Derive Peptide Flycodes by Conducting Random Experiment

The following content is descibed in more detail in Egloff et al. (2018) (under review NMETH-A35040).

library(NestLink)
stopifnot(require(specL))

Input Pool Frequency

aa_pool_x8 <- c(rep('A', 12), rep('S', 0), rep('T', 12), rep('N', 12),
    rep('Q', 12), rep('D', 8),  rep('E', 0), rep('V', 12), rep('L', 0),
    rep('F', 0), rep('Y', 8), rep('W', 0), rep('G', 12), rep('P', 12))

aa_pool_1_2_9_10 <- c(rep('A', 8), rep('S', 7), rep('T', 7), rep('N', 6),
    rep('Q', 6), rep('D', 8), rep('E', 8), rep('V', 9), rep('L', 6),
    rep('F', 5), rep('Y', 9), rep('W', 6),  rep('G', 15), rep('P', 0))

aa_pool_3_8 <- c(rep('A', 5), rep('S', 4), rep('T', 5), rep('N', 2),
    rep('Q', 2), rep('D', 8), rep('E', 8), rep('V', 7), rep('L', 5),
    rep('F', 4), rep('Y', 6), rep('W', 4),  rep('G', 12), rep('P', 28))

Sanity Check

table(aa_pool_x8)
## aa_pool_x8
##  A  D  G  N  P  Q  T  V  Y 
## 12  8 12 12 12 12 12 12  8
length(aa_pool_x8)
## [1] 100
table(aa_pool_1_2_9_10)
## aa_pool_1_2_9_10
##  A  D  E  F  G  L  N  Q  S  T  V  W  Y 
##  8  8  8  5 15  6  6  6  7  7  9  6  9
length(aa_pool_1_2_9_10)
## [1] 100
table(aa_pool_3_8)
## aa_pool_3_8
##  A  D  E  F  G  L  N  P  Q  S  T  V  W  Y 
##  5  8  8  4 12  5  2 28  2  4  5  7  4  6
length(aa_pool_3_8)
## [1] 100

Compose Peptides

GPGXXXXXXXX(VR|VSR|VFGIR|VSGER) peptide

replicate(10, compose_GPGx8cTerm(pool=aa_pool_x8))
##  [1] "GPGGANDPYDYVSR"   "GPGPPAPYAATVSR"   "GPGGTPAPVDTVFR"   "GPGTTDYTGDVVFR"  
##  [5] "GPGVPQGADAAVSR"   "GPGPYAYDNQPVFR"   "GPGGANTVPYVVSGER" "GPGVAPADAPNVFR"  
##  [9] "GPGVAAYYAYNVSGER" "GPGADGTVGNQVSGER"

GPYYXXXXXXYYR peptide

compose_GPx10R(aa_pool_1_2_9_10, aa_pool_3_8)
## [1] "GPVAWPDTPLNVR"

Generate peptides

set.seed(2)
(sample.size <- 3E+04)
## [1] 30000
peptides.GPGx8cTerm <- replicate(sample.size, compose_GPGx8cTerm(pool=aa_pool_x8))
peptides.GPx10R <- replicate(sample.size, compose_GPx10R(aa_pool_1_2_9_10, aa_pool_3_8))
# write.table(peptides.GPGx8cTerm, file='/tmp/pp.txt')

Peptide mass

Compute peptide mass

library(protViz)
(smp.peptide <- compose_GPGx8cTerm(aa_pool_x8))
## [1] "GPGPDDTDTYGVFR"
parentIonMass(smp.peptide)
## [1] 1496.665
pim.GPGx8cTerm <- unlist(lapply(peptides.GPGx8cTerm, function(x){parentIonMass(x)}))
pim.GPx10R <- unlist(lapply(peptides.GPx10R, function(x){parentIonMass(x)}))
pim.iRT <-  unlist(lapply(as.character(iRTpeptides$peptide), function(x){parentIonMass(x)}))

Draw parent ion mass histogram

(pim.min <- min(pim.GPGx8cTerm, pim.GPx10R))
## [1] 1037.512
(pim.max <- max(pim.GPGx8cTerm, pim.GPx10R))
## [1] 1890.877
(pim.breaks <- seq(round(pim.min - 1) , round(pim.max + 1) , length=75))
##  [1] 1037.000 1048.554 1060.108 1071.662 1083.216 1094.770 1106.324 1117.878
##  [9] 1129.432 1140.986 1152.541 1164.095 1175.649 1187.203 1198.757 1210.311
## [17] 1221.865 1233.419 1244.973 1256.527 1268.081 1279.635 1291.189 1302.743
## [25] 1314.297 1325.851 1337.405 1348.959 1360.514 1372.068 1383.622 1395.176
## [33] 1406.730 1418.284 1429.838 1441.392 1452.946 1464.500 1476.054 1487.608
## [41] 1499.162 1510.716 1522.270 1533.824 1545.378 1556.932 1568.486 1580.041
## [49] 1591.595 1603.149 1614.703 1626.257 1637.811 1649.365 1660.919 1672.473
## [57] 1684.027 1695.581 1707.135 1718.689 1730.243 1741.797 1753.351 1764.905
## [65] 1776.459 1788.014 1799.568 1811.122 1822.676 1834.230 1845.784 1857.338
## [73] 1868.892 1880.446 1892.000
hist(pim.GPGx8cTerm, breaks=pim.breaks, probability = TRUE, 
     col='#1111AAAA', xlab='peptide mass [Dalton]', ylim=c(0, 0.006))
hist(pim.GPx10R, breaks=pim.breaks,
     probability = TRUE, add=TRUE, col='#11AA1188')
abline(v=pim.iRT, col='grey')
legend("topleft", c('GPGx8cTerm', 'GPx10R', 'iRT'), 
     fill=c('#1111AAAA', '#11AA1133', 'grey'))

Hydrophobicity

Compute Hydrophobicity value using SSRC

the SSRC model, see Krokhin et al. (2004), is implemented as ssrc function in protViz.

For a sanity check we apply the ssrc function to a real world LC-MS run peptideStd consits of a digest of the FETUIN_BOVINE protein (400 amol) shipped with specL Panse et al. (2015).

library(specL)
ssrc <- sapply(peptideStd, function(x){ssrc(x$peptideSequence)})
rt <- unlist(lapply(peptideStd, function(x){x$rt}))
plot(ssrc, rt); abline(ssrc.lm <- lm(rt ~ ssrc), col='red'); 
legend("topleft", paste("spearman", round(cor(ssrc, rt, method='spearman'),2)))

here we apply ssrc to the simulated flycodes and iRT peptides Escher et al. (2012).

hyd.GPGx8cTerm <- ssrc(peptides.GPGx8cTerm)
hyd.GPx10R <- ssrc(peptides.GPx10R)
hyd.iRT <- ssrc(as.character(iRTpeptides$peptide))

(hyd.min <- min(hyd.GPGx8cTerm, hyd.GPx10R))
## [1] -7.63055
(hyd.max <- max(hyd.GPGx8cTerm, hyd.GPx10R))
## [1] 65.12112
hyd.breaks <- seq(round(hyd.min - 1) , round(hyd.max + 1) , length=75)

Draw hydrophobicity histogram

hist(hyd.GPGx8cTerm, breaks = hyd.breaks, probability = TRUE, 
     col='#1111AAAA', xlab='hydrophobicity', 
     ylim=c(0, 0.06),
     main='Histogram')
hist(hyd.GPx10R, breaks = hyd.breaks, probability = TRUE, add=TRUE, col='#11AA1188')
  abline(v=hyd.iRT, col='grey')
legend("topleft", c('GPGx8cTerm', 'GPx10R', 'iRT'),  fill=c('#1111AAAA', '#11AA1133', 'grey'))

Quality Control (QC)

QC of composed peptides

Input

round(table(aa_pool_x8)/length(aa_pool_x8), 2)
## aa_pool_x8
##    A    D    G    N    P    Q    T    V    Y 
## 0.12 0.08 0.12 0.12 0.12 0.12 0.12 0.12 0.08

Output

peptide2aa <- function(seq, from=4, to=4+8){
  unlist(lapply(seq, function(x){strsplit(substr(x, from, to), '')}))
}
peptides.GPGx8cTerm.aa <- peptide2aa(peptides.GPGx8cTerm)
round(table(peptides.GPGx8cTerm.aa)/length(peptides.GPGx8cTerm.aa), 2)
## peptides.GPGx8cTerm.aa
##    A    D    G    N    P    Q    T    V    Y 
## 0.11 0.07 0.11 0.11 0.11 0.11 0.11 0.22 0.07
peptides.GPx10R.aa <- peptide2aa(peptides.GPx10R, from=3, to=12)
round(table(peptides.GPx10R.aa)/length(peptides.GPx10R.aa), 2)
## peptides.GPx10R.aa
##    A    D    E    F    G    L    N    P    Q    S    T    V    W    Y 
## 0.06 0.08 0.08 0.04 0.13 0.05 0.04 0.17 0.04 0.05 0.06 0.08 0.05 0.07

Count GP patterns

sample.size 
## [1] 30000
length(grep('^GP(.*)GP(.*)R$', peptides.GPGx8cTerm))
## [1] 6319
length(grep('^GP(.*)GP(.*)R$', peptides.GPx10R))
## [1] 5959

Compute AA frequency table

count the peptides having the same AA composition

sample.size 
## [1] 30000
table(table(tt<-unlist(lapply(peptides.GPGx8cTerm, 
  function(x){paste(sort(unlist(strsplit(x, ''))), collapse='')}))))
## 
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14   16   17 
## 9541 3606 1607  792  427  204  104   50   34   20    6    5    6    2    1    1
# write.table(tt, file='GPGx8cTerm.txt')
table(table(unlist(lapply(peptides.GPx10R, 
  function(x){paste(sort(unlist(strsplit(x, ''))), collapse='')}))))
## 
##     1     2     3     4     5 
## 24844  2104   265    32     5

the NestLink function plot_in_silico_LCMS_map graphs the LC-MS maps.

par(mfrow=c(2, 2))
h <- NestLink:::.plot_in_silico_LCMS_map(peptides.GPGx8cTerm, main='GPGx8cTerm')
h <- NestLink:::.plot_in_silico_LCMS_map(peptides.GPx10R, main='GPx10R')

Session info

Here is the output of the sessionInfo() commmand.

## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] specL_1.46.0                seqinr_4.2-44              
##  [3] RSQLite_3.53.3              DBI_1.3.0                  
##  [5] knitr_1.52                  scales_1.4.0               
##  [7] ggplot2_4.0.3               NestLink_1.28.0            
##  [9] ShortRead_1.70.0            GenomicAlignments_1.48.0   
## [11] SummarizedExperiment_1.42.0 Biobase_2.72.0             
## [13] MatrixGenerics_1.24.0       matrixStats_1.5.0          
## [15] Rsamtools_2.28.0            GenomicRanges_1.64.0       
## [17] BiocParallel_1.46.0         protViz_0.7.9              
## [19] gplots_3.3.0                Biostrings_2.80.2          
## [21] Seqinfo_1.2.0               XVector_0.52.0             
## [23] IRanges_2.46.0              S4Vectors_0.50.3           
## [25] ExperimentHub_3.2.2         AnnotationHub_4.2.2        
## [27] BiocFileCache_3.2.0         dbplyr_2.6.0               
## [29] BiocGenerics_0.58.1         generics_0.1.4             
## [31] BiocStyle_2.40.0           
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.1-0         deldir_2.0-4         httr2_1.3.0         
##  [4] rlang_1.3.0          magrittr_2.0.5       ade4_1.7-24         
##  [7] otel_0.2.0           compiler_4.6.1       mgcv_1.9-4          
## [10] png_0.1-9            vctrs_0.7.3          pwalign_1.8.0       
## [13] pkgconfig_2.0.3      crayon_1.5.3         fastmap_1.2.0       
## [16] labeling_0.4.3       caTools_1.18.4       rmarkdown_2.32      
## [19] purrr_1.2.2          bit_4.6.0            xfun_0.61           
## [22] cachem_1.1.0         cigarillo_1.2.1      jsonlite_2.0.0      
## [25] blob_1.3.0           DelayedArray_0.38.2  jpeg_0.1-11         
## [28] parallel_4.6.1       R6_2.6.1             bslib_0.12.0        
## [31] RColorBrewer_1.1-3   jquerylib_0.1.4      Rcpp_1.1.2          
## [34] Matrix_1.7-6         splines_4.6.1        tidyselect_1.2.1    
## [37] abind_1.4-8          yaml_2.3.12          codetools_0.2-20    
## [40] hwriter_1.3.2.1      curl_8.0.0           lattice_0.23-1      
## [43] tibble_3.3.1         withr_3.0.3          KEGGREST_1.52.2     
## [46] S7_0.2.2             evaluate_1.0.5       pillar_1.11.1       
## [49] BiocManager_1.30.27  filelock_1.0.3       KernSmooth_2.23-27  
## [52] BiocVersion_3.23.1   gtools_3.9.5         glue_1.8.1          
## [55] maketools_1.3.2      tools_4.6.1          interp_1.1-6        
## [58] sys_3.4.3            buildtools_1.0.0     grid_4.6.1          
## [61] latticeExtra_0.6-31  AnnotationDbi_1.74.0 nlme_3.1-171        
## [64] cli_3.6.6            rappdirs_0.3.4       S4Arrays_1.12.1     
## [67] dplyr_1.2.1          gtable_0.3.6         sass_0.4.10         
## [70] digest_0.6.39        SparseArray_1.12.3   farver_2.1.2        
## [73] memoise_2.0.1        htmltools_0.5.9      lifecycle_1.0.5     
## [76] httr_1.4.9           MASS_7.3-66          bit64_4.8.6

References

Egloff, Pascal, Iwan Zimmermann, Fabian M. Arnold, et al. 2018. “Engineered Peptide Barcodes for In-Depth Analyses of Binding Protein Ensembles.” bioRxiv, ahead of print. https://doi.org/10.1101/287813.
Escher, C., L. Reiter, B. MacLean, et al. 2012. “Using iRT, a normalized retention time for more targeted measurement of peptides.” Proteomics 12 (8): 1111–21.
Krokhin, O. V., R. Craig, V. Spicer, et al. 2004. “An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS.” Mol. Cell Proteomics 3 (9): 908–19.
Panse, C., C. Trachsel, J. Grossmann, and R. Schlapbach. 2015. “specL–an R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics.” Bioinformatics 31 (13): 2228–31.