Source Code for Module Bio.NMR.NOEtools

 1  # NOEtools.py: A python module for predicting NOE coordinates from 
 2  #                assignment data. 
 3  # 
 4  #    The input and output are modelled on nmrview peaklists. 
 5  #    This modules is suitable for directly generating an nmrview 
 6  #    peaklist with predicted crosspeaks directly from the 
 7  #    input assignment peaklist. 
 8   
 9  import xpktools 
10   
11   
12 -def predictNOE(peaklist, originNuc, detectedNuc, originResNum, toResNum): 
13  # Predict the i->j NOE position based on self peak (diagonal) assignments 
14  # 
15  # example predictNOE(peaklist,"N15","H1",10,12) 
16  #    where peaklist is of the type xpktools.peaklist 
17  #    would generate a .xpk file entry for a crosspeak 
18  #    that originated on N15 of residue 10 and ended up 
19  #    as magnetization detected on the H1 nucleus of 
20  #    residue 12. 
21  # CAVEAT: The initial peaklist is assumed to be diagonal (self peaks only) 
22  #       and currently there is not checking done to insure that this 
23  #       assumption holds true.  Check your peaklist for errors and 
24  #       off diagonal peaks before attempting to use predictNOE. 
25   
26      returnLine = ""  # The modified line to be returned to the caller 
27   
28      datamap = _data_map(peaklist.datalabels) 
29   
30      # Construct labels for keying into dictionary 
31      originAssCol = datamap[originNuc + ".L"] + 1 
32      originPPMCol = datamap[originNuc + ".P"] + 1 
33      detectedPPMCol = datamap[detectedNuc + ".P"] + 1 
34   
35      # Make a list of the data lines involving the detected 
36      if str(toResNum) in peaklist.residue_dict(detectedNuc) \ 
37      and str(originResNum) in peaklist.residue_dict(detectedNuc): 
38          detectedList = peaklist.residue_dict(detectedNuc)[str(toResNum)] 
39          originList = peaklist.residue_dict(detectedNuc)[str(originResNum)] 
40          returnLine = detectedList[0] 
41   
42          for line in detectedList: 
43              aveDetectedPPM = _col_ave(detectedList, detectedPPMCol) 
44              aveOriginPPM = _col_ave(originList, originPPMCol) 
45              originAss = originList[0].split()[originAssCol] 
46   
47          returnLine = xpktools.replace_entry(returnLine, originAssCol + 1, originAss) 
48          returnLine = xpktools.replace_entry(returnLine, originPPMCol + 1, aveOriginPPM) 
49   
50      return returnLine 
51   
52   
53 -def _data_map(labelline): 
54      # Generate a map between datalabels and column number 
55      #   based on a labelline 
56      i = 0  # A counter 
57      datamap = {}  # The data map dictionary 
58      labelList = labelline.split()  # Get the label line 
59   
60      # Get the column number for each label 
61      for i in range(len(labelList)): 
62          datamap[labelList[i]] = i 
63   
64      return datamap 
65   
66   
67 -def _col_ave(list, col): 
68      # Compute average values from a particular column in a string list 
69      total = 0 
70      n = 0 
71      for element in list: 
72          total += float(element.split()[col]) 
73          n += 1 
74      return total / n 
75