fragquaxi

Overview

fragquaxi allows you to obtain fractional abundances of glycoforms (and proteoforms in general) from mass spectrometric (MS) data by quantification via XIC (extracted ion current) integration.

Installation

fragquaxi is currently only available from GitHub.

remotes::install_github("cdl-biosimilars/fragquaxi")

Usage

Load mass spectrometric data.

library(fragquaxi)
library(tibble)
library(dplyr)
library(tidyr)
library(ggplot2)

ms_data <- mzR::openMSfile(
  system.file("extdata", "mzml", "mab1.mzML", package = "fragquaxi")
)

Define proteins.

mab_sequence <- system.file(
  "extdata", "mab_sequence.fasta",
  package = "fragquaxi"
)

proteins <- define_proteins(
  mab = mab_sequence,
  .disulfides = 16
)

Define PTM compositions.

modcoms <- tribble(
  ~modcom_name, ~Hex, ~HexNAc, ~Fuc,
  "G0F/G0",        6,       8,    1,
  "G0F/G0F",       6,       8,    2,
  "G0F/G1F",       7,       8,    2,
  "G1F/G1F",       8,       8,    2,
  "G1F/G2F",       9,       8,    2,
  "G2F/G2F",      10,       8,    2,
) %>% 
  define_ptm_compositions()

Assemble proteoforms and calculate mass-to-charge ratios of proteoform ions in charge states 33+ to 40+

pfm_ions <-
  assemble_proteoforms(proteins, modcoms) %>% 
  ionize(charge_states = 33L:40L)

pfm_ions
#> # A tibble: 48 x 8
#>    protein_name modcom_name formula                         mass     z    mz
#>    <chr>        <chr>       <mol>                          <dbl> <int> <dbl>
#>  1 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    33 4484.
#>  2 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    34 4352.
#>  3 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    35 4228.
#>  4 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    36 4111.
#>  5 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    37 3999.
#>  6 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    38 3894.
#>  7 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    39 3794.
#>  8 mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    40 3700.
#>  9 mab          G0F/G0F     C6576 H10134 N1714 O2092 S44 148088.    33 4489.
#> 10 mab          G0F/G0F     C6576 H10134 N1714 O2092 S44 148088.    34 4357.
#> # … with 38 more rows, and 2 more variables: mz_min <dbl>, mz_max <dbl>

Plot these ions (here, only charge state 36+ of scans 126 to 136).

plot_ions(
  ms_data,
  ions = pfm_ions,
  scans = 126:136,
  xlim = c(4100, 4150)
)

Quantify these ions via XIC integration.

abundances <- quantify_ions(
  ms_data,
  pfm_ions,
  rt_limits = c(300, 350)
)

abundances
#> ℹ Abundances of 48 ions quantified in 352 mass spectra using 1 retention time window.
#> 
#> ── Parameters ──
#> 
#> MS data file:
#> '/home/wolfgang/R/x86_64-pc-linux-gnu-library/4.0/fragquaxi/extdata/mzml/mab1.mzML'
#> 
#> Ions:
#> # A tibble: 48 x 9
#>   ion_id protein_name modcom_name formula                         mass     z
#>   <chr>  <chr>        <chr>       <mol>                          <dbl> <int>
#> 1 id_1   mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    33
#> 2 id_2   mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    34
#> 3 id_3   mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    35
#> 4 id_4   mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    36
#> 5 id_5   mab          G0F/G0      C6570 H10124 N1714 O2088 S44 147942.    37
#> # … with 43 more rows, and 3 more variables: mz <dbl>, mz_min <dbl>,
#> #   mz_max <dbl>
#> 
#> Retention time limits:
#> # A tibble: 1 x 3
#>   rt_min rt_max scans     
#>    <dbl>  <dbl> <list>    
#> 1    300    350 <int [25]>
#> 
#> ── Results ──
#> 
#> # A tibble: 1 x 50
#>   rt_min rt_max    id_1    id_2   id_3   id_4   id_5   id_6   id_7   id_8   id_9
#>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1    300    350 153744. 204512. 2.57e5 3.34e5 1.84e5 1.77e5 1.21e5 1.14e5 2.90e6
#> # … with 39 more variables: id_10 <dbl>, id_11 <dbl>, id_12 <dbl>, id_13 <dbl>,
#> #   id_14 <dbl>, …

Plot abundances.

abundances %>%
  as_tibble() %>% 
  unnest(abundance_data) %>% 
  group_by(modcom_name) %>%
  summarise(abundance = sum(abundance)) %>% 
  mutate(frac_ab = abundance / sum(abundance) * 100) %>% 
  ggplot(aes(modcom_name, frac_ab)) +
  geom_col() +
  xlab("") +
  ylab("fractional abundance (%)")