示例#1
0
# Comon topology name and address
top = 'pNRTapo-strip.pdb'
# Number of processors
np = 10


# Functions required for parallelization
def multi_run_wrapper(args):
	return f(*args)
def f(msm,clL,frame,count):
	print count
	structure = msm.draw_samples(clL, 1)[frame]
	print structure
	f = md.load(T[structure[0][0]], top=top, frame=structure[0][1])
	f.save_pdb(str(count)+'.pdb')
	print count 
	
cluster = pickle.load(open(cl,'rb'))
clL = cluster.labels_
msm = io.load(msm)

synthTrj = msm.sample_discrete(state = None, n_steps = n_samples)

T = []
for trj in sorted(glob.glob(Trjs)):
	T.append(trj)

arg = [(msm,clL,msm_mapping_[synthTrj[count]],count) for count in range(len(synthTrj))]
p = Pool(np)
S = p.map(multi_run_wrapper, arg)
示例#2
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### Performing kinetic Monte Carlo simulations on Markov state models to estimate the sampling time required to reach the
### target state from an arbitrary inital state using SAXS guided adaptive sampling strategy.
### Required packages: numpy, msmbuilder
### @Chuankai Zhao, [email protected]

from msmbuilder.utils import io
import numpy as np

msm = io.load("MSM25.pkl")

Steps = [
    1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
    22, 23, 24, 25, 26, 27, 28, 29, 30
]
Num = 10
Maxrun = 100
Rounds = [round + 1 for round in range(Maxrun)]


def loadSAXSdata():
    file = open("ProteinG_discrepancy.txt")
    SAXS = np.loadtxt(file)
    N_states = np.shape(SAXS)[0]
    SAXS_dict = {}
    for i in range(N_states):
        state = int(SAXS[i][0])
        intensity = SAXS[i][1]
        SAXS_dict[state] = intensity
    return SAXS_dict

示例#3
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from msmbuilder.utils import io
msm = io.load('MSM.pkl')
counts = msm.countsmat_
import numpy as np
from scipy.optimize import minimize
msm.score
msm.score_ll  

sklearn.cross_validation.LeaveOneOut

  for j in range(0,parallel_traj[i]):
    for k in range(0,seq_traj[i]):
      if os.path.isfile(address+'round'+str(rounds[i]+1)+'/traj_files/par'+str(j+1)+'_sim'+str(k+1)+'.mdcrd')== True:
        f.write('trajin '+address+'round'+str(rounds[i]+1)+'/traj_files/par'+str(j+1)+'_sim'+str(k+1)+'.mdcrd 1 last 6'+'\n')
      else:
        pass
f.write('watershell :A8S watershell_pyl10_active.dat W1 lower 3.0 upper 5.0'+ '\n')

f.close()


#-----------------------------------------------------------------------------------
# Calculations of Lys and ser distance from ABA in inactive and active trajectories.
from msmbuilder.utils import io
#in shadowfax
lys=io.load('/home/sshukla4/pyl10/msm/rmsd_dist/dist_data/aba_lys_dist.pkl')
ser=io.load('/home/sshukla4/pyl10/msm/rmsd_dist/dist_data/cl2_ser_aba.pkl')

inactive_lys=lys[0:90]
active_lys=lys[536:576]
inactive_ser=ser[0:90]
active_ser=ser[536:576]

#In lab mac
os.chdir('/Users/Saurabh/Dropbox/My_Papers_and_Reports/Papers/ABA_binding/figures/active_inactive/pyl10')

inactive_lys=io.load('inactive_lys_aba_pyl10.pkl')
active_lys=io.load('active_lys_aba_pyl10.pkl')
inactive_ser=io.load('inactive_ser_aba_pyl10.pkl')
active_ser=io.load('active_ser_aba_pyl10.pkl')
        '--stride',
        dest='stride',
        help='Stride of the raw trajectories being subsampled.',
        default=None,
        type=int,
        required=True)
    args = parser.parse_args()
    return args


### Main program
if __name__ == '__main__':
    options = parse_cmdln()
    msm_name = options.msm
    cl_name = options.cl
    cluster = pickle.load(open(cl_name, 'rb'), encoding='latin1')
    msm = io.load(msm_name)
    cl_label = cluster.labels_
    n_samples = options.NS
    stride = options.stride

    # Read the topology file and the list of MD raw trajectories
    top = "/home/amoffet2/msm_network_project/folding/lindorff-larsen_2011_trajs/protein_g-350K/protein_g.pdb"
    trajnames = glob.glob(
        "/home/amoffet2/msm_network_project/folding/lindorff-larsen_2011_trajs/protein_g-350K/DESRES-Trajectory_NuG2-*-protein/NuG2-*-protein/*.dcd"
    )

    # Using msmbuilder.draw_samples function to extract 100 structures from each state
    selects = msm.draw_samples(cl_label, n_samples)
    pickstates(selects, trajnames, top, stride, options.NP)