# function for running CABS with different temperatures
# it will compute in directory name+_+temperature
here = os.getcwd() # since pycabs changing directories...
a = pycabs.CABS(sequence,secstr,template,name+"_"+str(temperature))
a.createLatticeReplicas(replicas=1)
a.modeling(Ltemp=temperature,Htemp=temperature, phot=300,cycles=100,dynamics=True)
#remember to come back to `here` directory
os.chdir(here)
#init these variables _before_ running cabs
name = "fnord"
# we have some template, it has to be as list
template=["/home/user/pycabs/playground/2pcy.pdb"]
# suppose we have porter prediction of sec. str.
sss = pycabs.parsePorterOutput("/home/user/pycabs/proba/playground/porter.ss")
sequence = sss[0]
secstr = sss[1]
# now we have all data required to run CABS
temp_from = 1.5
temp_to = 3.0
temp_interval = 0.1
temperatures=np.arange(temp_from,temp_to,temp_interval) # ranges of temperature
# create thread pool with two parallel threads
pool = mp.Pool(processes=2)
pool.map(runCABS,temperatures) # run cabs threads
# HERE IS THE END OF PART WHERE WE RUN CABS in parallel fashion.
#!/usr/bin/env python
import pycabs
from Pycluster import *
from numpy import array,zeros
data = pycabs.parsePorterOutput("/home/user/pycabs/proba/playground/porter.ss") # read PORTER
working_dir = "de_novo" # name of project
templates = [] # deNOVO
a = pycabs.CABS(data[0],data[1],templates,working_dir) # initialize CABS, create required files
# DENOVO a.generateConstraints()
a.createLatticeReplicas() # create start models from templates
a.modeling(Htemp=3.0,cycles=20,phot=100) # start modeling with default INP values and create TRAF.pdb when done
tr = a.getTraCoordinates() # load TRAF into memory and calculate RMSD all-vs-all :
#calculating RMSD 2D array for clustering
distances = zeros((len(tr),len(tr)))
for i in range(len(tr)):
for j in range(i,len(tr)):
rms = pycabs.rmsd(tr[i],tr[j])
distances[i][j] = distances[j][i] = rms
#save RMSD array as heat map
pycabs.heat_map(distances,"Protein model","Protein model","RMSD")
# clustering by K-medoids method (with 5 clusters)
clusterid,error,nfound = kmedoids(distances,nclusters=5,npass=15,initialid=None)
print clusterid,error
clusterid,error,nfound = kcluster(distances,nclusters=5,npass=15)
# save cluster medoids to file
