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# Copyright 2004 by Bob Bussell. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""NOEtools: For predicting NOE coordinates from assignment data.
The input and output are modelled on nmrview peaklists.
This modules is suitable for directly generating an nmrview
peaklist with predicted crosspeaks directly from the
input assignment peaklist.
"""
from . import xpktools
def predictNOE(peaklist, originNuc, detectedNuc, originResNum, toResNum):
"""Predict the i->j NOE position based on self peak (diagonal) assignments
Parameters
----------
peaklist : xprtools.Peaklist
List of peaks from which to derive predictions
originNuc : str
Name of originating nucleus.
originResNum : int
Index of originating residue.
detectedNuc : str
Name of detected nucleus.
toResNum : int
Index of detected residue.
Returns
-------
returnLine : str
The .xpk file entry for the predicted crosspeak.
Examples
--------
Using predictNOE(peaklist,"N15","H1",10,12)
where peaklist is of the type xpktools.peaklist
would generate a .xpk file entry for a crosspeak
that originated on N15 of residue 10 and ended up
as magnetization detected on the H1 nucleus of
residue 12
Notes
=====
The initial peaklist is assumed to be diagonal (self peaks only)
and currently there is no checking done to insure that this
assumption holds true. Check your peaklist for errors and
off diagonal peaks before attempting to use predictNOE.
"""
returnLine = "" # The modified line to be returned to the caller
datamap = _data_map(peaklist.datalabels)
# Construct labels for keying into dictionary
originAssCol = datamap[originNuc + ".L"] + 1
originPPMCol = datamap[originNuc + ".P"] + 1
detectedPPMCol = datamap[detectedNuc + ".P"] + 1
# Make a list of the data lines involving the detected
if str(toResNum) in peaklist.residue_dict(detectedNuc) \
and str(originResNum) in peaklist.residue_dict(detectedNuc):
detectedList = peaklist.residue_dict(detectedNuc)[str(toResNum)]
originList = peaklist.residue_dict(detectedNuc)[str(originResNum)]
returnLine = detectedList[0]
for line in detectedList:
aveDetectedPPM = _col_ave(detectedList, detectedPPMCol)
aveOriginPPM = _col_ave(originList, originPPMCol)
originAss = originList[0].split()[originAssCol]
returnLine = xpktools.replace_entry(returnLine, originAssCol + 1, originAss)
returnLine = xpktools.replace_entry(returnLine, originPPMCol + 1, aveOriginPPM)
return returnLine
def _data_map(labelline):
# Generate a map between datalabels and column number
# based on a labelline
i = 0 # A counter
datamap = {} # The data map dictionary
labelList = labelline.split() # Get the label line
# Get the column number for each label
for i in range(len(labelList)):
datamap[labelList[i]] = i
return datamap
def _col_ave(list, col):
# Compute average values from a particular column in a string list
total = 0.0
n = 0
for element in list:
total += float(element.split()[col])
n += 1
return total / n
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