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 showSummary(self):
from PEATDB.Ekin.Titration import TitrationAnalyser
self.showHeader(menu=1)
DB = self.DB = self.connect()
sys.stdout.flush()
t = TitrationAnalyser()
ekindata = t.getEkinDicts(DB)
print '<div class="main">'
t.dotitDBStats(ekindata)
print '</div>'
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 showAnalysis(self):
"""Analysis of current pKas"""
from PEATDB.Ekin.Titration import TitrationAnalyser
self.showHeader(menu=1)
DB = self.DB = self.connect()
t = TitrationAnalyser()
print '<div class="main">'
print '<p>Selected plots below reflect some of the analysis shown in the \
<a href="%s/paper_2010.pdf"> original paper</a> updated for the current dataset. </p>' %self.bindir
print '<a>The distributions shown are of the change in chemical shift over all\
detected titrations. `Reliable` pKas are those associated with\
the largest chemical shift changes in a titration curve and that meets the criteria defined in\
the paper. We define primary pKa values simply as the subset of the reliable pKa values \
that originate from titration curves with with only one titration.</a>'
sys.stdout.flush()
colnames = ['1H NMR','15N NMR','13C NMR']
for col in colnames:
p = t.extractpKas(DB,col,silent=True,minspan=0.06)
print '<div>'
print "<h2>%s: Distribution of Δδ for fitted pKa values</h2>" %col
img1 = t.analysepKas(p, silent=True, prefix=col, path=self.imagepath)
#t.makepKasTable(p)
print '<img src="%s/%s" align=center width=800 class="plot">' %(self.plotsdir, img1)
print '</div>'
sys.stdout.flush()
#compare nuclei
img2, img3 = t.compareNuclei(DB, '15N NMR', '1H NMR', titratable=False, silent=True, path=self.imagepath)
print '<p>Below is an analysis of the correspondence between fitted pKas for 1H and 15N \
where they are available for the same residue in the same protein. This is the same\
plot as figure 4 in the original paper updated for the current dataset.\
The plots are divided into reliable and other pKas for comparison.</p>'
print '<div>'
print '<center><img src="%s/%s" align=center width=600 class="plot"></center>' %(self.plotsdir, img2)
print '</div>'
print '<p>The same plot as above broken down by residue type and shown only for titratable\
residues.</p>'
print '<div>'
print '<center><img src="%s/%s" align=center width=600 class="plot"></center>' %(self.plotsdir, img3)
print '</div>'
self.footer()
return
def showDownloads(self):
"""Downloads links"""
self.showHeader(menu=1)
print '<div class="main">'
print '<h2>Downloads</h2>'
print '<h3>The entire dataset may be downloaded in text format as a single zip file.</h3>'
print
#print '<p>Exporting current data, please wait a moment...</p>'
sys.stdout.flush()
DB = self.DB = self.connect()
from PEATDB.Ekin.Titration import TitrationAnalyser
t = TitrationAnalyser()
filename = t.exportAll(DB)
print '<h2><a href="%s/%s"> download zip</a></h2>' %(self.bindir,os.path.basename(filename))
print '</div>'
self.footer()
return
from PEATDB.Ekin.Titration import TitrationAnalyser
from PEATDB.Base import PDatabase
from PEATDB.Ekin.Base import EkinProject
from PEATDB.DictEdit import DictEditor
import os
import pickle
cols = ['15N NMR', '1H NMR']
nuclnames = {'1H NMR':'H','15N NMR':'N'}
complete = ['HEWL', 'Bovine Beta-Lactoglobulin',
'Plastocyanin (Anabaena variabilis)',
'Plastocyanin (Phormidium)',
'Glutaredoxin', 'CexCD (Apo)',
'Protein G B1','Xylanase (Bacillus subtilus)']
col=cols[0]
nucl = nuclnames[col]
t = TitrationAnalyser()
#ghost mapping..
DB = PDatabase(server='peat.ucd.ie', username='******',
password='******', project='titration_db',
port=8080)
p=t.extractpKas(DB, col, names=['HEWL'], titratable=False, reliable=False, minspan=0.06)
t.mappKas(DB,col,p,names=['HEWL'],
nucleus=nucl,calculatespans=False)
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
from PEATDB.Ekin.Base import EkinProject
from PEATDB.DictEdit import DictEditor
import os
import pickle
cols = ['15N NMR', '1H NMR']
nuclnames = {'1H NMR': 'H', '15N NMR': 'N'}
complete = [
'HEWL', 'Bovine Beta-Lactoglobulin', 'Plastocyanin (Anabaena variabilis)',
'Plastocyanin (Phormidium)', 'Glutaredoxin', 'CexCD (Apo)', 'Protein G B1',
'Xylanase (Bacillus subtilus)'
]
col = cols[0]
nucl = nuclnames[col]
t = TitrationAnalyser()
#ghost mapping..
DB = PDatabase(server='peat.ucd.ie',
username='******',
password='******',
project='titration_db',
port=8080)
p = t.extractpKas(DB,
col,
names=['HEWL'],
titratable=False,
reliable=False,
minspan=0.06)
t.mappKas(DB, col, p, names=['HEWL'], nucleus=nucl, calculatespans=False)
