Scoring Spectra with HyperScore

In the chapter on spectrum alignment we showed how to determine matching peaks between theoretical and experimental spectra. For many use cases we might actually not be interested in obtaining the list of matched peaks but would like to have a simple, single score that indicates how “well” the two spectra matched. The HyperScore is a method to assign a score to peptide-spectrum matches.


HyperScore computes the (ln transformed) HyperScore of theoretical spectrum, calculated from a peptide/oligonucleotide sequence, with an experimental spectrum, loaded from an mzML file.

  1. the dot product of peak intensities between matching peaks in experimental and theoretical spectrum is calculated

  2. the HyperScore is calculated from the dot product by multiplying by factorials of matching b- and y-ions

from urllib.request import urlretrieve
from pyopenms import *

gh = ""
urlretrieve(gh + "/src/data/SimpleSearchEngine_1.mzML", "searchfile.mzML")

Generate a Theoretical Spectrum

We now use the TheoreticalSpectrumGenerator to generate a theoretical spectrum for the sequence we are interested in, RPGADSDIGGFGGLFDLAQAGFR, and compare the peaks to a spectra from our file.

tsg = TheoreticalSpectrumGenerator()
thspec = MSSpectrum()
p = Param()
p.setValue("add_metainfo", "true")
peptide = AASequence.fromString("RPGADSDIGGFGGLFDLAQAGFR")
tsg.getSpectrum(thspec, peptide, 1, 1)
# Iterate over annotated ions and their masses
for ion, peak in zip(thspec.getStringDataArrays()[0], thspec):
    print(ion, peak.getMZ())

e = MSExperiment()
MzMLFile().load("searchfile.mzML", e)
spectrum_of_interest = e[2]
print("Spectrum native id", spectrum_of_interest.getNativeID())
mz, i = spectrum_of_interest.get_peaks()
peaks = [(mz, i) for mz, i in zip(mz, i) if i > 1500 and mz > 300]
for peak in peaks:
    print(peak[0], "mz", peak[1], "int")

Comparing the spectrum and the experimental spectrum for RPGADSDIGGFGGLFDLAQAGFR we can easily see that the most abundant ions in the spectrum are \(\ce{y8}\) (\(877.452\) m/z), \(\ce{b10}\) (\(926.432\)), \(\ce{y9}\) (\(1024.522\) m/z) and \(\ce{b13}\) (\(1187.544\) m/z).

Getting a Score

We now run HyperScore to compute the similarity of the theoretical spectrum and the experimental spectrum and print the result

hscore = HyperScore()
fragment_mass_tolerance = 5.0
is_tol_in_ppm = True
result = hscore.compute(
    fragment_mass_tolerance, is_tol_in_ppm, spectrum_of_interest, thspec
If we didn’t know ahead of time which spectrum was a match we can loop through all the spectra from our file,

calculate scores for all of them, and print the result:

for f in e:
    score = hscore.compute(fragment_mass_tolerance, is_tol_in_ppm, f, thspec)
    print(f.getNativeID() + ":" + str(score))