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Protein Inference

This page explains how msmu infers proteins from peptide-level features through msmu.pp.infer_protein.

How proteins are inferred

Protein inference in msmu is performed through a series of incremental refinement steps. By modifying the initial peptide-protein relationship, proteins are grouped based on shared peptide evidence, following principles outlined in Nesvizhskii & Aebersold (2005). The main steps are as follows:

  1. Construct initial peptide-protein graph
    A initial graph explaining peptide-protein relationships is constructed.
  2. Merge indistinguishable proteins (_find_indistinguishable)
    Proteins associated with identical sets of peptides are merged into a single protein group. The protein group is named as a comma-separated list of members.
  3. Collapse subsettable proteins (_find_subsettable)
    If the peptide set of one protein group is a strict subset of another, protein with smaller peptide set is reassigned to the protein group that has larger peptide set.
  4. Resolve subsumable proteins (_find_subsumable)
    Proteins lacking unique peptides are evaluated within connected components of shared peptides. Proteins that cannot be distinguished are merged, while components without unique peptide evidence are dropped.
  5. Finalize protein group assignment
    After above steps, all remaining protein groups are distinguishable (i.e., having at least one unique peptide). Mappings explaining peptide-protein relationship and annotations describing how each protein was handled are stored in mdata.uns.

Input

A MuData that has:

  • A peptide modality containing var["stripped_peptide"] and var["proteins"] (semicolon-separated accessions per peptide). If decoys exist, they are pulled from mdata["peptide"].uns["decoy"].

Usage

Only the MuData is required; the reader's own column names are the defaults.

mdata = mm.pp.infer_protein(mdata)

Optional arguments:

  • modality (default "peptide") — modality holding the peptide-level data.
  • protein_colname (default "proteins") — .var column with the semicolon-delimited accessions.
  • peptide_colname (default "stripped_peptide") — .var column with the peptide sequence.
  • propagated_from (default None) — reuse an existing inference instead of running one.

propagated_from takes an already-inferred MuData, or a path to an .h5mu holding one, and applies its uns["peptide_map"] / uns["protein_map"] as-is (it raises if the source lacks them). The usual case is PTM data, which should carry the protein grouping of its matched global dataset rather than one inferred from modified peptides alone:

ptm_mdata = mm.pp.infer_protein(ptm_mdata, propagated_from=global_mdata)

Output

A MuData with:

  • mdata["peptide"].var["protein_group"]: Newly inferred protein group
  • mdata["peptide"].var["peptide_type"]: Peptide type (unique or shared).
  • Decoys receive the same annotations under mdata.uns["decoy"].

Output MuData also contains mapping information inside uns

  • mdata.uns["peptide_map"]: peptide → protein group mapping.
  • mdata.uns["protein_map"]: per-protein mapping with flags for indistinguishable/subset/subsumable status.

Citation

Nesvizhskii, A. I., & Aebersold, R. (2005). Interpretation of shotgun proteomic data. Molecular & cellular proteomics, 4(10), 1419-1440.