def titDBUtils(self, DB=None, col=None, prot=None, a=None, E=None,
refit=False, addmeta=False, getexperrs=False,
yuncert=None):
"""Add some meta and refit all for an ekin prj or a rec/field in db"""
if E==None and DB != None:
E = DB[prot][col]
E.checkDatasets()
t = TitrationAnalyser()
if refit == True:
models = ['Linear', '1 pKa 2 Chemical shifts',
'2 pKas, 3 Chemical shifts',
'3 pKas, 4 Chemical shifts']
E = t.findBest(E, models, geterrs=False)
if addmeta == True:
E = t.setMetaInfo(E, atom=a)
if getexperrs == True:
if yuncert == None:
print 'No value for Y uncertainty!, please supply it'
return
print 'Using %s for y uncertainty.' %yuncert
print
E = t.getExpErrs(E, xuncert=0.1, yuncert=yuncert)
self.save(DB, col, prot, E)
#DB.commit('refit/added meta info')
return E
def analysepKas(self, p=None):
"""Get the main pKas of all/titr group and do analysis"""
E = self.currprj
if E==None: return
t = TitrationAnalyser()
if p == None:
p = t.findpKas(E, titratable=True, reliable=False, minspan=0.06)
t.analysepKas(p)
return
def analyseTitDB(self, DB, col, names=None):
"""Extract titdb pKas"""
import matplotlib.pyplot as plt
plt.rc('font',size=28)
plt.rc('savefig',dpi=300)
plt.rc('axes',linewidth=.5)
#plt.rc('text',usetex=True)
nuclnames = {'1H NMR':'H','15N NMR':'N'}
t = TitrationAnalyser()
#extract reliable pkas from selected proteins
#p = t.extractpKas(DB, col, names=names, titratable=False, reliable=False, minspan=0.06)
#t.analysepKas(p)
t.compareNuclei(DB, '15N NMR', '1H NMR', names=names, titratable=True)
return
def addpKaTables(self, DB, names, col='1H NMR'):
"""Create labbook tables for 'real' pKas for required proteins"""
t = TitrationAnalyser()
prots = t.getProtNames(DB)
for name in names:
recname = DB.getRecordName(name)
E = DB[recname][col]
titrresidues = t.getResidues(E, titratable=True)
S = DB.createLabbookSheet(name+'.pKas')
for r in titrresidues:
d, res, resnum = r
pKa = ''
S.addRecord(res+resnum,pka=pKa,resname=res,
resnum=resnum,error='')
DB.saveLabbook(name+'.pKas', S)
DB.saveLabbooktoFile('titdb.labbook')
return
def exportAll(self, DB, col=None):
t = TitrationAnalyser()
t.exportAll(DB, col)
return
# Jens Nielsen
# SBBS, Conway Institute
# University College Dublin
# Dublin 4, Ireland
#
# Author: Damien Farrell 2009
import pickle, sys, os, copy, time, types
import numpy
from PEATDB.Base import PDatabase
from PEATDB.Ekin.Titration import TitrationAnalyser
from PEATDB.Ekin.Base import EkinProject, EkinDataset
path = os.environ['HOME']
t = TitrationAnalyser()
H = '1H NMR'
N = '15N NMR'
C = '13C NMR'
'''yuncerts = {H: 0.03, fields[1]:0.2, fields[2]: 0.1} #from lawrence
minspans = {fields[0]: 0.06, fields[1]:0.2, fields[2]: 0.2} '''
complete = [
'HEWL', 'Bovine Beta-Lactoglobulin', 'Plastocyanin (Anabaena variabilis)',
'Plastocyanin (Phormidium)', 'CexCD (Apo)', 'Protein G B1', 'Glutaredoxin',
'Staphylococcal Nuclease D+PHS'
]
def loadDB():
from PEATDB.Base import PDatabase
