def search(self):
for spectrum in self.spectrum_dataset:
if spectrum.has_precursor_charge():
charge_list = [spectrum.precursor_charge]
min_charge = 1 if spectrum.precursor_charge > 0 else -1
else:
if self.maximum_charge < 0:
charge_list = list(charge_range_(-1, self.maximum_charge))
min_charge = -1
else:
charge_list = list(charge_range_(1, self.maximum_charge))
min_charge = 1
precursor_candidates_masses = [
(neutral_mass(spectrum.precursor_mz, c, self.charge_carrier), c) for c in charge_list
]
precursor_candidate_structures = []
for candidate_mass, candidate_charge in precursor_candidates_masses:
precursor_candidate_structures.extend(
(s, candidate_charge) for s in self.structure_database.search_mass(
candidate_mass, self.precursor_mass_tolerance))
solutions = []
for precursor, charge in precursor_candidate_structures:
solution = match(
spectrum.peaklist, precursor, self.product_ion_mass_tolerance,
charge_range=(min_charge, charge), charge_carrier=self.charge_carrier)
solutions.append(solution)
solution_set = SolutionSet(spectrum, solutions, spectrum.name)
yield solution_set
def test_deconvolution(self):
scan = self.make_scan()
algorithm_type = AveragineDeconvoluter
deconresult = deconvolute_peaks(
scan.peak_set, {
"averagine": peptide,
"scorer": PenalizedMSDeconVFitter(5., 1.),
"use_subtraction": False,
}, left_search_limit=3, deconvoluter_type=algorithm_type)
dpeaks = deconresult.peak_set
assert len(dpeaks) == 6
for point in points:
peak = dpeaks.has_peak(neutral_mass(point[0], point[1]))
self.assertIsNotNone(peak)
def test_deconvolution(self):
scan = self.make_scan()
algorithm_type = AveragineDeconvoluter
deconresult = deconvolute_peaks(
scan.peak_set, {
"averagine": peptide,
"scorer": PenalizedMSDeconVFitter(5., 1.),
"use_subtraction": False
}, deconvoluter_type=algorithm_type)
dpeaks = deconresult.peak_set
assert len(dpeaks) == 6
for point in points:
peak = dpeaks.has_peak(neutral_mass(point[0], point[1]))
self.assertIsNotNone(peak)
def scale(self, mz, charge=1, charge_carrier=PROTON):
neutral = neutral_mass(mz, charge, charge_carrier)
scale = neutral / self.base_mass
scaled = {}
for elem, count in self.base_composition.items():
scaled[elem] = round(count * scale)
scaled_mass = calculate_mass(scaled)
delta_hydrogen = round(scaled_mass - neutral)
H = scaled["H"]
if H > delta_hydrogen:
scaled["H"] = H - delta_hydrogen
else:
scaled["H"] = 0
return scaled
def scale(self, mz, charge=1, charge_carrier=PROTON):
neutral = neutral_mass(mz, charge, charge_carrier)
scale = neutral / self.base_mass
scaled = {}
for elem, count in self.base_composition.items():
scaled[elem] = round(count * scale)
scaled_mass = calculate_mass(scaled)
delta_hydrogen = round(scaled_mass - neutral)
H = scaled["H"]
if H > delta_hydrogen:
scaled["H"] = H - delta_hydrogen
else:
scaled["H"] = 0
return scaled
def test_graph_deconvolution(self):
scan = self.make_scan()
scan.pick_peaks()
self.assertIsNotNone(scan.peak_set)
algorithm_type = AveraginePeakDependenceGraphDeconvoluter
deconresult = deconvolute_peaks(
scan.peak_set, {
"averagine": peptide,
"scorer": PenalizedMSDeconVFitter(5., 1.)
}, deconvoluter_type=algorithm_type)
dpeaks = deconresult.peak_set
deconvoluter = deconresult.deconvoluter
for point in points:
peak = dpeaks.has_peak(neutral_mass(point[0], point[1]))
self.assertIsNotNone(peak)
fp = scan.has_peak(peak.mz)
self.assertAlmostEqual(
deconvoluter.peak_dependency_network.find_solution_for(fp).mz,
peak.mz, 3)
def test_graph_deconvolution(self):
scan = self.make_scan()
scan.pick_peaks()
self.assertIsNotNone(scan.peak_set)
algorithm_type = AveraginePeakDependenceGraphDeconvoluter
deconresult = deconvolute_peaks(
scan.peak_set, {
"averagine": peptide,
"scorer": PenalizedMSDeconVFitter(5., 1.)
}, deconvoluter_type=algorithm_type)
dpeaks = deconresult.peak_set
assert len(dpeaks) == 2
deconvoluter = deconresult.deconvoluter
for point in points:
peak = dpeaks.has_peak(neutral_mass(point[0], point[1]))
self.assertIsNotNone(peak)
fp = scan.has_peak(peak.mz)
self.assertAlmostEqual(
deconvoluter.peak_dependency_network.find_solution_for(fp).mz,
peak.mz, 3)
def scale(self, mz, charge=1, charge_carrier=PROTON):
"""Given an m/z and a charge state, interpolate the composition
of the polymer with the matching neutral mass
Parameters
----------
mz : float
The reference m/z to calculate the neutral mass to interpolate from
charge : int, optional
The reference charge state to calculate the neutral mass. Defaults to 1
charge_carrier : float, optional
The mass of the charge carrier. Defaults to the mass of a proton.
Returns
-------
Mapping
The interpolated composition for the calculated neutral mass,
rounded to the nearest integer and hydrogen corrected.
References
----------
Senko, M. W., Beu, S. C., & McLafferty, F. W. (1995). Determination of monoisotopic masses and ion populations
for large biomolecules from resolved isotopic distributions. Journal of the American Society for Mass
Spectrometry, 6(4), 229–233. http://doi.org/10.1016/1044-0305(95)00017-8
"""
neutral = neutral_mass(mz, charge, charge_carrier)
scale = neutral / self.base_mass
scaled = {}
for elem, count in self.base_composition.items():
scaled[elem] = round(count * scale)
scaled_mass = calculate_mass(scaled)
delta_hydrogen = round(scaled_mass - neutral)
H = scaled["H"]
if H > delta_hydrogen:
scaled["H"] = H - delta_hydrogen
else:
scaled["H"] = 0
return scaled
def scale(self, mz, charge=1, charge_carrier=PROTON):
"""Given an m/z and a charge state, interpolate the composition
of the polymer with the matching neutral mass
Parameters
----------
mz : float
The reference m/z to calculate the neutral mass to interpolate from
charge : int, optional
The reference charge state to calculate the neutral mass. Defaults to 1
charge_carrier : float, optional
The mass of the charge carrier. Defaults to the mass of a proton.
Returns
-------
Mapping
The interpolated composition for the calculated neutral mass,
rounded to the nearest integer and hydrogen corrected.
References
----------
Senko, M. W., Beu, S. C., & McLafferty, F. W. (1995). Determination of monoisotopic masses and ion populations
for large biomolecules from resolved isotopic distributions. Journal of the American Society for Mass
Spectrometry, 6(4), 229–233. http://doi.org/10.1016/1044-0305(95)00017-8
"""
neutral = neutral_mass(mz, charge, charge_carrier)
scale = neutral / self.base_mass
scaled = {}
for elem, count in self.base_composition.items():
scaled[elem] = round(count * scale)
scaled_mass = calculate_mass(scaled)
delta_hydrogen = round(scaled_mass - neutral)
H = scaled["H"]
if H > delta_hydrogen:
scaled["H"] = H - delta_hydrogen
else:
scaled["H"] = 0
return scaled