def convertToRichMSSpectrum(input_):
rs = pyopenms.RichMSSpectrum()
for p in input_:
rp = pyopenms.RichPeak1D()
rp.setMZ(p.getMZ())
rp.setIntensity(p.getIntensity())
rs.push_back(rp)
return rs
def testRichMSSpectrum(self):
spec = pyopenms.RichMSSpectrum()
p = pyopenms.RichPeak1D()
p.setMZ(500.0)
p.setIntensity(1e5)
spec.push_back(p)
p_back, = list(spec)
assert isinstance(p_back, pyopenms.RichPeak1D)
assert p_back.getMZ() == 500.0
assert p_back.getIntensity() == 1e5
def testRichMSSpectrum(self):
spec = pyopenms.RichMSSpectrum()
p = pyopenms.RichPeak1D()
p.setMZ(500.0)
p.setIntensity(1e5)
spec.push_back(p)
p_back, = list(spec)
assert isinstance(p_back, pyopenms.RichPeak1D)
assert p_back.getMZ() == 500.0
assert p_back.getIntensity() == 1e5
spec.updateRanges()
assert isinstance(spec.getMin()[0], float)
assert isinstance(spec.getMax()[0], float)
assert isinstance(spec.getMinInt(), float)
assert isinstance(spec.getMaxInt(), float)
assert spec.getMaxInt() == 1e5
assert spec.getMinInt() == 1e5
def simplisticBinnedScoring(self, phit, spectrum):
"""Simplistic phospho-scoring of a spectrum against a peptide hit.
This function enumerates all possible locations for the
phosphorylation and computes a similarity score between the
experimental spectrum and the theoretical spectrum for each
possibility.
"""
seq = phit.getSequence().toString()
seq = seq.replace("(Phospho)", "")
possibilities = []
spectrum_b = self.binSpectrum(spectrum)
charge = 1
# Iterate over all possible phosphosites
for m in re.finditer("[STY]", seq):
new_sequence = seq[:m.start() + 1] + "(Phospho)" + seq[m.start() -
1:]
new_aaseq = pyopenms.AASequence(new_sequence)
# Generate theoretical spectrum
spectrum_generator = pyopenms.TheoreticalSpectrumGenerator()
rs = pyopenms.RichMSSpectrum()
try:
spectrum_generator.addPeaks(rs, new_aaseq,
pyopenms.Residue.ResidueType.YIon,
charge)
spectrum_generator.addPeaks(rs, new_aaseq,
pyopenms.Residue.ResidueType.BIon,
charge)
except AttributeError:
# 1.11
spectrum_generator.addPeaks(rs, new_aaseq,
pyopenms.ResidueType.YIon, charge)
spectrum_generator.addPeaks(rs, new_aaseq,
pyopenms.ResidueType.BIon, charge)
theor = convertToMSSpectrum(rs)
theor_b = self.binSpectrum(theor)
# Compare theoretical spectrum to experimental spectrum
comp_score = self.compare_binnedSpectra(spectrum_b, theor_b)
possibilities.append([comp_score, new_aaseq])
# Sort the result by score, return the best scoring result
possibilities.sort(lambda x, y: -cmp(x[0], y[0]))
return possibilities[0]