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Peptide Sequences/makefingerprint.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon May 14 10:26:18 2018
+
+@author: jtfl2
+"""
+import numpy as np
+filename = '12lengthpeptides.csv'
+f = open(filename,'r')
+f.readline()
+
+def find_base_structure(seq):
+    structure = np.zeros((3,3))
+    consecutive = 0
+    placement = 0
+    for amino in seq:
+        if amino in 'IMVAL' and consecutive <= 5:
+            structure[0, placement] = 1
+            consecutive = consecutive + 1
+            if consecutive > 5:
+                placement = placement + 1
+
+        if amino in 'RHKDESTNQG' and consecutive <= 10:
+            structure[1, placement] = 1
+            consecutive = consecutive + 1
+            if consecutive > 10:
+                placement = placement + 1
+
+        if amino in 'YWFRHKDESTNQG' and consecutive <= 13:
+            structure[2, placement] = 1
+            consecutive = consecutive + 1
+            if consecutive > 13:
+                placement = placement + 1
+
+

Peptide Sequences/pc_self.py

@@ -0,0 +1,302 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon May  7 16:16:42 2018
+
+@author: jtfl2
+"""
+import numpy as np
+
+masteraminos = {
+    'A':{   '3letter':  'Ala',
+	    'sc_mass':  15.0234,
+	    'pk1':      2.35,
+	    'pk2':      9.87,
+	    'sc_hphob': 0.5},
+
+    'R':{   '3letter':  'Arg',
+	    'sc_mass':  100.0873,
+	    'pk1':      1.82,
+	    'pk2':      8.99,
+	    'pk3':      12.48,
+	    'sc_hphob': 1.81},
+
+    'N':{   '3letter':  'Asn',
+	    'sc_mass':  58.0292,
+	    'pk1':      2.14,
+	    'pk2':      8.72,
+	    'sc_hphob': 0.85},
+
+    'D':{   '3letter':  'Asp',
+	    'sc_mass':  59.0132,
+	    'pk1':      1.99,
+	    'pk2':      9.9,
+	    'pk3':      3.9,
+	    'sc_hphob': 3.64},
+
+    'C':{   '3letter':  'Cys',
+	    'sc_mass':  46.9955,
+	    'pk1':      1.92,
+	    'pk2':      10.7,
+	    'pk3':      8.3,
+	    'sc_hphob': -0.02,
+	    'extco':    125},
+
+    'Q':{   '3letter':  'Gln',
+	    'sc_mass':  72.0448,
+	    'pk1':      2.17,
+	    'pk2':      9.13,
+	    'sc_hphob': 0.77},
+
+    'E':{   '3letter':  'Glu',
+	    'sc_mass':  73.0288,
+	    'pk1':      2.1,
+	    'pk2':      9.47,
+	    'pk3':      4.07,
+	    'sc_hphob': 3.63},
+
+    'G':{   '3letter':  'Gly',
+	    'sc_mass':  1.0078,
+	    'pk1':      2.35,
+	    'pk2':      9.78,
+	    'sc_hphob': 1.15},
+
+    'H':{   '3letter':  'His',
+	    'sc_mass':  81.0452,
+	    'pk1':      1.8,
+	    'pk2':      9.33,
+	    'pk3':      6.04,
+	    'sc_hphob': 2.33},
+
+    'I':{   '3letter':  'Ile',
+	    'sc_mass':  57.0702,
+	    'pk1':      2.32,
+	    'pk2':      9.76,
+	    'sc_hphob': -1.12},
+
+    'L':{   '3letter':  'Leu',
+	    'sc_mass':  57.0702,
+	    'pk1':      2.33,
+	    'pk2':      9.74,
+	    'sc_hphob': -1.25},
+
+    'K':{   '3letter':  'Lys',
+	    'sc_mass':  72.0811,
+	    'pk1':      2.16,
+	    'pk2':      9.06,
+	    'pk3':      10.54,
+	    'sc_hphob': 2.8},
+
+    'M':{   '3letter':  'Met',
+	    'sc_mass':  75.0267,
+	    'pk1':      2.13,
+	    'pk2':      9.28,
+	    'sc_hphob': -0.67},
+
+    'F':{   '3letter':  'Phe',
+	    'sc_mass':  91.0546,
+	    'pk1':      2.2,
+	    'pk2':      9.31,
+	    'sc_hphob': -1.71},
+
+    'P':{   '3letter':  'Pro',
+	    'sc_mass':  41.039,
+	    'pk1':      1.95,
+	    'pk2':      10.64,
+	    'sc_hphob': 0.14},
+
+    'S':{   '3letter':  'Ser',
+	    'sc_mass':  31.0183,
+	    'pk1':      2.19,
+	    'pk2':      9.21,
+	    'sc_hphob': 0.46},
+
+    'T':{   '3letter':  'Thr',
+	    'sc_mass':  45.0339,
+	    'pk1':      2.09,
+	    'pk2':      9.1,
+	    'sc_hphob': 0.25},
+
+    'W':{	'3letter':  'Trp',
+	    'sc_mass':  130.0655,
+	    'pk1':      2.46,
+	    'pk2':      9.41,
+	    'sc_hphob': -2.09,
+	    'extco':    5500},
+
+    'Y':{   '3letter':  'Tyr',
+	    'sc_mass':  107.0495,
+	    'pk1':      2.2,
+	    'pk2':      9.21,
+	    'pk3':      10.07,
+	    'sc_hphob': -0.71,
+	    'extco':    1490},
+
+    'V':{   '3letter':  'Val',
+	    'sc_mass':  43.0546,
+	    'pk1':      2.39,
+	    'pk2':      9.74,
+	    'sc_hphob': -0.46}}
+
+
+def CharCount(theString,theChar): 
+    result = 0
+    for i in range(0, len(theString)):
+        if theString[i] == theChar:
+            result = result + 1
+    return result
+
+
+def count_aminos(sequence):
+    rescounts = {}
+    rescounts['A'] = CharCount(sequence, "A")
+    rescounts['R'] = CharCount(sequence, "R")
+    rescounts['N'] = CharCount(sequence, "N")
+    rescounts['D'] = CharCount(sequence, "D")
+    rescounts['C'] = CharCount(sequence, "C")
+    rescounts['Q'] = CharCount(sequence, "Q")
+    rescounts['E'] = CharCount(sequence, "E")
+    rescounts['G'] = CharCount(sequence, "G")
+    rescounts['H'] = CharCount(sequence, "H")
+    rescounts['I'] = CharCount(sequence, "I")
+    rescounts['L'] = CharCount(sequence, "L")
+    rescounts['K'] = CharCount(sequence, "K")
+    rescounts['M'] = CharCount(sequence, "M")
+    rescounts['F'] = CharCount(sequence, "F")
+    rescounts['P'] = CharCount(sequence, "P")
+    rescounts['S'] = CharCount(sequence, "S")
+    rescounts['T'] = CharCount(sequence, "T")
+    rescounts['W'] = CharCount(sequence, "W")
+    rescounts['Y'] = CharCount(sequence, "Y")
+    rescounts['V'] = CharCount(sequence, "V")
+    return rescounts
+
+def calcmass(counts,seq):
+    alphamass = 56.0136
+    h2o_mass = 18.0105
+    mass = alphamass*len(seq) + h2o_mass
+    for key in counts:
+        mass = mass + counts[key]*masteraminos[key]['sc_mass']
+    mass = round(mass,4)
+
+    return mass 
+
+
+def cystine_count(cysteines):
+    return (cysteines-(cysteines%2))/2
+
+
+
+def calcec(counts,ranges):
+    ec2 = counts['W']*masteraminos['W']['extco'] + counts['Y']*masteraminos['Y']['extco']
+    ec1 = ec2 + cystine_count(counts['C'])*masteraminos['C']['extco']
+
+    if not ranges[3]:
+        ranges[3] = [ec1,ec1]
+    elif ranges[3][0] > ec1:
+        ranges[3][0] = ec1
+    elif ranges[3][1] < ec1:
+        ranges[3][1] = ec1
+
+    if not ranges[4]:
+        ranges[4] = [ec2,ec2]
+    elif ranges[4][0] > ec2:
+        ranges[4][0] = ec2
+    elif ranges[4][1] < ec2:
+        ranges[4][1] = ec2
+
+    return [ec1, ec2,ranges]
+
+
+def find_charge(a,b,pH):
+    c = 0
+    for key in a:
+        if a[key]['count'] > 0:
+            c = c + -a[key]['count']/(1 + pow(10, (a[key]['pk']-pH)))
+
+    for key in b:
+        if b[key]['count'] > 0:
+            c += b[key]['count']/(1 + pow(10, (pH - b[key]['pk'])))
+    c = round(c,3)
+    return c
+
+def calcpi(counts,sequence,ranges):
+    #alert("pI being calculated for " + sequence)
+    first_res = sequence[0]
+    #alert("first residue is "+first_res)
+    last_res = sequence[len(sequence)-1]
+    #alert("last residue is "+last_res)
+    acids = {   'C-term': {'count': 1,           'pk':masteraminos[first_res]['pk1']},
+		'D':      {'count': counts['D'], 'pk':masteraminos['D']['pk3']},
+		'E':      {'count': counts['E'], 'pk':masteraminos['E']['pk3']},
+		'C':      {'count': counts['C'], 'pk':masteraminos['C']['pk3']},
+		'Y':      {'count': counts['Y'], 'pk':masteraminos['Y']['pk3']}}
+
+    bases = {   'N-term': {'count': 1,           'pk':masteraminos[last_res]['pk2']},
+		'K':      {'count': counts['K'], 'pk':masteraminos['K']['pk3']},
+		'R':      {'count': counts['R'], 'pk':masteraminos['R']['pk3']},
+		'H':      {'count': counts['H'], 'pk':masteraminos['H']['pk3']}}    
+
+
+    for pH in np.arange(0,14,0.01):
+        if find_charge(acids,bases,pH) <= 0:
+            break
+
+    pI = round(pH,2)
+    net_charge = round(find_charge(acids, bases, 7))
+    if not ranges[0]:
+        ranges[0] = [pI,pI]
+    elif ranges[0][0] > pI:
+        ranges[0][0] = pI
+    elif ranges[0][1] < pI:
+        ranges[0][1] = pI
+
+    if not ranges[1]:
+        ranges[1] = [net_charge,net_charge]
+    elif ranges[1][0] > net_charge:
+        ranges[1][0] = net_charge
+    elif ranges[1][1] < net_charge:
+        ranges[1][1] = net_charge
+
+    return [pI,net_charge,ranges]
+
+
+def calchphob(counts,ranges):
+    hydrophobicity = 7.9
+    for key in counts:
+        hydrophobicity = hydrophobicity + counts[key] * masteraminos[key]['sc_hphob']
+    hydrophobicity = round(hydrophobicity,2)  
+
+    if not ranges[2]:
+        ranges[2] = [hydrophobicity,hydrophobicity]
+    elif ranges[2][0] > hydrophobicity:
+        ranges[2][0] = hydrophobicity
+    elif ranges[2][1] < hydrophobicity:
+        ranges[2][1] = hydrophobicity
+
+    return [round(hydrophobicity,2),ranges]
+
+
+
+def calcProp(seq,ranges):
+    P_count = count_aminos(seq)
+    P_mass = calcmass(P_count,seq)
+    [P_pI,P_charge,ranges] = calcpi(P_count,seq,ranges)
+    [P_hphob,ranges] = calchphob(P_count,ranges)
+    [P_ec1,P_ec2,ranges] = calcec(P_count,ranges)
+    return [seq,float(P_mass),float(P_pI),P_charge,float(P_hphob),P_ec1,P_ec2,ranges]
+
+
+def make_barcodes(properties):
+    from PIL import Image
+    ranges = properties[7]
+    binary = list(''.join(format(ord(x), 'b') for x in properties[0]))
+    binary = [255*int(i) for i in binary]
+    prop = [((float(properties[2])-ranges[0][0])/(ranges[0][1]-ranges[0][0]))*255,(((float(properties[3])-ranges[1][0])/(ranges[1][1]-ranges[1][0]))*255),(((float(properties[4])-ranges[2][0])/(ranges[2][1]-ranges[2][0]))*255),(((float(properties[5])-ranges[3][0])/(ranges[3][1]-ranges[3][0]))*255),(((float(properties[6])-ranges[4][0])/(ranges[4][1]-ranges[4][0]))*255)]
+    #prop = [round(i) for i in prop]
+    return [binary,prop]
+
+
+
+
+
+

Peptide Sequences/pepsequences.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Apr 27 13:38:09 2018
+
+@author: jtfl2
+"""
+import itertools as it
+import pc_self as pcs
+
+
+
+aromatic = {}
+hydrophobic = {}
+hydrophilic = {}
+per = {}
+ranges = [[],[],[],[],[]]
+
+hydrophobic['3'] = it.product('IMVAL', repeat=3)
+hydrophilic['5'] = it.product('TSGNQ', repeat=5)
+aromatic['4'] = it.product('YWF','HRKDETSGNQYWF','HRKDETSGNQYWF','HRKDETSGNQYWF')
+
+per['12'] = it.product(hydrophobic['3'],hydrophilic['5'],aromatic['4'], repeat = 1)
+
+f = open('12lengthpeptides.csv', 'w')
+f.write("Sequence,Mass,Isoelectric Point (pI),Net Charge,Hydrophobicity,Extinction coefficient 1,Extinction coefficient2" + "\n")
+p = ""
+for p in per['12']:
+    seq = "".join(p[0] + p[1] + p[2])
+    Info = pcs.calcProp(seq,ranges)
+    ranges = Info[7]
+    f.write(Info[0] + ',' + str(Info[1])+ ','+ str(Info[2])+ ','+ str(Info[3])+ ','+ str(Info[4])+ ','+ str(Info[5])+ ','+ str(Info[6])   + "\n")
+f.close()

Peptide Sequences/readme.txt

@@ -0,0 +1,7 @@
+Please keep the INI file and the executable file in the same folder. No 
+Installation is needed.
+
+LTFViewer 5.2u supports ANSI and Unicode. It currently does not support
+Unicode big endian and UTF-8.
+
+LTFViewer 5.2u requires Windows NT and up.