In-silico cleavage of polypeptides using the cleaver package
18 May 2026
Abstract
This vignette describes the in-silico cleavage of polypeptides using the cleaver package.
Package
cleaver 1.51.0
1 Introduction
Most proteomics experiments need protein (peptide) separation and cleavage procedures before these molecules could be analyzed or identified by mass spectrometry or other analytical tools.
cleaver allows in-silico cleavage of polypeptide sequences to e.g. create theoretical mass spectrometry data.
The cleavage rules are taken from the ExPASy PeptideCutter tool (Gasteiger et al. 2005).
2 Simple Usage
Loading the cleaver package:
library("cleaver")Getting help and list all available cleavage rules:
help("cleave")Cleaving of Gastric juice peptide 1 (P01358) using Trypsin:
## cleave it
cleave("LAAGKVEDSD", enzym="trypsin")## $LAAGKVEDSD
## [1] "LAAGK" "VEDSD"## get the cleavage ranges
cleavageRanges("LAAGKVEDSD", enzym="trypsin")## $LAAGKVEDSD
## start end
## [1,] 1 5
## [2,] 6 10## get only cleavage sites
cleavageSites("LAAGKVEDSD", enzym="trypsin")## $LAAGKVEDSD
## [1] 5Sometimes cleavage is not perfect and the enzym miss some cleavage positions:
## miss one cleavage position
cleave("LAAGKVEDSD", enzym="trypsin", missedCleavages=1)## $LAAGKVEDSD
## [1] "LAAGKVEDSD"cleavageRanges("LAAGKVEDSD", enzym="trypsin", missedCleavages=1)## $LAAGKVEDSD
## start end
## [1,] 1 10## miss zero or one cleavage positions
cleave("LAAGKVEDSD", enzym="trypsin", missedCleavages=0:1)## $LAAGKVEDSD
## [1] "LAAGK" "VEDSD" "LAAGKVEDSD"cleavageRanges("LAAGKVEDSD", enzym="trypsin", missedCleavages=0:1)## $LAAGKVEDSD
## start end
## [1,] 1 5
## [2,] 6 10
## [3,] 1 10Combine cleaver and Biostrings (Pages et al., n.d.):
## create AAStringSet object
p <- AAStringSet(c(gaju="LAAGKVEDSD", pnm="AGEPKLDAGV"))
## cleave it
cleave(p, enzym="trypsin")## AAStringSetList of length 2
## [["gaju"]] LAAGK VEDSD
## [["pnm"]] AGEPK LDAGVcleavageRanges(p, enzym="trypsin")## IRangesList object of length 2:
## $gaju
## IRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 5 5
## [2] 6 10 5
##
## $pnm
## IRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 5 5
## [2] 6 10 5cleavageSites(p, enzym="trypsin")## $gaju
## [1] 5
##
## $pnm
## [1] 53 Insulin & Somatostatin Example
Downloading Insulin (P01308) and Somatostatin (P61278) sequences from the UniProt (The UniProt Consortium 2012) database using UniProt.ws (Carlson, n.d.).
## load UniProt.ws library
library("UniProt.ws")
## select species Homo sapiens
up <- UniProt.ws(taxId=9606)
## download sequences of Insulin/Somatostatin
s <- select(up,
keys=c("P01308", "P61278"),
columns=c("sequence"),
keytype="UniProtKB"
)
## fetch only sequences
sequences <- setNames(s$Sequence, s$Entry)
## remove whitespaces
sequences <- gsub(pattern="[[:space:]]", replacement="", x=sequences)Cleaving using Pepsin:
cleave(sequences, enzym="pepsin")## $P01308
## [1] "MA" "L" "W" "MRLLP"
## [5] "LL" "A" "WGPDPAAA" "F"
## [9] "VNQH" "CGSH" "VEA" "Y"
## [13] "VCGERG" "FF" "YTPKTRREAED" "QVGQVE"
## [17] "GGGPGAGS" "LQP" "LA" "EGS"
## [21] "QKRGIVEQCCTSICS" "Q" "EN" "CN"
##
## $P61278
## [1] "ML" "SCRL" "QCA"
## [4] "L" "AA" "SIV"
## [7] "A" "GCVTGAPSDPRL" "RQ"
## [10] "FL" "QKS" "LAAAAGKQEL"
## [13] "AK" "Y" "AE"
## [16] "SEPNQTENDA" "LEPED" "SQAAEQDEMRL"
## [19] "EL" "QRSANSNPAMAPRERKAGCKN" "FF"
## [22] "W" "KT" "FTSC"4 Isotopic Distribution Of Tryptic Digested Insulin
A common use case of in-silico cleavage is the calculation of the isotopic distribution of peptides (which were enzymatic digested in the in-vitro experimental workflow). Here BRAIN (Claesen et al. 2012; Dittwald et al. 2013) is used to calculate the isotopic distribution of cleaver’s output. (please note: it is only a toy example, e.g. the relation of intensity values between peptides isn’t correct).
## load BRAIN library
library("BRAIN")
## cleave insulin
cleavedInsulin <- cleave(sequences[1], enzym="trypsin")[[1]]
## create empty plot area
plot(NA, xlim=c(150, 4300), ylim=c(0, 1),
xlab="mass", ylab="relative intensity",
main="tryptic digested insulin - isotopic distribution")
## loop through peptides
for (i in seq(along=cleavedInsulin)) {
## count C, H, N, O, S atoms in current peptide
atoms <- BRAIN::getAtomsFromSeq(cleavedInsulin[[i]])
## calculate isotopic distribution
d <- useBRAIN(atoms)
## draw peaks
lines(d$masses, d$isoDistr, type="h", col=2)
}
5 Session Information
## R version 4.6.0 Patched (2026-05-01 r89994)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BRAIN_1.59.0 lattice_0.22-9 PolynomF_2.0-8
## [4] UniProt.ws_2.53.0 cleaver_1.51.0 Biostrings_2.81.1
## [7] Seqinfo_1.3.0 XVector_0.53.0 IRanges_2.47.1
## [10] S4Vectors_0.51.2 BiocGenerics_0.59.2 generics_0.1.4
## [13] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.53.0 xfun_0.57 bslib_0.11.0
## [4] httr2_1.2.2 Biobase_2.73.1 vctrs_0.7.3
## [7] rjsoncons_1.3.2 tools_4.6.0 curl_7.1.0
## [10] tibble_3.3.1 AnnotationDbi_1.75.0 RSQLite_3.52.0
## [13] blob_1.3.0 pkgconfig_2.0.3 BiocBaseUtils_1.15.1
## [16] dbplyr_2.5.2 lifecycle_1.0.5 compiler_4.6.0
## [19] progress_1.2.3 tinytex_0.59 htmltools_0.5.9
## [22] sass_0.4.10 yaml_2.3.12 pillar_1.11.1
## [25] crayon_1.5.3 jquerylib_0.1.4 cachem_1.1.0
## [28] magick_2.9.1 tidyselect_1.2.1 digest_0.6.39
## [31] dplyr_1.2.1 bookdown_0.46 fastmap_1.2.0
## [34] grid_4.6.0 cli_3.6.6 magrittr_2.0.5
## [37] prettyunits_1.2.0 filelock_1.0.3 rappdirs_0.3.4
## [40] bit64_4.8.0 rmarkdown_2.31 httr_1.4.8
## [43] bit_4.6.0 otel_0.2.0 png_0.1-9
## [46] hms_1.1.4 memoise_2.0.1 evaluate_1.0.5
## [49] knitr_1.51 BiocFileCache_3.3.0 rlang_1.2.0
## [52] Rcpp_1.1.1-1.1 glue_1.8.1 DBI_1.3.0
## [55] BiocManager_1.30.27 jsonlite_2.0.0 R6_2.6.1References
Carlson, Marc. n.d. UniProt.ws: R Interface to UniProt Web Services.
Claesen, Jürgen, Piotr Dittwald, Tomasz Burzykowski, and Dirk Valkenborg. 2012. “An Efficient Method to Calculate the Aggregated Isotopic Distribution and Exact Center-Masses.” Journal of The American Society for Mass Spectrometry 23 (4): 753–63.
Dittwald, Piotr, Jürgen Claesen, Tomasz Burzykowski, Dirk Valkenborg, and Anna Gambin. 2013. “BRAIN: A Universal Tool for High-Throughput Calculations of the Isotopic Distribution for Mass Spectrometry.” Analytical Chemistry 85 (4): 1991–94.
Gasteiger, Elisabeth, Christine Hoogland, Alexandre Gattiker, et al. 2005. “Protein Identification and Analysis Tools on the ExPASy Server.” In The Proteomics Protocols Handbook, edited by John M. Walker. Humana Press. https://doi.org/10.1385/1-59259-890-0:571.
Pages, H., P. Aboyoun, R. Gentleman, and S. DebRoy. n.d. Biostrings: String Objects Representing Biological Sequences, and Matching Algorithms.
The UniProt Consortium. 2012. “Reorganizing the Protein Space at the Universal Protein Resource (UniProt).” Nucleic Acids Research 40 (D1): D71–75. https://doi.org/10.1093/nar/gkr981.