nproc):
lag=int(lag)
dir=os.path.dirname(genfile)
if not os.path.exists('%s/target.txt' % dir.split('Data')[0]):
print "need target.txt with experimental value"
sys.exit()
ref=loadtxt('%s/target.txt' % dir.split('Data')[0])
filename=genfile.split(dir)[1].split('.lh5')[0]
if "Coarse" in filename:
coarse=filename.split('Coarsed')[1].split('_')[0]
rcut=filename.split('_r')[1].split('_')[0]
modeldir='%s/msml%s_coarse_r%s_d%s/' % ( dir.split('/')[0], lag, rcut, coarse)
else:
modeldir='%s/msml%s' % (dir, lag)
print "computing PMF from %s with lag %s frames" % (filename, lag)
conf=Trajectory.load_from_pdb('%s/%s_noh.pdb' % (dir.split('Data')[0], system))
lig_atoms=conf['XYZList'].shape[1]
map=loadtxt('%s/Mapping.dat' % modeldir)
pops=loadtxt('%s/Populations.dat' % modeldir)
ligfile='%s.vmd_ligcoords.pickle' % genfile.split('.lh5')[0]
if os.path.exists(ligfile):
lighandle=open(ligfile, 'rb')
else:
print "need to run tcl scripts and pickler for coordinates"
sys.exit()
ligcoors=pickle.load(lighandle)
lighandle.close()
mapped_ligcoors, x_range, y_range, z_range, box_volume=get_minmax(ligcoors, map)
correction=-0.6*log(box_volume/1600.0)
# get prot-lig distances
com_distances=loadtxt('%s.vmd_com.dat' % genfile.split('.lh5')[0], usecols=(1,))
ref_com=com_distances[0]
com_distances=com_distances[1:]
if len(com_distances)==len(map):
print "protein-ligand COM distances per state exist"
# PMF
frames=where(map!=-1)[0]
mapped_states=map[frames]
mapped_com=range(0, len(frames))
mapped_com_distances=com_distances[frames]
space=PMF3D.PMF3D(x_range, y_range, z_range)
spacetrack=space.microstate_count_grid(mapped_ligcoors)
new_pops={ key: pops[n] for (n, key) in enumerate(mapped_ligcoors.keys())}
GD=space.new_manual_allcoor_grid(spacetrack, mapped_ligcoors, new_pops, type='pops')
free=array([-0.6*log(i) for i in pops])
subtract=min(free)
free=array([k-subtract for k in free])
GD=space.new_manual_allcoor_grid(spacetrack, mapped_ligcoors, new_pops, type='pops')
GDfree=-0.6*log(GD)
GDfree=PMF3D.convert(GDfree, max(free))
GDfree=GDfree-min(GDfree.flatten())
space.write_dx(GDfree, modeldir)
frees=[]
corrs=[]
axis=[]
volumes=[]
cutoffs=arange(0, 40, 1)
if volume=='all':
for cutoff in cutoffs:
bound_frames=where(mapped_com_distances < cutoff)[0]
if len(bound_frames)==0:
print "no bound states less than reference com distance %s" % cutoff
continue
gen_bound_frames=[]
for i in bound_frames:
location=where(map==i)[0]
gen_bound_frames.append(location)
new_points={ key: mapped_ligcoors[key] for key in bound_frames}
new_pops={ key: pops[key] for key in bound_frames}
GD=space.new_manual_allcoor_grid(spacetrack, new_points, new_pops, type='pops')
boundspace=space.pmfvolume(GD)
new_pops={ key: 1.0 for key in bound_frames}
GD=space.new_manual_allcoor_grid(spacetrack, new_points, new_pops, type='pops')
boundvolume=space.pmfvolume(GD)
volumes.append(boundvolume)
print "count bound volume ", boundvolume
# unbound frames are above COM cutoff
unbound_frames=array([int(x) for x in mapped_states if x not in bound_frames])
new_points={ key: mapped_ligcoors[key] for key in unbound_frames}
new_pops={ key: pops[key] for key in unbound_frames}
GD=space.new_manual_allcoor_grid(spacetrack, new_points, new_pops, type='pops')
unboundspace=space.pmfvolume(GD)
# for counting states
new_pops={ key: 1.0 for key in unbound_frames}
GD=space.new_manual_allcoor_grid(spacetrack, new_points, new_pops, type='pops')
unboundvolume=space.pmfvolume(GD)
#print "unbound volume ", unboundvolume
# free energy from ratio
depth=-0.6*log(boundspace/unboundspace)
frees.append(depth)
axis.append(cutoff)
print "corrected integrated dG ratio at cutoff %s is %s" % (cutoff, depth+correction)
k=len(pops)
savetxt('%s/%s_k%s_l%s_pocketdistance_free.dat' % (modeldir, filename, k, lag), [x+correction for x in frees])
savetxt('%s/%s_k%s_l%s_pocketdistance_volume.dat' % (modeldir, filename, k, lag), volumes)
savetxt('%s/%s_k%s_l%s_pocketdistance_axis.dat' % (modeldir, filename, k, lag), axis)
pylab.figure()
#pylab.plot(axis, corrs, color='green', label='correction')
pylab.plot(axis, [ref]*len(frees), color='black', label='exp')
pylab.plot(axis, [x+correction for x in frees], color='red', label='standard free')
pylab.legend()
pylab.savefig('%s/%s_k%s_l%s_pocketdistance_free.png' % (modeldir, filename, k, lag))
#pylab.show()
else:
k=len(pops)
standard_frees=loadtxt('%s/%s_k%s_l%s_pocketdistance_free.dat' % (modeldir, filename, k, lag))
bound_volumes=loadtxt('%s/%s_k%s_l%s_pocketdistance_volume.dat' % (modeldir, filename, k, lag))
axis=loadtxt('%s/%s_k%s_l%s_pocketdistance_axis.dat' % (modeldir, filename, k, lag))
cutoff=float(volume)
index=where(axis==cutoff)[0]
print "standard free energy is %s" % (standard_frees[index])
print "(population-weighted) bound volume is %s A^3" % bound_volumes[index]
from pyschwancr import dataIO
import os, sys, re
matplotlib.rcParams['font.size']=22
pp = PdfPages( args.out_plot )
HB = metric_HB.HydrogenBond()
input_dict = io.loadh( args.in_cm )
triples = input_dict['donor_h_acceptor_ainds']
CMs = input_dict['HB_maps']
num_acceptors = len( np.unique( triples[:,2] ) )
num_donors = len( np.unique( triples[:,0] ) )
nat_pdb = Trajectory.load_from_pdb( args.nat_FN )
T = mmread( args.tProb )
n_res = np.unique( nat_pdb['ResidueID'] ).shape[0]
CM_pdb = HB.prepare_trajectory( nat_pdb )
CM_pdb = CM_pdb.reshape( (1,num_donors, num_acceptors), order='C')[0]
native_locs = np.array(np.where( CM_pdb )).T
vals,vecs = MSMLib.GetEigenvectors( T, args.num_vecs+1 )
vecs = vecs.real
if args.lagtime != None:
timescales = - args.lagtime / np.log( vals[1:].real ) # NOTE: This means the 1st dyn e-vec is indexed at 0
for i in xrange(1,args.num_vecs+1):
formatter = logging.Formatter(fmt="%(asctime)s - %(message)s", datefmt="%H:%M:%S")
sh.setFormatter(formatter)
logger.addHandler(sh)
logger.propagate = False
logger.info("start")
Proj = Project.load_from(args.proj_FN)
logger.info("loaded project info")
try:
Ass = io.loadh(args.ass_FN)["arr_0"]
except:
Ass = io.loadh(args.ass_FN)["Data"]
HB = metric_HB.HydrogenBond()
pdb = Trajectory.load_from_pdb(Proj.conf_filename)
donorH_ainds = np.where(((pdb["AtomNames"] == "H") | (pdb["AtomNames"] == "HN")) & (pdb["ResidueNames"] != "PRO"))[
0
] # _ainds correspond to atom indices in the trajectory
donor_ainds = np.where((pdb["AtomNames"] == "N") & (pdb["ResidueNames"] != "PRO"))[0]
acceptor_ainds = np.where(pdb["AtomNames"] == "O")[0] # This excludes the C-Terminus which has naming issues...
n_res = np.unique(pdb["ResidueID"]).shape[0]
ppdb = HB.prepare_trajectory(pdb) # Do this to set the donor indices and stuff
atom_indices = np.sort(np.concatenate((donor_ainds, donorH_ainds, acceptor_ainds)))
CMs_1d = np.zeros((Ass.max() + 1, ppdb.shape[1]))
def construct_basic_metric(metric_name, args):
if metric_name == 'rmsd':
if args.rmsd_atom_indices != 'all':
atom_indices = np.loadtxt(args.rmsd_atom_indices, np.int)
else:
atom_indices = None
metric = RMSD(atom_indices)#, omp_parallel=args.rmsd_omp_parallel)
elif metric_name == 'dihedral':
metric = Dihedral(metric=args.dihedral_metric,
p=args.dihedral_p, angles=args.dihedral_angles,
userfilename=args.dihedral_userfilename)
elif metric_name == 'contact':
if args.contact_which != 'all':
contact_which = np.loadtxt(args.contact_which, np.int)
else:
contact_which = 'all'
if args.contact_cutoff_file != None:
contact_cutoff = np.loadtxt(args.contact_cutoff_file, np.float)
elif args.contact_cutoff != None:
contact_cutoff = float( args.contact_cutoff )
else:
contact_cutoff = None
if contact_cutoff != None and contact_cutoff < 0:
metric = ContinuousContact(contacts=contact_which,
scheme=args.contact_scheme)
else:
metric = BooleanContact(contacts=contact_which,
cutoff=contact_cutoff, scheme=args.contact_scheme)
elif metric_name == 'atompairs':
if args.atompairs_which != None:
pairs = np.loadtxt(args.atompairs_which, np.int)
else:
pairs = None
metric = AtomPairs(metric=args.atompairs_metric, p=args.atompairs_p,
atom_pairs=pairs)
elif metric_name == 'positions':
target = Trajectory.load_from_pdb(args.target)
if args.pos_atom_indices != None:
atom_indices = np.loadtxt(args.pos_atom_indices, np.int)
else:
atom_indices = None
if args.align_indices != None:
align_indices = np.loadtxt(args.align_indices, np.int)
else:
align_indices = None
metric = Positions(target, atom_indices=atom_indices, align_indices=align_indices,
metric=args.positions_metric, p=args.positions_p)
elif metric_name == 'custom':
with open(args.picklemetric_input) as f:
metric = pickle.load(f)
print '#'*80
print 'Loaded custom metric:'
print metric
print '#'*80
else:
# apply the constructor on args and take the first non-none element
# note that using these itertools constructs, we'll only actual
# execute the constructor until the match is achieved
metrics = itertools.imap(lambda c: c(args), locate_metric_plugins('construct_metric'))
try:
metric = itertools.dropwhile(lambda c: not c, metrics).next()
except StopIteration:
# This means that none of the plugins acceptedthe metric
raise RuntimeError("Bad metric. Could not be constructed by any built-in or plugin metric. Perhaps you have a poorly written plugin?")
if not isinstance(metric, AbstractDistanceMetric):
return ValueError("%s is not a AbstractDistanceMetric" % metric)
return metric
atomweights['N'] = 14
atomweights['H'] = 1
atomweights['S'] = 32
weights = []
for x in names:
try:
int(x[0])
weights.append(atomweights[x[1]])
except ValueError:
weights.append(atomweights[x[0]])
return numpy.array(weights)
# load in trajectory, here I have just a pdb with one frame
# you can loop over frames in a trajectory
conf = Trajectory.load_from_pdb('new-aln-state847.cent.pdb')
# here i organize the groups in dictionaries
coms = dict()
groups = dict()
group_wts = dict()
residues = dict()
numbers = [1, 2, 3, 4]
start = 1
for num in numbers:
groups[num] = dict() # group ids
group_wts[num] = dict() # group mass weight ids
end = start + 1
residues[num] = [start, end
] # adding consecutive resids to groups in this example
start = end + 1
atomweights['N']=14
atomweights['H']=1
atomweights['S']=32
weights=[]
for x in names:
try:
int(x[0])
weights.append(atomweights[x[1]])
except ValueError:
weights.append(atomweights[x[0]])
return numpy.array(weights)
# load in trajectory, here I have just a pdb with one frame
# you can loop over frames in a trajectory
conf=Trajectory.load_from_pdb('new-aln-state847.cent.pdb')
# here i organize the groups in dictionaries
coms=dict()
groups=dict()
group_wts=dict()
residues=dict()
numbers=[1,2,3,4]
start=1
for num in numbers:
groups[num]=dict() # group ids
group_wts[num]=dict() # group mass weight ids
end=start+1
residues[num]=[start,end] # adding consecutive resids to groups in this example
start=end+1
from fpop import FPOPExchanger
import numpy as np
from scipy.io import mmread
from msmbuilder import Trajectory
import IPython
from matplotlib.pyplot import *
ion()
t = mmread('tProb.mtx')
pdb = Trajectory.load_from_pdb('proteinG_wt.rename.pdb')
avg_sasas = np.loadtxt('avg_sasas.dat')
timepoints = np.arange(0, 50, 2)
print timepoints.shape
exchanger = FPOPExchanger(t, avg_sasas, 100E-6, 1E-3, 20E-3, pdb, timepoints, lagtime=50E-9, init_pops=np.loadtxt('init48.dat'), force_dense=False)
total_products, total_res_pops = exchanger.run(return_res_pops=True, num_labels=10)
for i in range(5):
plot(timepoints * 0.05, total_products[:, i], label='%d labels' % i, lw=3)
legend(prop={'size' : 14})
xlabel(r'time ($\mu s$)')
ylabel('Population')
IPython.embed()
from msmbuilder.geometry import asa
from msmbuilder import Trajectory
from msmbuilder import io
import numpy as np
p=Trajectory.load_from_pdb('barstarH.pdb')
sasa = asa.calculate_asa(p, n_sphere_points=150)
io.saveh('sasa.h5', sasa=sasa)
