def __init__(self, seq, dmax=-1, chargePattern=[], validateSeq=False):
"""
seq = amino acid sequence as a string
"""
if not verifyType(seq, str):
raise SequenceException(
"Must pass a string to a new Sequence object")
# by default don't validate
if validateSeq:
seq = seq.upper()
seq = self.validateSequence(seq)
self.seq = seq.upper()
self.len = len(seq)
self.chargePattern = chargePattern
if(chargePattern == []):
for i in np.arange(0, self.len):
if(lkupTab.lookUpCharge(self.seq[i]) > 0):
chargePattern = np.append(chargePattern, 1)
elif(lkupTab.lookUpCharge(self.seq[i]) < 0):
chargePattern = np.append(chargePattern, -1)
else:
chargePattern = np.append(chargePattern, 0)
self.chargePattern = chargePattern
# initializing to prevent extra computational time
self.dmax = dmax
# set phosphosites as empty
self.phosphosites = []
# set color pallete
self.set_HTMLColorResiduePalette(aminoacids.DEFAULT_COLOR_PALETTE)
# build a complexity object (just code encapsulation - no state)
self.ComplexityObject = SequenceComplexity()
class Sequence:
"""
The sequence class is the main class which holds sequence properties
and carries out sequence parameter logic operations
"""
#...................................................................................#
def __init__(self, seq, dmax=-1, chargePattern=[], validateSeq=False):
"""
seq = amino acid sequence as a string
"""
if not verifyType(seq, str):
raise SequenceException(
"Must pass a string to a new Sequence object")
# by default don't validate
if validateSeq:
seq = seq.upper()
seq = self.validateSequence(seq)
self.seq = seq.upper()
self.len = len(seq)
self.chargePattern = chargePattern
if(chargePattern == []):
for i in np.arange(0, self.len):
if(lkupTab.lookUpCharge(self.seq[i]) > 0):
chargePattern = np.append(chargePattern, 1)
elif(lkupTab.lookUpCharge(self.seq[i]) < 0):
chargePattern = np.append(chargePattern, -1)
else:
chargePattern = np.append(chargePattern, 0)
self.chargePattern = chargePattern
# initializing to prevent extra computational time
self.dmax = dmax
# set phosphosites as empty
self.phosphosites = []
# set color pallete
self.set_HTMLColorResiduePalette(aminoacids.DEFAULT_COLOR_PALETTE)
# build a complexity object (just code encapsulation - no state)
self.ComplexityObject = SequenceComplexity()
#...................................................................................#
def __unicode__(self):
""" Returns the sequences """
return self.seq
#...................................................................................#
def __str__(self):
""" Returns the sequences """
return self.seq
#...................................................................................#
def validateSequence(self, seq):
processed = ""
AAs = data.aminoacids.ONE_TO_THREE.keys()
pos = 0
messageWarned = False
# for each residue in your protein sequence
for i in seq:
pos = pos + 1
if i not in AAs:
# if we find whitespace
if i.isspace():
if not messageWarned:
# only warn once...
status_message("Removing whitespace from sequence")
messageWarned = True
pass
# if unexpected residue/character bail
else:
raise SequenceException(
"Invalid amino acid [" + str(i) + "] found at position " + str(pos))
# else append sequence to the processed sequence
else:
processed = processed + i
# determine proline content and warn if over 15%
prolineContent = float(processed.count("P")) / float(len(processed))
if prolineContent > 0.15:
warning_message(
"This sequence has a proline content of greater than 15%.\nThis may render some analyses [notably kappa and phase diagram predictions] incorrect")
return processed
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# GENERAL SEQUENCE PROPERTIES
#
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#...................................................................................#
def fraction_disorder_promoting(self):
"""
Get the fraction of 'disorder promoting residues' in a
sequence
"""
D = ['T', 'A', 'G', 'R', 'D', 'H', 'Q', 'K', 'S', 'E', 'P']
# the O list is redundant but I've included it for good measure!
O = ['W', 'F', 'Y', 'I', 'M', 'L', 'V', 'N', 'C']
D_count = 0
O_count = 0
for i in self.seq:
if i in D:
D_count += 1
else:
O_count += 1
return float(D_count) / len(self.seq)
def amino_acid_fraction(self):
AADICT = {'A': 0,
'C': 0,
'D': 0,
'E': 0,
'F': 0,
'G': 0,
'H': 0,
'I': 0,
'K': 0,
'L': 0,
'M': 0,
'N': 0,
'P': 0,
'Q': 0,
'R': 0,
'S': 0,
'T': 0,
'V': 0,
'W': 0,
'Y': 0}
for i in self.seq:
AADICT[i] += 1
for i in AADICT:
AADICT[i] = float(AADICT[i]) / float(len(self.seq))
return AADICT
#...................................................................................#
def countPos(self):
""" Get the number of positive residues in the sequence """
return len(np.where(self.chargePattern > 0)[0])
#...................................................................................#
def countNeg(self):
""" Get the number of negative residues in the sequence """
return len(np.where(self.chargePattern < 0)[0])
#...................................................................................#
def countNeut(self):
""" Get the number of neutral residues in the sequence """
return len(np.where(self.chargePattern == 0)[0])
#...................................................................................#
def Fplus(self):
""" Get the fraction of positive residues in the sequence """
return self.countPos() / (self.len + 0.0)
#...................................................................................#
def Fminus(self):
""" Get the fraction of negative residues in the sequence """
return self.countNeg() / (self.len + 0.0)
#...................................................................................#
def FCR(self):
""" Get the fraction of charged residues in the sequence """
return (self.countPos() + self.countNeg()) / (self.len + 0.0)
#...................................................................................#
def NCPR(self):
""" Get the net charge per residue of the sequence """
return (self.countPos() - self.countNeg()) / (self.len + 0.0)
#...................................................................................#
def mean_net_charge(self):
""" Get the absolute magnitude of the mean net charge """
return abs(self.NCPR())
#...................................................................................#
def kappa(self):
"""
Return the kappa value, as defined in REF 1 \
"""
if self.deltaMax() == 0:
warning_message(
"The sequence has no charged residues - kappa is not a valid/relevant parameter")
return -1
else:
kappaVal = self.delta() / self.deltaMax()
# so the heuristics for kappa are good BUT may under estimate
# deltaMax is some cases. If this is a small deviation then we
# just set it to 0 because the sequence with the highest delta is probably
# an sequence-Isomer of the sequence we have. If this deviation is larger,
# however, it may be indicative of a bug in the code which we
# should address
if kappaVal > 1.0 and kappaVal < 1.1:
return 1.0
else:
return kappaVal
#...................................................................................#
def phasePlotRegion(self):
""" Returns the IDP diagram of states [REF 1] region based on the FCR/NCPR
rules. Possible return values are;
1 +-----------------------+---------------+
| + |
| + |
| 4 + |
F | + |
R | + |
A | + +
C | + + |
T | + + |
. | + 3 + |
0.5 | + + |
N | + + |
E + + |
G | + + |
+ 2 + + |
R | + + + 4 |
E | + 2 + + |
S | + + + |
| 1 + 2 + + |
0 +---------+---+-------------------------+
0 0.5 1
FRACTION OF POSITIVE RESIDUES
1) Weak polyampholytes and polyelectrolyte
2) Janus sequences
3) Strong polyampholytes
4) Strong polyelectrolytes
"""
fcr = self.FCR()
ncpr = self.NCPR()
# if we're in region 1
if(fcr < .25):
return 1
# if we're in region 2
elif(fcr >= .25 and fcr <= .35):
return 2
# if we're in region 3
elif(fcr > .35 and abs(ncpr) < 0.35):
return 3
# if we're in region 4 or 5
elif(self.Fplus() > 0.35):
if self.Fminus() > 0.35:
raise SequenceException(
"Algorithm bug when coping with phase plot regions")
return 5
elif(self.Fminus() > 0.35):
return 4
else: # This case is impossible but here for completeness\
raise SequenceException(
"Found inaccessible region of phase diagram. Numerical error")
#...................................................................................#
def phasePlotAnnotation(self):
""" Return the string annotation for the diagram of states region
which this sequence falls into
"""
region = self.phasePlotRegion()
if(region == 1):
return 'Globule/Tadpole'
elif(region == 2):
return 'Boundary Region'
elif(region == 3):
return 'Coils,Hairpins and Chimeras'
elif(region == 4):
return 'Negatively Charged Swollen Coils'
elif(region == 5):
return 'Positively Charged Swollen Coils'
else:
return 'ERROR, NOT A REAL REGION'
#...................................................................................#
def meanHydropathy(self):
""" Return the mean hydropathy value for the sequence
"""
ans = 0
for i in xrange(0, self.len):
ans += lkupTab.lookUpHydropathy(self.seq[i]) / self.len
return ans
#...................................................................................#
def uverskyHydropathy(self):
"""
Return the mean hydropathy as calculated by Uversky (window size of
five, with normalized Kyte-Doolitle scale.
This means we can use different hydrophobicity scales if we choose, but
it won't break the Uversky plots
"""
normalizedKD = data.aminoacids.get_KD_uversky()
translate = data.aminoacids.ONE_TO_THREE
ans = 0
for idx in xrange(0, self.len):
ans += normalizedKD[translate[self.seq[idx]]] / self.len
return ans
#...................................................................................#
def cumMeanHydropathy(self):
""" Return a vector of the average cumulative hydropathy. i.e.
[ vector of cumulative hydropathy ] / [ vector of sequence position ]
This allows you to examine how hydropathy changes through the sequence
"""
ans = [lkupTab.lookUpHydropathy(self.seq[0])]
for i in xrange(1, self.len):
ans.append(ans[i - 1] + lkupTab.lookUpHydropathy(self.seq[i]))
ans /= (np.arange(0, self.len) + 1)
return ans
#...................................................................................#
def linearDistOfNCPR(self, bloblen):
"""
Returns a np vertical stack object showing the NCPR over
blob-sized regions along the sequence
"""
nblobs = self.len - bloblen + 1
blobncpr = [0] * nblobs
# for each overlapping blob in the sequence calculate the NCPR
for i in np.arange(0, nblobs):
blob = self.chargePattern[i:(i + bloblen)]
bpos = len(np.where(blob > 0)[0])
bneg = len(np.where(blob < 0)[0])
blobncpr[i] = (bpos - bneg) / (bloblen + 0.0)
return np.vstack((np.arange(1, nblobs + 1), blobncpr))
#...................................................................................#
def linearDistOfFCR(self, bloblen):
"""
Returns an np vertical stack object showing the FCR over
blob-sized regions along the sequence
"""
nblobs = self.len - bloblen + 1
blobncpr = [0] * nblobs
# for each overlapping blob in the sequence calculate the FCR
for i in np.arange(0, nblobs):
blob = self.chargePattern[i:(i + bloblen)]
bpos = len(np.where(blob > 0)[0])
bneg = len(np.where(blob < 0)[0])
blobncpr[i] = (bpos + bneg) / (bloblen + 0.0)
return np.vstack((np.arange(1, nblobs + 1), blobncpr))
#...................................................................................#
def linearDistOfSigma(self, bloblen):
"""
Returns an np vertical stackobject showing how the "sigma" parameter
varies over blob-sized regions along the sequence
"""
nblobs = self.len - bloblen + 1
blobsig = [0] * nblobs
# for each overlapping blob in the sequence calculate the sigma
# parameter
for i in np.arange(0, nblobs):
blob = self.chargePattern[i:(i + bloblen)]
bpos = len(np.where(blob > 0)[0])
bneg = len(np.where(blob < 0)[0])
bncpr = (bpos - bneg) / (bloblen + 0.0)
bfcr = (bpos + bneg) / (bloblen + 0.0)
if(bfcr == 0):
bsig = 0
else:
bsig = bncpr**2 / bfcr
blobsig[i] = bsig
return np.vstack((np.arange(1, nblobs + 1), blobsig))
#...................................................................................#
def linearDistOfHydropathy(self, bloblen):
"""
Returns an np vertical stackobject showing how the 0 to 1 normallized hydrophobicity
varies over blob-sized regions along the sequence
"""
nblobs = self.len - bloblen + 1
blobhydro = [0] * nblobs
# construct a vector of the hydropathy values of each residue in
# in the sequence (using the Uversky-normalized KD scale which runs from 0 to 1
# with 1 being the most hydrophobic.
# get Uversky hydrophobicity lookup table
KDU = aminoacids.get_KD_uversky()
hydrochain = []
for i in self.seq:
hydrochain.append(KDU[aminoacids.ONE_TO_THREE[i]])
# for each overlapping blob in the sequence calculate the hydropathy
for i in np.arange(0, nblobs):
blob = hydrochain[i:(i + bloblen)]
blobhydro[i] = sum(blob) / float(bloblen)
return np.vstack((np.arange(1, nblobs + 1), blobhydro))
#...................................................................................#
def linearDistOfHydropathy_2(self, bloblen):
"""
Returns an np vertical stackobject showing how the 0 to 9 normallized hydrophobicity
varies over blob-sized regions along the sequence
"""
nblobs = self.len - bloblen + 1
blobhydro = [0] * nblobs
# construct a vector of the hydropathy values of each residue in
# in the sequence (using the re-set KD scale which runs from 0 to 9
# with 9 being the most hydrophobic. This scaling may be changed
# in future versions...
hydrochain = [lkupTab.lookUpHydropathy(res) for res in self.seq]
# for each overlapping blob in the sequence calculate the hydropathy
for i in np.arange(0, nblobs):
blob = hydrochain[i:(i + bloblen)]
blobhydro[i] = sum(blob)
return np.vstack((np.arange(1, nblobs + 1), blobhydro))
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# SEQUENCE COMPLEXITY FUNCTIONS
#
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
def get_linear_WF_complexity(
self,
alphabetSize=20,
userAlphabet={},
windowSize=10,
stepSize=1):
"""
Returns the Wooton-Federhen (WF) vectorial complexity using a sliding window approach
"""
return self.ComplexityObject.get_WF_complexity(
self.seq, alphabetSize, userAlphabet, windowSize, stepSize)
def get_linear_LC_complexity(
self,
alphabetSize=20,
userAlphabet={},
windowSize=10,
stepSize=1,
wordSize=3):
"""
Returns the Linguistic Complexity (LC) vectorial complexity using a sliding window approach
"""
return self.ComplexityObject.get_LC_complexity(
self.seq, alphabetSize, userAlphabet, windowSize, stepSize, wordSize)
def get_linear_LZW_complexity(
self,
alphabetSize=20,
userAlphabet={},
windowSize=10,
stepSize=1):
"""
Returns the Lempel-Ziv-Welch (LZW) vectorial complexity using a sliding window approach
"""
return self.ComplexityObject.get_LZW_complexity(
self.seq, alphabetSize, userAlphabet, windowSize, stepSize)
get_reducedAlphabetSequence(alphabetSize, userAlphabet)
def get_reducedAlphabetSequence(self, alphabetSize=20, userAlphabet={}):
"""
Returns the amino acid sequence after being translated to a reduced alphabet
"""
return self.ComplexityObject.reduce_alphabet(
self.seq, alphabetSize, userAlphabet)
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# KAPPA CALCULATING FUNCTIONS
#
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# The functions in the following section focus on parameters involved in the kappa
# calculation.
#
#...................................................................................#
def sigma(self):
""" Returns the sigma value for a sequence
\sigma = \dfrac{NCPR^2}{FCR}
When the sequence has one or more charged residues
sigma = (NCPR^2)/FCR
When the sequence has no charged residues
sigma = 0
"""
if(self.countNeut() == self.len):
return 0
else:
return self.NCPR()**2 / self.FCR()
#...................................................................................#
def deltaForm(self, bloblen):
""" Calculate the delta value as defined in REF 1
"""
sigma = self.sigma()
nblobs = self.len - bloblen + 1
ans = 0
for i in xrange(0, nblobs):
# get the blob charge pattern list
blob = self.chargePattern[i:(i + bloblen)]
# calculate a bunch of parameters for the blob
# with the blob sigma value being the ultimate
# goal
bpos = np.where(blob > 0)[0].size
bneg = np.where(blob < 0)[0].size
bncpr = (bpos - bneg) / (bloblen + 0.0)
bfcr = (bpos + bneg) / (bloblen + 0.0)
if(bfcr == 0):
bsig = 0
else:
bsig = bncpr**2 / bfcr
# calculate the square deviation of the
# blob sigma from the sequence sigma and
# weight by the number of blobs in the sequence
ans += (sigma - bsig)**2 / nblobs
return ans
#...................................................................................#
def delta(self):
"""
Return the average delta value from blobs of size 5 and 6, as
arrived upon as being optimal in REF 1
"""
return (self.deltaForm(5) + self.deltaForm(6)) / 2
#...................................................................................#
def deltaMax(self):
"""
Return the maximum possible delta value for the sequence
"""
# If this has been computed already, then return it
if(self.dmax != -1):
return self.dmax
elif(self.FCR() == 0):
self.dmax = 0
#################################################################
# FIRST computational trick - if only positive or negative
elif self.countPos() == 0 or self.countNeg() == 0:
nneuts = self.countNeut()
# construct a neutral block
neutralBlock = '0' * nneuts
# construct a charged block
if self.countPos() == 0:
# make a negative charge block
chargedBlock = '-' * self.countNeg()
chargeV = "-"
ncharge = self.countNeg()
else:
# make a positive charge block
chargedBlock = '+' * self.countPos()
chargeV = "+"
ncharge = self.countPos()
# if the charge block is shorter than the neutral block
if len(neutralBlock) > len(chargedBlock):
for position in xrange(0, (self.len - ncharge) + 1):
setupSequence = position * "0" + \
chargedBlock + "0" * (nneuts - position)
if not len(setupSequence) == self.len:
raise SequenceException(
"Error in DeltaMax calculation")
nseq = Sequence(setupSequence)
# update self.dmax if relevant
self.dmax = max([nseq.delta(), self.dmax])
# if the neutral block is shorter
else:
for position in xrange(0, (self.len - nneuts) + 1):
setupSequence = position * chargeV + \
neutralBlock + chargeV * (ncharge - position)
if not len(setupSequence) == self.len:
raise SequenceException(
"Error in DeltaMax calculation")
nseq = Sequence(setupSequence)
# update self.dmax if relevant
self.dmax = max([nseq.delta(), self.dmax])
#################################################################
# Second computational trick (Maximum Charge Separation)
# If we have no neutral residues this is easy
elif(self.countNeut() == 0):
nNeg = self.countNeg()
nPos = self.countPos()
posBlock = "+" * nPos
negBlock = "-" * nNeg
if len(posBlock) > len(negBlock):
for position in xrange(0, (self.len - nNeg) + 1):
setupSequence = position * "+" + \
negBlock + "+" * (nPos - position)
if not len(setupSequence) == self.len:
raise SequenceException(
"Error in DeltaMax calculation")
nseq = Sequence(setupSequence)
# update self.dmax if relevant
self.dmax = max([nseq.delta(), self.dmax])
else:
for position in xrange(0, (self.len - nPos) + 1):
setupSequence = position * "-" + \
posBlock + "-" * (nNeg - position)
if not len(setupSequence) == self.len:
raise SequenceException(
"Error in DeltaMax calculation")
nseq = Sequence(setupSequence)
# update self.dmax if relevant
self.dmax = max([nseq.delta(), self.dmax])
#################################################################
# Third computational trick (Maximization of # of Charged Blobs)
# relevant if we have 18 or more neutral residues
elif(self.countNeut() >= 18):
nneuts = self.countNeut()
# construct positive and negative blocks
posBlock = '+' * self.countPos()
negBlock = '-' * self.countNeg()
# There are three regions where you can have neutral residues
# to maximize delta: start/middle/end
# This set of loops iterates through the various relevant combinations
# of neutral residues at those locations. Importantly, this depends
# on a blob length of 5-6!
for startNeuts in xrange(0, 7):
for endNeuts in xrange(0, 7):
setupSequence = ''
midBlock = ''
endBlock = ''
setupSequence = '0' * startNeuts
endBlock = '0' * endNeuts
midBlock = '0' * (nneuts - startNeuts - endNeuts)
# now we construct *an* optimal sequence based on the start/mid/end
# permutation of neutral residues
setupSequence += posBlock
setupSequence += midBlock
setupSequence += negBlock
setupSequence += endBlock
nseq = Sequence(setupSequence)
# update self.dmax if relevant
self.dmax = max([nseq.delta(), self.dmax])
#################################################################
# Fourth computational trick (Search through set of sequences that fit
# DMAX pattern)
else:
posBlock = ''
negBlock = ''
nneuts = self.countNeut()
# construct positive and negative blocks
posBlock = '+' * self.countPos()
negBlock = '-' * self.countNeg()
# iterate through different permutations where
# the size of the middle number of neutral residues varies
for midNeuts in xrange(0, nneuts + 1):
midBlock = '0' * midNeuts
for startNeuts in xrange(0, nneuts - midNeuts + 1):
setupSequence = ''
# construct some permutation of the sequence
setupSequence = '0' * startNeuts
setupSequence += posBlock
setupSequence += midBlock
setupSequence += negBlock
setupSequence = setupSequence + '0' * \
(nneuts - startNeuts - midNeuts)
nseq = Sequence(setupSequence)
# update self.dmax if relevant
self.dmax = max([nseq.delta(), self.dmax])
return self.dmax
#...................................................................................#
def swapRes(self, index1, index2):
"""
Causes the two residues indexed by index1 and index2 in the sequence
to be swapped. This returns a new sequence object with pre-calculated dmax
"""
if(index1 == index2):
return Sequence(self.seq)
# we want index 2 to be greater than index 1
elif(index2 < index1):
index1, index2 = index2, index1
else:
pass
# create a new tempseq as a charlist and then
# swap the characters and positions index1 and index2
tempseq = list(self.seq)
tempseq[index1], tempseq[index2] = tempseq[index2], tempseq[index1]
charge1 = self.chargePattern[index1]
charge2 = self.chargePattern[index2]
# creates a totally new array with new values - do we need this?
tempChargeSeq = cp.deepcopy(self.chargePattern)
tempChargeSeq[index1] = charge2
tempChargeSeq[index2] = charge1
# returns a new sequence with dmax and the chargeSequence already
# generated
return Sequence(''.join(tempseq), self.dmax, tempChargeSeq)
#...................................................................................#
def swapRandChargeRes(self, frozen=set()):
""" Function which randomly selects two residues and swaps them if that
swap would change the kappa value
"""
# get a random number
rand = rng.Random()
rand.seed(time.time())
# determine the indices from which we can swap
# (i.e. all positive indices which do not overlap with the set of frozen
# residues)
posInd = set(np.where(self.chargePattern > 0)[0]) - frozen
negInd = set(np.where(self.chargePattern < 0)[0]) - frozen
neutInd = set(np.where(self.chargePattern == 0)[0]) - frozen
if(len(neutInd) == 0):
if(len(posInd) == 0 or len(negInd) == 0):
status_message(
'swap will not change kappa, only one charge type in sequence')
return self
else:
chargeType = [1, 2]
elif(len(negInd) == 0):
if(len(posInd) == 0 or len(neutInd) == 0):
status_message(
'swap will not change kappa, only one charge type in sequence')
return self
else:
chargeType = [1, 3]
elif(len(posInd) == 0):
if(len(negInd) == 0 or len(neutInd) == 0):
status_message(
'swap will not change kappa, only one charge type in sequence')
return self
else:
chargeType = [2, 3]
else:
chargeType = rand.sample([1, 2, 3], 2)
if(chargeType[0] == 1):
swapPair1 = rand.sample(posInd, 1)
elif(chargeType[0] == 2):
swapPair1 = rand.sample(negInd, 1)
elif(chargeType[0] == 3):
swapPair1 = rand.sample(neutInd, 1)
if(chargeType[1] == 1):
swapPair2 = rand.sample(posInd, 1)
elif(chargeType[1] == 2):
swapPair2 = rand.sample(negInd, 1)
elif(chargeType[1] == 3):
swapPair2 = rand.sample(neutInd, 1)
return self.swapRes(swapPair1[0], swapPair2[0])
#...................................................................................#
def full_shuffle(self, frozen=set()):
"""
Function which totally shuffles a sequences, but keeps the positions
in the frozen set in their position
"""
moveable_indicies = set(np.arange(0, self.len)) - set(frozen)
lookup = {}
index = 0
for i in self.seq:
lookup[index] = i
index = index + 1
# lookup now corresponds to an index->AA lookupyable with lookup
# indexed
newseq = list(moveable_indicies)
rand = rng.Random()
rand.seed(time.time())
rand.shuffle(newseq)
sequencelist = list(self.seq)
new_seq = []
for i in xrange(0, self.len):
if i in frozen:
new_seq.append(lookup[i])
else:
new_seq.append(lookup[newseq.pop()])
return Sequence("".join(new_seq), self.dmax)
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# PHOSPHORYLATION RELATED FUNCTIONS
#
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# The functions below focus on phosphorylation and the like
#
#...................................................................................#
def setPhosPhoSites(self, listOfPsites):
"""
Set one or more sites on your sequence which can be phosphorylated. Note that
this indexes from 1 (like all of bioinformatics) and not from 0 (like all of
computer science).
i.e. "KKKYKKK" the Y here is at position 4
Internally we do translate to indexing from 0, but this is not something you
should have to worry about
Note that all data validation for the phosphosite list is done in this function.
"""
# if we passed a single value not in a list then convert to a list of length
# 1
if isinstance(listOfPsites, int):
tmp = listOfPsites
listOfPsites = []
listOfPsites.appned(tmp)
# evaluate proposed phosphosites
for site in listOfPsites:
# check we can convert to an integer!
site = int(site)
# python indexes from 0 but humans from 1
idx = site - 1
# if we're outside our sequence
if idx >= len(self.seq) or idx < 0:
warning_message("Proposed phosphosite (" + str(idx + 1) +
" is outside sequence range. Skipping...")
pass
# grab the residue letter from the sequence
res = self.seq[idx]
status_message("Setting " + res + str(idx + 1))
if res not in ["S", "T", "Y"]:
# we skip it if it seems like an unphosphorylatable residue
warning_message(
'Position ' +
str(site) +
' in sequence is a non phosphorylatable residue [' +
str(res) +
']')
else:
if idx in self.phosphosites:
# don't add the same residue twice, but no need to warn
# about it
pass
else:
# let's add that bad-boy!
self.phosphosites.append(idx)
#...................................................................................#
def calculateNumberDifferentPhosphoStates(self):
""" Function which returns the number of different phosphorylation
states assuming each phosphosite can be phosphorylated independently
of each other.
This can be useful if you want to estimate how long a complete
phoshostate distribution calculation might take (each calc is about
0.2-1 second, depending on your CPU)
"""
return np.power(2, len(self.phosphosites))
#...................................................................................#
def clear_phosphosites(self):
""" Function which zeros out all the stored phosphosite data """
self.phosphosites = []
#...................................................................................#
def calculateKappaDistOfPhosphoStates(self):
""" Function which, using the previously defined possible phosphosites, calculates the cloud
of possible kappa values for all combination of phosphorylation. This is a relativly long
operation due to the fact that the FULL kappa must be recalculated everytime (i.e. delta max
must be recomputed each time, unlike with sequence permutants where DMAX remains constant).
To aid with this, every 50 calculations the progress is printed
"""
##
#----------------------------------------------------------------------
# local function
def print_progress(count, total):
if (count % 50) == 0:
status_message("Done " + str(count) + " of " + str(total))
num_calcs = self.calculateNumberDifferentPhosphoStates()
#----------------------------------------------------------------------
# itertools.product("01"..) produces all variants of a list where each
# element is either 0 or 1 using an iterator
phosphokappa = []
count = 0
for phosphostatus in itertools.product(
"01", repeat=len(self.phosphosites)):
newseq = list(self.seq)
indx = 0
# look over each element in our phosphosite on/off list
for i in phosphostatus:
# if that element is ON
if int(i) == 1:
# set the AA at that position to a negative residue (we use E but
# could be D)
newseq[self.phosphosites[indx]] = "E"
indx = indx + 1
# now we've replaced some number of T/Y/R with E representing a different
# phosphostate
newseq = "".join(newseq)
newseqObj = Sequence(newseq)
# now add that new kappa to the list, update, and repeat!
phosphokappa.append(
(newseqObj.kappa(),
newseqObj.Fplus(),
newseqObj.Fminus(),
newseqObj.FCR(),
newseqObj.NCPR(),
newseqObj.meanHydropathy(),
phosphostatus))
print_progress(count, num_calcs)
count = count + 1
return phosphokappa
#...................................................................................#
def get_phosphosites(self):
""" Returns the list of phosphosites (indexed from 1
not zero)
"""
newSites = []
for i in self.phosphosites:
newSites.append(i + 1)
return newSites
#...................................................................................#
def get_phosphosequence(self):
"""
Returns a sequence with all phosphorylated residues converted to E.
Phosphorylated residues defined by the phosphosites object variable
"""
if len(self.phosphosites) == 0:
warning_message(
"No phosphosites defined - phosphosequence will be equivalent to the unphosphorylated sequence")
# first defined the empty sequence
pseq = ""
idx = 0
# for each position if that residue is phosphorylatable set it to 'E' instead
# of the actual Y/S/T
for i in self.seq:
if idx in self.phosphosites:
# extra level of checking!
if i not in ["S", "Y", "T"]:
raise SequenceException(
"In get_phosphosequence - trying to replace non-phsophrylatable residue with GLU")
pseq = pseq + "E"
else:
pseq = pseq + self.seq[idx]
idx = idx + 1
return pseq
#...................................................................................#
def kappa_at_maxPhos(self):
"""
Returns the kappa if all the phosphosites
were phosphorylated
"""
# no phosphosites defined
if len(self.phosphosites) == 0:
return self.kappa()
else:
newseq = list(self.seq)
for pos in self.phosphosites:
newseq[pos] = "E"
newseq = "".join(newseq)
newseqObj = Sequence(newseq)
return newseqObj.kappa()
#...................................................................................#
def get_STY_residues(self):
"""
Where are all the Ser/Thr/Tyr residues in your
sequence.
Note we return positions which index from 1 not from 0
"""
sites = []
idx = 1
for i in self.seq:
if i in ["Y", "S", "T"]:
sites.append(idx)
idx = idx + 1
return sites
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#
# FORMATTING FUNCTION
#
# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
#...................................................................................#
def toString(self):
"""
"""
s = "%i\t%3.5f\t%3.5f\t%3.5f\t%3.5f\t%3.5f\t%3.5f\t%3.5f\t%3.5f\t%3.5f" % (self.len, self.Fminus(), self.Fplus(
), self.FCR(), self.NCPR(), self.sigma(), self.delta(), self.deltaMax(), self.kappa(), self.meanHydropathy())
return s
def set_HTMLColorResiduePalette(self, colorDict):
"""
Function which lets you userdefine the color pallete being used with a dictionary of [AA]="color" mappings where
color must be one of the 17 key HTML colours;
['aqua', 'black', 'blue', 'fuchsia', 'gray', 'green', 'lime', 'maroon', 'navy', 'olive', 'orange', 'purple',
'red', 'silver', 'teal', 'white', 'yellow']
"""
valid = {}
# for each one letter amino acid code
for i in aminoacids.ONE_TO_THREE:
# check all the amino acids were there
if i not in colorDict:
raise SequenceException(
"When trying to set amino acid color palette the amino acid " +
str(i) +
" was missing from the input dictionary")
# check if the color is real
if colorDict[i] not in [
'aqua',
'black',
'blue',
'fuchsia',
'gray',
'green',
'lime',
'maroon',
'navy',
'olive',
'orange',
'purple',
'red',
'silver',
'teal',
'white',
'yellow']:
raise SequenceException(
"When trying to set amino acid color palette the color " + str(
colorDict[i]) + " was used, which is not one of the 17 standard HTML colours")
# if we get here update the 'valid' dictionary
valid[i] = colorDict[i].lower()
# if we got here we got through the full 20 amino acids succesfully
self.aminoAcidColorMap = {}
for i in valid:
self.aminoAcidColorMap[i] = valid[i]
def get_HTMLColorString(self):
"""
Function which creates a <p> </p> wrapped HTML string with the sequence colored according to the defined
aminoAcidColorMap.
"""
colorString = '<p style="font-family:Courier;">'
count = -1
for residue in self.seq:
count = count + 1
if(np.mod(count, 10) == 0):
colorString = colorString + " "
if(np.mod(count, 50) == 0):
colorString = colorString + "<br>"
color = self.aminoAcidColorMap[residue]
colorString = '%s<span style="color:%s">%s</span>' % (
colorString, color, residue)
colorString = colorString + "</p>"
return colorString
#...................................................................................#
def toFileString(self):
s = "Sequence :\t%s\n\n" % (self.seq)
s += "N :\t%i\n" % (self.len)
s += "f- :\t%3.5f\n" % (self.Fminus())
s += "f+ :\t%3.5f\n" % (self.Fplus())
s += "FCR :\t%3.5f\n" % (self.FCR())
s += "NCPR :\t%3.5f\n" % (self.NCPR())
s += "Kappa :\t%3.5f\n" % (self.kappa())
s += "Sigma :\t%3.5f\n" % (self.sigma())
s += "Delta :\t%3.5f\n" % (self.delta())
s += "Max Delta :\t%3.5f\n" % (self.deltaMax())
s += "Hydropathy :\t%3.5f\n\n" % (self.meanHydropathy())
s += "Phase Plot Region: %i\n" % (self.phasePlotRegion())
s += "Phase Plot Annotation: %s\n" % (self.phasePlotAnnotation())
return s
