pepVet evaluates proteolytic digests before acquisition. A protein sequence and enzyme produce peptide coordinates, score components, a composite label, and the resolved scoring parameters. The tool comparison describes differences between pepVet and five other digestion tools.
Protein sequence
|
v
digest_protein() -> tibble of peptides (one row per peptide)
|
v
score_peptides() -> one-row tibble of component scores + verdict
|
v
evaluate_digest() -> convenience wrapper: digest + score + params in one call
|
v
pepvet_check() -> evaluate + print report in one interactive call
|
v
compare_digests() -> run evaluate_digest across N enzymes, rank by composite
|
v
recommend_enzyme() -> return the name of the top-ranked enzyme
|
v
batch_evaluate() -> run evaluate_digest across all proteins in a FASTA
| returns a flat tibble: one row per protein
v
summarize_batch() -> proteome-level verdict distribution and score stats
|
v
triage_proteins() -> per-protein action labels
|
v
export_peptide_list() -> export valid peptides to Skyline, generic CSV, or FASTA
|
v
digest_report() -> compact console output for any of the aboveEvery output is programmatic. digest_protein() returns a
tibble with one row per peptide. score_peptides() returns a
tibble with one row per protein. compare_digests() returns
a tibble with one row per enzyme. evaluate_digest() returns
a named list with the score tibble, the peptide tibble, and the resolved
parameters.
Every pepVet function that takes a protein sequence accepts the same input shapes. You can pass:
Biostrings::AAString or AAStringSetThe package ships fixed FASTA fixtures in inst/extdata/
for these examples.
digest_protein() cleaves a protein sequence using any
cleaver-compatible enzyme rule and returns a tibble with one row per
peptide fragment.
digest_protein(bsa_path, enzyme = "trypsin", missed_cleavages = 1L)
#> # A tibble: 157 × 6
#> protein_id peptide start end length missed_cleavages
#> <chr> <chr> <int> <int> <int> <int>
#> 1 sp|P02769|ALBU_BOVIN Albumin OS=… MK 1 2 2 0
#> 2 sp|P02769|ALBU_BOVIN Albumin OS=… MKWVTF… 1 19 19 1
#> 3 sp|P02769|ALBU_BOVIN Albumin OS=… WVTFIS… 3 19 17 0
#> 4 sp|P02769|ALBU_BOVIN Albumin OS=… WVTFIS… 3 23 21 1
#> 5 sp|P02769|ALBU_BOVIN Albumin OS=… GVFR 20 23 4 0
#> 6 sp|P02769|ALBU_BOVIN Albumin OS=… GVFRR 20 24 5 1
#> 7 sp|P02769|ALBU_BOVIN Albumin OS=… R 24 24 1 0
#> 8 sp|P02769|ALBU_BOVIN Albumin OS=… RDTHK 24 28 5 1
#> 9 sp|P02769|ALBU_BOVIN Albumin OS=… DTHK 25 28 4 0
#> 10 sp|P02769|ALBU_BOVIN Albumin OS=… DTHKSE… 25 34 10 1
#> # ℹ 147 more rowsThe output columns are:
| Column | Description |
|---|---|
protein_id |
Full FASTA header or supplied name |
peptide |
Amino acid sequence of the fragment |
start |
1-based start position in the parent protein |
end |
1-based end position (inclusive) |
length |
Peptide length in residues |
missed_cleavages |
Number of internal cleavage sites skipped |
digest_protein() expands missed cleavages explicitly and
retains exact start and end coordinates for each joined product.
# mc=0: strict cleavage only
digest_protein("AKRTPK", enzyme = "trypsin", missed_cleavages = 0L)
#> # A tibble: 3 × 6
#> protein_id peptide start end length missed_cleavages
#> <chr> <chr> <int> <int> <int> <int>
#> 1 sequence_1 AK 1 2 2 0
#> 2 sequence_1 R 3 3 1 0
#> 3 sequence_1 TPK 4 6 3 0
# mc=1: also include once-skipped joins
digest_protein("AKRTPK", enzyme = "trypsin", missed_cleavages = 1L)
#> # A tibble: 5 × 6
#> protein_id peptide start end length missed_cleavages
#> <chr> <chr> <int> <int> <int> <int>
#> 1 sequence_1 AK 1 2 2 0
#> 2 sequence_1 AKR 1 3 3 1
#> 3 sequence_1 R 3 3 1 0
#> 4 sequence_1 RTPK 3 6 4 1
#> 5 sequence_1 TPK 4 6 3 0The validation layer checks enzyme names against a registry of 40
cleaver-compatible rules, normalises case, and trims whitespace. Common
examples include trypsin, lysc,
glutamyl endopeptidase, asp-n endopeptidase,
chymotrypsin-high, and thermolysin. The input
" Trypsin " therefore resolves to trypsin.
annotate_cleavage_sites() classifies local
trypsin-family motifs using the package’s sequence rules.
annotate_cleavage_sites(bsa_path, enzyme = "trypsin")
#> # A tibble: 86 × 5
#> position residue flanking_context efficiency rule_applied
#> <int> <chr> <chr> <chr> <chr>
#> 1 2 K MKWV high default_trypsin_site
#> 2 19 R YSRGV high default_trypsin_site
#> 3 23 R VFRRD medium adjacent_basic_residues
#> 4 24 R FRRDT low acidic_p1_prime
#> 5 28 K THKSE high default_trypsin_site
#> 6 34 R AHRFK high default_trypsin_site
#> 7 36 K RFKDL low acidic_p1_prime
#> 8 44 K HFKGL high default_trypsin_site
#> 9 65 K HVKLV high default_trypsin_site
#> 10 75 K FAKTC high default_trypsin_site
#> # ℹ 76 more rowspepVet currently labels tryptic sites as:
low for proline-blocked (KP /
RP) and acidic P1’ (KD, KE,
RD, RE) motifsmedium for adjacent-basic sites such as
KK, KR, and RRhigh for other tryptic sitesThese labels are sequence-local annotations. They do not model structure, PTMs, extended subsite preferences, cleavage probability, or abundance.
score_peptides() summarises a digest tibble into
per-protein component scores and a weighted composite verdict.
digest_result <- digest_protein(bsa_path,
enzyme = "trypsin",
missed_cleavages = 1L
)
score_peptides(digest_result)
#> # A tibble: 1 × 10
#> protein_id S_length S_coverage S_count S_hydro S_charge composite_score
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sp|P02769|ALBU_B… 0.688 0.997 1 0.769 0.778 0.885
#> # ℹ 3 more variables: verdict <chr>, median_peptide_length <dbl>,
#> # preset_used <chr>Only peptides inside the active length_range count as
valid. The default window is 7 to 25 residues. Fragments outside that
window still appear in the digest tibble, but they do not contribute to
the component numerators. See the scoring
model article for the formula of each component.
Composite score verdict thresholds are Good at
>= 0.65, Moderate at
>= 0.40, and Poor below 0.40.
These strict, conservative boundaries are package design choices for
triage. They are not calibrated probabilities, and the current
PeptideAtlas analysis did not reliably calibrate them. Treat the labels
as prompts for review, not predictions of experimental success.
pepVet is not a peptide detectability predictor. Existing ML-based tools learn peptide-level detection patterns from experimental MS data. pepVet does something narrower: it ranks the digest quality of a protein for a chosen enzyme-workflow combination using explicit physicochemical criteria.
pepVet scores describe which digest-level components raise or lower a ranking. They do not estimate the observation probability of a specific peptide.
The scoring model separates package design choices, expert priors, physical constants, and the limited PeptideAtlas evaluation. The workflow presets article lists the resolved settings for each preset.
S_charge reflects extra internal basic-residue
richness, not whether a peptide can ionize at all.pepVet scores are interpretable rankings, not calibrated probabilities. The composite score has no unit. It ranks proteins by digest quality within a given enzyme-workflow combination.
Cross-workflow comparisons are not valid when the resolved scoring
configuration changes. score_peptides() records a
preset_used column. The function reports a preset name when
the resolved GRAVY window, peptide-length window, weights, and pI
setting exactly match a shipped preset. It marks other configurations as
"custom".
Workflow presets give you a starting parameter set for common
experiment types. Each preset returns a list with
gravy_range, length_range, and
weights, so you can pass the result into
evaluate_digest() or score_peptides()
directly.
pepvet_preset("standard")
#> $gravy_range
#> [1] -1.0 0.6
#>
#> $length_range
#> [1] 7 25
#>
#> $weights
#> S_length S_coverage S_count S_hydro S_charge S_unique
#> 0.200 0.348 0.226 0.138 0.088 0.000
#>
#> $include_pI
#> [1] FALSEsyn_path <- system.file("extdata", "P37840_isoforms.fasta", package = "pepVet")
syn_proteome <- digest_protein(syn_path, enzyme = "trypsin")
targeted_preset <- pepvet_preset("targeted")
do.call(
evaluate_digest,
c(list(sequence = syn_path, enzyme = "trypsin", proteome = syn_proteome), targeted_preset)
)$scores
#> # A tibble: 3 × 13
#> protein_id S_length S_coverage S_count S_hydro S_charge S_unique
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sp|P37840|SYUA_HUMAN Al… 0.516 0.693 1 0.812 0.688 0
#> 2 sp|P37840-2|SYUA_HUMAN … 0.581 1 1 0.722 0.667 0.111
#> 3 sp|P37840-3|SYUA_HUMAN … 0.481 0.659 1 0.692 0.615 0.231
#> # ℹ 6 more variables: composite_score <dbl>, verdict <chr>,
#> # median_peptide_length <dbl>, preset_used <chr>,
#> # n_high_efficiency_sites <int>, n_low_efficiency_sites <int>Six presets ship with pepVet. Each bundles a length range, a GRAVY window, and scoring weights for a common experiment type. See the workflow presets article for the full reference.
Presets with non-zero S_unique weights require a
proteome digest. pepVet rejects those presets when proteome
is missing because the component is undefined without a comparison
background.
When you supply a proteome digest,
score_peptides() adds a sixth component,
S_unique. It measures the fraction of valid peptides that
appear in exactly one protein in the supplied background. Shared
sequences can map to more than one protein, so the relevance of this
component depends on the background.
# Digest the proteome background first
proteome_digest <- digest_protein(proteome_path, enzyme = "trypsin")
# Score BSA in the context of that proteome
bsa_digest <- digest_protein(bsa_path, enzyme = "trypsin")
score_peptides(bsa_digest, proteome = proteome_digest)Proteome-aware scoring switches the default weight set to give
S_unique a 20% share:
c(S_length = 0.160, S_coverage = 0.279, S_count = 0.181, S_hydro = 0.110, S_charge = 0.070, S_unique = 0.200).
evaluate_digest() wraps digest_protein()
and score_peptides() into a single call and returns a named
list.
ev <- evaluate_digest(bsa_path, enzyme = "trypsin", missed_cleavages = 1L)
names(ev)
#> [1] "scores" "peptides" "params"ev$scores
#> # A tibble: 1 × 12
#> protein_id S_length S_coverage S_count S_hydro S_charge composite_score
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sp|P02769|ALBU_B… 0.688 0.997 1 0.769 0.778 0.885
#> # ℹ 5 more variables: verdict <chr>, median_peptide_length <dbl>,
#> # preset_used <chr>, n_high_efficiency_sites <int>,
#> # n_low_efficiency_sites <int>ev$params
#> $enzyme
#> [1] "trypsin"
#>
#> $missed_cleavages
#> [1] 1
#>
#> $protein_ids
#> [1] "sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB PE=1 SV=4"
#>
#> $preset_used
#> [1] "standard"
#>
#> $gravy_range
#> [1] -1.0 0.6
#>
#> $length_range
#> [1] 7 25
#>
#> $weights
#> S_length S_coverage S_count S_hydro S_charge
#> 0.200 0.348 0.226 0.138 0.088
#>
#> $proteome_aware
#> [1] FALSE
#>
#> $include_pI
#> [1] FALSEThe $params element records the resolved enzyme name,
missed cleavages, full protein IDs, and preset_used
value.
When you need peptide-level cleavage-risk context, request it
explicitly. The flag adds a cleavage_efficiency column to
the peptide table. The protein-level efficiency counts
(n_high_efficiency_sites,
n_low_efficiency_sites) are always present in the scores
table regardless of this flag.
ev_eff <- evaluate_digest(
bsa_path,
enzyme = "trypsin",
missed_cleavages = 1L,
include_cleavage_efficiency = TRUE
)
ev_eff$peptides
#> # A tibble: 157 × 7
#> protein_id peptide start end length missed_cleavages cleavage_efficiency
#> <chr> <chr> <int> <int> <int> <int> <chr>
#> 1 sp|P02769|AL… MK 1 2 2 0 high
#> 2 sp|P02769|AL… MKWVTF… 1 19 19 1 high
#> 3 sp|P02769|AL… WVTFIS… 3 19 17 0 high
#> 4 sp|P02769|AL… WVTFIS… 3 23 21 1 medium
#> 5 sp|P02769|AL… GVFR 20 23 4 0 medium
#> 6 sp|P02769|AL… GVFRR 20 24 5 1 low
#> 7 sp|P02769|AL… R 24 24 1 0 low
#> 8 sp|P02769|AL… RDTHK 24 28 5 1 low
#> 9 sp|P02769|AL… DTHK 25 28 4 0 low
#> 10 sp|P02769|AL… DTHKSE… 25 34 10 1 low
#> # ℹ 147 more rows
ev_eff$scores[, c("protein_id", "n_high_efficiency_sites", "n_low_efficiency_sites")]
#> # A tibble: 1 × 3
#> protein_id n_high_efficiency_si…¹ n_low_efficiency_sites
#> <chr> <int> <int>
#> 1 sp|P02769|ALBU_BOVIN Albumin OS… 61 18
#> # ℹ abbreviated name: ¹n_high_efficiency_sitesThe protein-level counts are informational and do not contribute to the score. They summarize the sequence-local labels returned by the annotation rules.
compare_digests() runs evaluate_digest()
across a vector of enzyme names for a single protein and returns a
tibble sorted from best to worst composite score.
comp <- compare_digests(
bsa_path,
enzymes = c(
"trypsin", "lysc", "glutamyl endopeptidase",
"asp-n endopeptidase", "chymotrypsin-high"
),
missed_cleavages = 1L
)
comp
#> # A tibble: 5 × 13
#> enzyme protein_id S_length S_coverage S_count S_hydro S_charge composite_score
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 tryps… sp|P02769… 0.688 0.997 1 0.769 0.778 0.885
#> 2 gluta… sp|P02769… 0.681 0.936 1 0.802 0.975 0.884
#> 3 lysc sp|P02769… 0.628 0.857 1 0.763 0.776 0.823
#> 4 asp-n… sp|P02769… 0.494 0.529 0.923 0.725 0.925 0.673
#> 5 chymo… sp|P02769… 0.477 0.590 0.840 0.549 0.863 0.642
#> # ℹ 5 more variables: verdict <chr>, median_peptide_length <dbl>,
#> # preset_used <chr>, n_high_efficiency_sites <int>,
#> # n_low_efficiency_sites <int>compare_digests() defines one row per enzyme for one
protein and therefore rejects multi-protein input.
recommend_enzyme() runs compare_digests()
internally and returns the name of the highest-scoring enzyme as a
character string. If multiple enzymes tie on the composite score, the
function returns all tied names in alphabetical order. Despite the
function’s established name, this is a model ranking under the supplied
settings, not an experimental recommendation.
batch_evaluate() runs evaluate_digest()
independently for every protein in a multi-FASTA file and returns a
flat tibble with one row per protein. Columns include
protein_id, protein_length, all component
scores, composite_score, verdict, count
fields, and four sequence-level difficulty flags.
batch <- batch_evaluate(proteome_path,
enzyme = "trypsin",
missed_cleavages = 1L
)
# Number of proteins evaluated
nrow(batch)
# Score and verdict for the first few proteins
batch[, c("protein_id", "composite_score", "verdict")]batch_evaluate() evaluates each protein independently.
Valid-peptide counts and hydrophobicity flags follow the active
length_range and gravy_range. Short-protein
and low-complexity flags remain fixed sequence-level heuristics. The
returned tibble serves as input to summarize_batch() and
triage_proteins().
For proteome-aware uniqueness scoring, supply a background proteome digest:
proteome_digest <- digest_protein(proteome_path, enzyme = "trypsin")
batch <- batch_evaluate(proteome_path,
enzyme = "trypsin",
proteome = proteome_digest
)summarize_batch() computes proteome-level aggregate
statistics from the tibble returned by batch_evaluate(). It
returns a named list with five elements.
summary <- summarize_batch(batch)
# Verdict distribution (Good / Moderate / Poor and their percentages)
summary$verdict_counts
# Composite score distribution
summary$score_distribution
# Per-component mean scores: lowest value is the weakest dimension
summary$component_summary
# Proteins in the bottom 10% by composite score
summary$problem_proteins
# Moderate / Poor proteins where hydrophobicity or short-protein flags are set:
# likely candidates for switching to a less specific enzyme
summary$enzyme_switch_candidatesenzyme_switch_candidates is a heuristic derived from
sequence-level difficulty flags, not from running alternative enzymes.
Use compare_digests() to assess a specific alternative
enzyme under the active scoring settings.
triage_proteins() appends a deterministic
action label derived from the verdict and difficulty
flags.
triaged <- triage_proteins(batch)
# Count of each action
table(triaged$action)
# Proteins that should be tried with a different enzyme
triaged[
triaged$action == "try_other_enzyme",
c("protein_id", "verdict", "composite_score")
]The action rules are:
proceed: the verdict is Good.skip: a non-Good row has no valid peptides or has the
low-complexity flag.try_other_enzyme: a remaining row has the
hydrophobicity flag, the short-protein flag, or a Poor
verdict.consider_alternative: any remaining
Moderate row.These labels do not run another enzyme or predict that the suggested action will improve an experiment.
export_peptide_list() filters valid peptides from any
digest_protein() tibble and writes them in a format
compatible with downstream tools.
peps <- digest_protein(bsa_path, enzyme = "trypsin", missed_cleavages = 1L)
# Skyline transition list: one row per valid peptide per charge state
# Columns: Protein, Peptide Sequence, Precursor Charge, Precursor Mz
export_peptide_list(peps, format = "skyline", charges = 2:3)
#> # A tibble: 216 × 4
#> Protein `Peptide Sequence` `Precursor Charge` `Precursor Mz`
#> <chr> <chr> <int> <dbl>
#> 1 sp|P02769|ALBU_BOVIN Al… MKWVTFISLLLLFSSAY… 2 1132.
#> 2 sp|P02769|ALBU_BOVIN Al… MKWVTFISLLLLFSSAY… 3 755.
#> 3 sp|P02769|ALBU_BOVIN Al… WVTFISLLLLFSSAYSR 2 1002.
#> 4 sp|P02769|ALBU_BOVIN Al… WVTFISLLLLFSSAYSR 3 668.
#> 5 sp|P02769|ALBU_BOVIN Al… WVTFISLLLLFSSAYSR… 2 1232.
#> 6 sp|P02769|ALBU_BOVIN Al… WVTFISLLLLFSSAYSR… 3 821.
#> 7 sp|P02769|ALBU_BOVIN Al… DTHKSEIAHR 2 597.
#> 8 sp|P02769|ALBU_BOVIN Al… DTHKSEIAHR 3 399.
#> 9 sp|P02769|ALBU_BOVIN Al… SEIAHRFK 2 494.
#> 10 sp|P02769|ALBU_BOVIN Al… SEIAHRFK 3 330.
#> # ℹ 206 more rows# Generic annotated table: all peptide columns plus gravy, pI, and valid flag
export_peptide_list(peps, format = "generic")
#> # A tibble: 157 × 9
#> protein_id peptide start end length missed_cleavages gravy pI valid
#> <chr> <chr> <int> <int> <int> <int> <dbl> <dbl> <lgl>
#> 1 sp|P02769|ALB… MK 1 2 2 0 -1 9.25 FALSE
#> 2 sp|P02769|ALB… MKWVTF… 1 19 19 1 0.984 10.3 TRUE
#> 3 sp|P02769|ALB… WVTFIS… 3 19 17 0 1.22 9.05 TRUE
#> 4 sp|P02769|ALB… WVTFIS… 3 23 21 1 1.09 11.1 TRUE
#> 5 sp|P02769|ALB… GVFR 20 23 4 0 0.525 10.3 FALSE
#> 6 sp|P02769|ALB… GVFRR 20 24 5 1 -0.48 12.5 FALSE
#> 7 sp|P02769|ALB… R 24 24 1 0 -4.5 10.3 FALSE
#> 8 sp|P02769|ALB… RDTHK 24 28 5 1 -3.16 9.25 FALSE
#> 9 sp|P02769|ALB… DTHK 25 28 4 0 -2.82 7.00 FALSE
#> 10 sp|P02769|ALB… DTHKSE… 25 34 10 1 -1.7 7.17 TRUE
#> # ℹ 147 more rows# FASTA character vector for valid peptides only
# Each header: >protein_id|start-end
head(export_peptide_list(peps, format = "fasta"))
#> [1] ">sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB PE=1 SV=4|peptide_1-19"
#> [2] "MKWVTFISLLLLFSSAYSR"
#> [3] ">sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB PE=1 SV=4|peptide_3-19"
#> [4] "WVTFISLLLLFSSAYSR"
#> [5] ">sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB PE=1 SV=4|peptide_3-23"
#> [6] "WVTFISLLLLFSSAYSRGVFR"Write directly to a file by passing a path to the file
argument:
pepvet_check() combines evaluate_digest()
and digest_report() in one interactive call. It prints a
console report and returns the result invisibly.
result <- pepvet_check(bsa_path, enzyme = "trypsin")
#> pepVet digest check
#> -------------------
#> Protein sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB
#> PE=1 SV=4
#> Enzyme trypsin
#> Preset standard
#> Missed cleavages Up to 1
#> Peptides 157 total; 108 within 7-25 aa
#> Verdict Good
#> Composite 0.885
#> Component Score Profile
#> S_length 0.688 [#######---]
#> S_coverage 0.997 [##########]
#> S_count 1.000 [##########]
#> S_hydro 0.769 [########--]
#> S_charge 0.778 [########--]The returned value is the full evaluate_digest() result
list, so you can pipe it into any downstream step:
digest_report() prints an ASCII-safe summary for an
evaluate_digest() result or a ranked table for a
compare_digests() result. Long protein identifiers wrap,
and comparison tables simplify when the terminal is narrow. The function
returns its input invisibly.
digest_report(ev)
#> pepVet digest check
#> -------------------
#> Protein sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB
#> PE=1 SV=4
#> Enzyme trypsin
#> Preset standard
#> Missed cleavages Up to 1
#> Peptides 157 total; 108 within 7-25 aa
#> Verdict Good
#> Composite 0.885
#> Component Score Profile
#> S_length 0.688 [#######---]
#> S_coverage 0.997 [##########]
#> S_count 1.000 [##########]
#> S_hydro 0.769 [########--]
#> S_charge 0.778 [########--]digest_report(comp)
#> pepVet enzyme comparison
#> ------------------------
#> Protein sp|P02769|ALBU_BOVIN Albumin OS=Bos taurus OX=9913 GN=ALB
#> PE=1 SV=4
#> Best score trypsin (0.885, Good)
#> Rank Enzyme S_len S_cov S_cnt S_hyd S_chg Score Verdict
#> -----------------------------------------------------------------------
#> 1 trypsin 0.688 0.997 1.000 0.769 0.778 0.885 Good
#> 2 glutamyl endopeptidase 0.681 0.936 1.000 0.802 0.975 0.884 Good
#> 3 lysc 0.628 0.857 1.000 0.763 0.776 0.823 Good
#> 4 asp-n endopeptidase 0.494 0.529 0.923 0.725 0.925 0.673 Good
#> 5 chymotrypsin-high 0.477 0.590 0.840 0.549 0.863 0.642 Moderatedigest_report() returns invisible(x), so a
script can print the report and retain the object:
The remaining sections connect the core workflow to a challenging protein, the shipped amino acid data, and the package’s narrower role among proteomics tools.
Histone H3.1 is a package example in which strict tryptic digestion produces many short fragments from its lysine- and arginine-rich N-terminal tail. Many of those fragments fall below pepVet’s conservative 7-residue boundary. This is a scoring result under the active settings, not a universal detection threshold.
ev_h3_trypsin <- evaluate_digest(h3_path, enzyme = "trypsin")
ev_h3_trypsin$scores$verdict
#> [1] "Moderate"In this in-silico comparison, LysC recognises only lysine and cleaves less frequently on H3.1, producing longer peptides with a higher score under the default settings:
compare_digests(
h3_path,
enzymes = c(
"trypsin", "lysc", "glutamyl endopeptidase",
"asp-n endopeptidase"
)
)
#> # A tibble: 4 × 13
#> enzyme protein_id S_length S_coverage S_count S_hydro S_charge composite_score
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lysc sp|P68431… 0.593 0.735 1 0.625 0.938 0.769
#> 2 tryps… sp|P68431… 0.305 0.632 0.662 0.833 0.833 0.619
#> 3 gluta… sp|P68431… 0.467 0.404 1 0.571 0.714 0.602
#> 4 asp-n… sp|P68431… 0.333 0.404 0.640 1 1 0.578
#> # ℹ 5 more variables: verdict <chr>, median_peptide_length <dbl>,
#> # preset_used <chr>, n_high_efficiency_sites <int>,
#> # n_low_efficiency_sites <int>The package ships aa_properties, a 22-row tibble
containing the 20 standard amino acids plus selenocysteine and
pyrrolysine.
aa_properties
#> # A tibble: 22 × 6
#> amino_acid molecular_weight residue_monoisotopic_mass hydrophobicity
#> <chr> <dbl> <dbl> <dbl>
#> 1 A 89.0 71.0 1.8
#> 2 C 121. 103. 2.5
#> 3 D 133. 115. -3.5
#> 4 E 147. 129. -3.5
#> 5 F 165. 147. 2.8
#> 6 G 75.0 57.0 -0.4
#> 7 H 155. 137. -3.2
#> 8 I 131. 113. 4.5
#> 9 K 146. 128. -3.9
#> 10 L 131. 113. 3.8
#> # ℹ 12 more rows
#> # ℹ 2 more variables: pKa_side_chain <dbl>, is_basic <lgl>The kyte_doolittle column contains the Kyte-Doolittle
(1982) hydrophobicity scale used in S_hydro. The
is_basic flag supports S_charge, and the
pKa_side_chain column holds reference pKa values for the
seven ionisable residues (C, D, E, H, K, R, Y) used in
calculate_pI().
pepVet covers a narrow part of the workflow. It is not a spectral library builder or a peptide detectability predictor. It compares enzyme-workflow combinations using explicit digest-level criteria before acquisition. This describes the package scope rather than an experimental performance claim.
The tool-comparison vignette compares pepVet against five tools on a panel of five proteins and five enzymes. It covers baseline peptide overlap, capability differences across 16 dimensions, the scoring model in practice, workflow preset effects, pipeline integration with PeptideRanger, and a binary versus graded classification comparison with Protein Cleaver.
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 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] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] pepVet_0.99.0 rmarkdown_2.31
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.7.3 crayon_1.5.3 cli_3.6.6
#> [4] knitr_1.51 rlang_1.3.0 xfun_0.60
#> [7] otel_0.2.0 generics_0.1.4 jsonlite_2.0.0
#> [10] glue_1.8.1 S4Vectors_0.51.5 buildtools_1.0.0
#> [13] Biostrings_2.81.3 htmltools_0.5.9 maketools_1.3.2
#> [16] sys_3.4.3 sass_0.4.10 stats4_4.6.1
#> [19] cleaver_1.51.0 Seqinfo_1.3.0 tibble_3.3.1
#> [22] evaluate_1.0.5 jquerylib_0.1.4 fastmap_1.2.0
#> [25] IRanges_2.47.2 yaml_2.3.12 lifecycle_1.0.5
#> [28] compiler_4.6.1 pkgconfig_2.0.3 XVector_0.53.0
#> [31] digest_0.6.39 R6_2.6.1 utf8_1.2.6
#> [34] pillar_1.11.1 magrittr_2.0.5 bslib_0.11.0
#> [37] tools_4.6.1 BiocGenerics_0.59.10 cachem_1.1.0