MSstatsLiP

Workflows

Proteolytic Resistance Analysis

library(MSstatsLiP)

# Calculate proteolytic resistance ratios and perform differential analysis
Accessibility <- calculateProteolyticResistance(
  MSstatsLiP_Summarized, 
  fasta_file, 
  differential_analysis = TRUE
)

# Plot proteolytic resistance results as a barcode along the protein sequence
ResistanceBarcodePlotLiP(
  Accessibility, 
  fasta_file, 
  which.prot = "P16622", 
  which.condition = "F1", 
  differential_analysis = TRUE, 
  which.comp = "F1 vs F2", 
  address = FALSE
)

Input: Summarized LiP-MS and TrP-MS datasets (MSstatsLiP_Summarized) and a FASTA file (fasta_file). Output: Proteolytic resistance ratios and sequence-aligned barcode plots.

Standard Workflow

library(MSstatsLiP)

# Convert raw Spectronaut data to MSstatsLiP format
msstats_data <- SpectronauttoMSstatsLiPFormat(
  raw_lip, 
  fasta_file, 
  raw_prot
)

# Summarize the preprocessed data
MSstatsLiP_Summarized <- dataSummarizationLiP(
  msstats_data, 
  normalization.LiP = "equalizeMedians"
)

# Perform group comparison modeling
MSstatsLiP_model <- groupComparisonLiP(MSstatsLiP_Summarized)

Input: Raw Spectronaut LiP and TrP datasets, and a FASTA file. Output: Preprocessed, summarized, and modeled LiP-MS and TrP-MS datasets.

When to Use

• Analyzing Limited Proteolysis-coupled Mass Spectrometry (LiP-MS) datasets alongside Trypsin-only control (TrP-MS) datasets.
• Preprocessing Spectronaut exports using SpectronauttoMSstatsLiPFormat.
• Performing differential analysis of proteolytic resistance patterns across conditions using calculateProteolyticResistance and groupComparisonLiP.
• Visualizing peptide-level proteolytic resistance changes along a protein sequence using ResistanceBarcodePlotLiP.

When NOT to Use

• For standard bottom-up shotgun proteomics data without limited proteolysis, use MSstats directly.
• For differential expression analysis of RNA-seq data, use packages like limma, edgeR, or DESeq2.

Data Requirements

• Raw LiP-MS and TrP-MS datasets (e.g., Spectronaut exports) containing columns like PG.ProteinAccessions, PEP.GroupingKey, EG.Qvalue, and F.PeakArea.
• A FASTA file containing the protein sequences of interest.

Key Parameters

• normalization.LiP ("equalizeMedians"): Normalization method for LiP data in dataSummarizationLiP.
• differential_analysis (TRUE): Whether to perform differential analysis in calculateProteolyticResistance.
• which.prot (NULL): Protein accession to plot in ResistanceBarcodePlotLiP.
• which.condition (NULL): Condition to plot in ResistanceBarcodePlotLiP.
• address (FALSE): Whether to save the plot to a file or display it in ResistanceBarcodePlotLiP.

Best Practices

• Filter out half-tryptic (HT) peptides and retain only fully tryptic (FT) peptides for proteolytic resistance analysis using calculateTrypticity.
• Ensure that the Condition nomenclature is identical in both LiP and TrP datasets before summarization.
• Ensure unique BioReplicate nomenclature for case-control experiments.

Common Pitfalls

• Mismatched condition names between LiP and TrP datasets, causing errors in summarization or modeling; verify using unique(msstats_data[["LiP"]]$Condition) %in% unique(msstats_data[["TrP"]]$Condition).
• High memory usage during summarization; clear memory cache using rm() and gc() if needed.

Alternatives

• MSstats for standard proteomics without limited proteolysis.
• limma for general linear modeling of expression data.

Citations

• Cappelletti et al., 2021 (referenced in vignette text)

References

• Homepage: bioconductor.org/packages/MSstatsLiP
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html