Isotopic Pattern Generation

The ms_deisotope.averagine module contains several pre-created isotopic models whose name is derived from the original peptide averagine [Senko], as well as isotopic models for other common molecular classes. Isotopic patterns are generated from a chemical composition using brainpy, which is an implementation of the Baffling Recursive Algorithm for Isotopic distributioN calculations [Dittwald2014].

import ms_deisotope

# Senko peptide averagine
isotopic_pattern = ms_deisotope.peptide.isotopic_cluster(966.12, 2)
for peak in isotopic_pattern:
    print(peak.mz, peak.intensity)
ms_deisotope includes several pre-defined averagines (or “averagoses” as may be more appropriate):

  1. Senko’s peptide - ms_deisotope.peptide

  2. Native N- and O-glycan - ms_deisotope.glycan

  3. Permethylated glycan - ms_deisotope.permethylated_glycan

  4. Glycopeptide - ms_deisotope.glycopeptide

  5. Sulfated Glycosaminoglycan - ms_deisotope.heparan_sulfate

  6. Unsulfated Glycosaminoglycan - ms_deisotope.heparin

class ms_deisotope.averagine.Averagine(base_composition)

An isotopic model which can be used to interpolate the composition of a class of molecule given an average monomer composition and a theoretical polymer mass

Implements the Mapping interface.

base_composition

A mapping from element symbol to average count (float) of that element for the average monomer

Type

Mapping

base_mass

The base mass of the average monomer. Calculated from base_composition

Type

float

isotopic_cluster(self, double mz, int charge=1, double charge_carrier=PROTON, double truncate_after=0.95, double ignore_below=0.0) → TheoreticalIsotopicPattern

Generate a theoretical isotopic pattern for the given m/z and charge state, thresholded by theoretical peak height and density.

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.

  • truncate_after (float, optional) – The percentage of the signal in the theoretical isotopic pattern to include. Defaults to 0.95, including the first 95% of the signal in the generated pattern

  • ignore_below (float, optional) – Omit theoretical peaks whose intensity is below this number. Defaults to 0.0

Returns

The generated and thresholded pattern

Return type

TheoreticalIsotopicPattern

scale(self, double mz, int charge=1, double charge_carrier=PROTON) → dict

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

The interpolated composition for the calculated neutral mass, rounded to the nearest integer and hydrogen corrected.

Return type

Mapping

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

class ms_deisotope.averagine.TheoreticalIsotopicPattern(peaklist, origin, offset=None)

Represent a theoretical isotopic peak list

peaklist

The theoretical isotopic pattern peak list

Type

list of brainpy.TheoreticalPeak

origin

The monoisotopic peak’s m/z

Type

float

ignore_below(self, double ignore_below=0.0) → TheoreticalIsotopicPattern

Discards peaks whose intensity is below ignore_below.

After discarding peaks, the pattern will be renormalized to sum to 1.0

Parameters

ignore_below (float, optional) – The threshold below which peaks will be discarded

Returns

self

Return type

TheoreticalIsotopicPattern

scale(self, list experimental_distribution, str method='sum') → TheoreticalIsotopicPattern

Scales self’s intensity to match the intensity distribution of the experimental isotopic pattern in experimental_distribution.

The method argument must be one of:

“sum”:

Scale each peak of the theoretical distribution by the sum of the intensity in the experimental distribution such that the sums of their intensities are equal.

“max”:

Select the most abundant peak in the theoretical distribution \(t_i\), find it’s match in the experimental distribution \(e_i\), find the scaling factor \(\alpha = \frac{e_i}{t_i}\) which will make \(e_i == t_i\) and scale all peaks in self by \(\alpha\)

“basepeak”:

As in “max”, except the most abundant peak index is taken from the experimental distribution

“top3”:

The as in “max”, but the scaling factor is the mean of the scale factors for the top three most abundant theoretical peaks.

Parameters

  • experimental_distribution (list) – The experimental peaks matched

  • method (str, optional) – The scaling method to use. Defaults to "sum"

Returns

self

Return type

TheoreticalIsotopicPattern

shift(self, double mz) → TheoreticalIsotopicPattern

Shift all the m/z of peaks in the isotopic pattern by offset m/z.

This will update origin to reflect the new starting monoisotopic m/z.

Parameters

offset (float) – The amount to shift each peak in the pattern by in m/z

Returns

self

Return type

TheoreticalIsotopicPattern

truncate_after(self, double truncate_after=0.95) → TheoreticalIsotopicPattern

Drops peaks from the end of the isotopic pattern which make up the last 1 - truncate_after percent of the isotopic pattern.

After truncation, the pattern is renormalized to sum to 1

Parameters

truncate_after (float, optional) – The percentage of the isotopic pattern signal to retain. Defaults to 0.95.

Returns

self

Return type

TheoreticalIsotopicPattern

class ms_deisotope.averagine.AveragineCache(averagine, backend=None, double cache_truncation=1.)

A wrapper around a Averagine instance which will cache isotopic patterns produced for new (m/z, charge) pairs and reuses it for nearby m/z values

averagine

The averagine to use to generate new isotopic patterns

Type

Averagine

cache_truncation

Number of decimal places to round off the m/z for caching purposes

Type

float

isotopic_cluster(self, double mz, int charge=1, double charge_carrier=PROTON, double truncate_after=0.95, double ignore_below=0.0) → TheoreticalIsotopicPattern

Generate a theoretical isotopic pattern for the given m/z and charge state, thresholded by theoretical peak height and density.

Mimics Averagine.isotopic_cluster() but uses the object’s cache through has_mz_charge_pair().

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.

  • truncate_after (float, optional) – The percentage of the signal in the theoretical isotopic pattern to include. Defaults to 0.95, including the first 95% of the signal in the generated pattern

  • ignore_below (float, optional) – Omit theoretical peaks whose intensity is below this number. Defaults to 0.0

Returns

The generated and thresholded pattern

Return type

TheoreticalIsotopicPattern

Senko

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

Dittwald2014

Dittwald, P., & Valkenborg, D. (2014). BRAIN 2.0: time and memory complexity improvements in the algorithm for calculating the isotope distribution. Journal of the American Society for Mass Spectrometry, 25(4), 588–94. https://doi.org/10.1007/s13361-013-0796-5