# Contents

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

library(NestLink)
stopifnot(require(specL))

# 1 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))

# 2 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

# 3 Compose Peptides

## 3.1 GPGXXXXXXXX(VR|VSR|VFGIR|VSGER) peptide

replicate(10, compose_GPGx8cTerm(pool=aa_pool_x8))
##  [1] "GPGPNTGYQGYVFR"   "GPGVVYQTGYGVSR"   "GPGDTPNVTVTVFR"
##  [7] "GPGAVNGAVQNVSR"   "GPGQAPQPPANVSGER" "GPGNPVNTQDNVSR"
## [10] "GPGDAVTDPNTVSR"

## 3.2 GPYYXXXXXXYYR peptide

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

# 4 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')

# 5 Peptide mass

## 5.1 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$peptide), function(x){parentIonMass(x)})) ## 5.2 Draw parent ion mass histogram (pim.min <- min(pim.GPGx8cTerm, pim.GPx10R)) ## [1] 1037.512 (pim.max <- max(pim.GPGx8cTerm, pim.GPx10R)) ## [1] 1890.877 (pim.breaks <- seq(round(pim.min - 1) , round(pim.max + 1) , length=75)) ## [1] 1037.000 1048.554 1060.108 1071.662 1083.216 1094.770 1106.324 1117.878 ## [9] 1129.432 1140.986 1152.541 1164.095 1175.649 1187.203 1198.757 1210.311 ## [17] 1221.865 1233.419 1244.973 1256.527 1268.081 1279.635 1291.189 1302.743 ## [25] 1314.297 1325.851 1337.405 1348.959 1360.514 1372.068 1383.622 1395.176 ## [33] 1406.730 1418.284 1429.838 1441.392 1452.946 1464.500 1476.054 1487.608 ## [41] 1499.162 1510.716 1522.270 1533.824 1545.378 1556.932 1568.486 1580.041 ## [49] 1591.595 1603.149 1614.703 1626.257 1637.811 1649.365 1660.919 1672.473 ## [57] 1684.027 1695.581 1707.135 1718.689 1730.243 1741.797 1753.351 1764.905 ## [65] 1776.459 1788.014 1799.568 1811.122 1822.676 1834.230 1845.784 1857.338 ## [73] 1868.892 1880.446 1892.000 hist(pim.GPGx8cTerm, breaks=pim.breaks, probability = TRUE, col='#1111AAAA', xlab='peptide mass [Dalton]', ylim=c(0, 0.006)) hist(pim.GPx10R, breaks=pim.breaks, probability = TRUE, add=TRUE, col='#11AA1188') abline(v=pim.iRT, col='grey') legend("topleft", c('GPGx8cTerm', 'GPx10R', 'iRT'), fill=c('#1111AAAA', '#11AA1133', 'grey')) # 6 Hydrophobicity ## 6.1 Compute Hydrophobicity value using SSRC the SSRC model, see Krokhin et al. (2004), is implemented as ssrc function in protViz. For a sanity check we apply the ssrc function to a real world LC-MS run peptideStd consits of a digest of the FETUIN_BOVINE protein (400 amol) shipped with specL Panse et al. (2015). library(specL) ssrc <- sapply(peptideStd, function(x){ssrc(x$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)

## 6.2 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'))

# 7 Quality Control (QC)

## 7.1 QC of composed peptides

### 7.1.1 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

### 7.1.2 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

## 7.2 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

## 7.3 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
## 9541 3606 1607  792  427  204  104   50   34   20    6    5    6    2    1
##   17
##    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.GPx10R, main='GPx10R')

# 8 Session info

Here is the output of the sessionInfo() commmand.

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
##
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets
## [8] methods   base
##
## other attached packages:
##  [1] specL_1.19.0                seqinr_3.4-5
##  [3] RSQLite_2.1.1               DBI_1.0.0
##  [5] knitr_1.22                  scales_1.0.0
## [11] SummarizedExperiment_1.15.0 DelayedArray_0.11.0
## [13] matrixStats_0.54.0          Biobase_2.45.0
## [15] Rsamtools_2.1.2             GenomicRanges_1.37.0
## [17] GenomeInfoDb_1.21.0         BiocParallel_1.19.0
## [19] protViz_0.4.0               gplots_3.0.1.1
## [21] Biostrings_2.53.0           XVector_0.25.0
## [23] IRanges_2.19.0              S4Vectors_0.23.0
## [25] ExperimentHub_1.11.1        AnnotationHub_2.17.2
## [27] BiocFileCache_1.9.0         dbplyr_1.4.0
## [29] BiocGenerics_0.31.0         BiocStyle_2.13.0
##
## loaded via a namespace (and not attached):
##  [1] httr_1.4.0                    bit64_0.9-7
##  [3] gtools_3.8.1                  shiny_1.3.2
##  [5] assertthat_0.2.1              interactiveDisplayBase_1.23.0
##  [7] highr_0.8                     BiocManager_1.30.4
##  [9] latticeExtra_0.6-28           blob_1.1.1
## [11] GenomeInfoDbData_1.2.1        yaml_2.2.0
## [13] pillar_1.3.1                  lattice_0.20-38
## [15] glue_1.3.1                    digest_0.6.18
## [17] RColorBrewer_1.1-2            promises_1.0.1
## [19] colorspace_1.4-1              plyr_1.8.4
## [21] htmltools_0.3.6               httpuv_1.5.1
## [23] Matrix_1.2-17                 pkgconfig_2.0.2
## [25] bookdown_0.9                  zlibbioc_1.31.0
## [27] purrr_0.3.2                   xtable_1.8-4
## [29] gdata_2.18.0                  later_0.8.0
## [31] tibble_2.1.1                  withr_2.1.2
## [33] lazyeval_0.2.2                magrittr_1.5
## [35] crayon_1.3.4                  mime_0.6
## [37] memoise_1.1.0                 evaluate_0.13
## [39] MASS_7.3-51.4                 hwriter_1.3.2
## [41] tools_3.6.0                   stringr_1.4.0
## [43] munsell_0.5.0                 AnnotationDbi_1.47.0
## [47] caTools_1.17.1.2              rlang_0.3.4
## [49] grid_3.6.0                    RCurl_1.95-4.12
## [51] rappdirs_0.3.1                labeling_0.3
## [53] bitops_1.0-6                  rmarkdown_1.12
## [55] gtable_0.3.0                  codetools_0.2-16
## [57] curl_3.3                      R6_2.4.0
## [59] dplyr_0.8.0.1                 bit_1.1-14
## [61] KernSmooth_2.23-15            stringi_1.4.3
## [63] Rcpp_1.0.1                    tidyselect_0.2.5
## [65] xfun_0.6

# References

Escher, C., L. Reiter, B. MacLean, R. Ossola, F. Herzog, J. Chilton, M. J. MacCoss, and O. Rinner. 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, W. Ens, K. G. Standing, R. C. Beavis, and J. A. Wilkins. 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.