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PAMPA CRAFT: Fillin

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touzet edited this page Mar 16, 2025 · 22 revisions

This option allows to fill in missing fields in a peptide table.

PAMPA CRAFT fillin can automatically compute masses for peptides that lack this information. Peptide mass is computed from the peptide sequence and the PTM description. If no PTM description is provided, PTMs are determined automatically based on the rules outlined in the PTMs page. If FASTA sequences are provided, it can calculate peptide positions or, conversely, deduce the sequence of a peptide marker from its position in the sequence or in the helical region. If only the mass is available, it will search for all tryptic peptides whose mass is compatible. If a taxonomy is provided, it can also add taxonomic information, such as the family, the common name.

It is of particular interest, for instance, in supplementing a manually created peptide table from partial data available in the literature or to cross-check information for peptides.

Usage

 python3 pampa_craft --fillin 
   -p PEPTIDE_TABLE Peptide table for which missing information should be completed
   -f FASTA         Fasta file for supplementary sequences
   -d DIRECTORY     Directory containing Fasta files for supplementary sequences
   -l LIMIT         Limit file to specify constraints on the set of sequences
   -t TAXONOMY      Taxonomy (TSV file)    
   -o OUTPUT        Path to the output file (new peptide table)
   -e ERROR         Error margin tolerance (needed for characterization of peptides from their mass)

Peptide table (-p)

Name of the peptide table to complete. PAMPA CRAFT --fillin will attempt to fill in all blank cells and blank columns.

Output file (-o)

Name of the new table obtained by completion of the input table.

Target sequences (-f, -d and -l)

The target sequences are the amino-acids sequences that will be used to supplement the markers. Those sequences can be available either in a (multi-)FASTA file (-f option), or in a directory containing FASTA files (-d option). In both cases, the set of sequences can optionally be limited to a subset of organisms, molecules or sequence identifiers with -l option.

Option -f : The specified file can contain an arbitrary number of FASTA sequences, coming from various organisms. Refer to the FASTA sequences section for details on the syntax used in FASTA headings.

Option -d: The directory can contain an arbitrary number of FASTA files, following the same requirements as with '-f' option. Only files with extension .fa or .fasta will be examined.

Option -l: This option allows to filter the set of FASTA sequences to limit the selection according to the organism (OS=), the taxid (OX=), the gene name (GN=), the sequence identifier (SeqID=). The full description is given in section Limiting searches.

Error margin tolerance (-e)

This option is used when peptide sequences are guessed from their observed mass.

Taxonomy (-t)

Name of a taxonomy file that will be used to infer taxonomic information: genus, family, order, common name, TaxID, taxon name for example. The full description of the syntax is given in section Taxonomy.

Example 1 : Adding and modifying PTMs

The peptide G of Canis lupus is known to be have the sequence "GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR" and m/z 2999.5 (with 5 hydroxyprolines). You would like to add m/z for 4 hydroxyprolines, and the peptide table should also account for one possible deamidation.

  1. Create the peptide table as below. The values in the PTM column follow the PTMs syntax. The row for 5H is provided as a reference and is optional. The three other lines correspond to the new values.
Taxon Marker Sequence PTM Mass
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 5H 2999.5
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 4H
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 5H1D
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 4H1D

Download the file: table_dog_G.tsv

  1. Type the command:
python3 pampa_craft.py --fillin -p table_dog_G.tsv -o output_dog_G.tsv
  1. The result file is output_dog_G.tsv and looks like below.
Taxon Marker Sequence PTM Mass
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 5H 2999.5
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 4H 2983.51195568482
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 5H1D 3000.49088668482
Canis lupus G GPSGEPGTAGPPGTPGPQGLLGAPGILGLPGSR 4H1D 2984.49597168482

Download the file: output_dog_G.tsv

Example 2 : Identifying peptide markers based on their positions within the helical region

Marker P2 is defined in mammals by its start position in the helical region (292 in COL1A2) and its length (18 amino acids). In this example, the goal is to identify the sequence of marker P2 in three murine species: Mus musculus, Rattus norvegicus, and Rattus rattus. Additionally, you want to compute the corresponding PTMs and mass, and verify whether the peptide undergoes enzymatic digestion.

  1. Create a FASTA file containing the COL1A2 sequences for Mus musculus, Rattus norvegicus and Rattus rattus. The FASTA heading should follow the rules described in the section FASTA sequences.

Download the file : murine.fasta

  1. Create a peptide table to document the information you have about P2 (length, helical position, gene) and the information you aim to obtain (sequence, mass, PTMs, digestion) for your species.
Taxon Marker Sequence Mass PTM Hel Length Gene Digestion
P2 292 18 COL1A2

Download the file: table_P2.tsv

  1. Type the command.
python3 pampa_craft.py --fillin -p table_P2.tsv -f murine.fasta -o output_P2.tsv

The resulting peptide table output_P2.tsv is as follows.

Taxon Marker Sequence Mass PTM Hel Length Gene Digestion
Rattus rattus P2 GSPGEPGSAGPAGPPGLR 1608.7612401848 3H 292 18 COL1A2 Yes
Rattus norvegicus P2 GSPGEPGSAGPAGPPGLR 1608.7612401848 3H 292 18 COL1A2 Yes
Mus musculus P2 GSPGEAGSAGPAGPPGLR 1566.75067512066 2H 292 18 COL1A2 Yes

See the result file: output_P2.tsv

Example 3 : Identifying peptide markers based on their observed m/z

This example explains how to determine a marker's peptide sequence from its observed m/z using FASTA sequences. This can serve two purposes: first, to verify the consistency of MS data with the genetic material, and second, to obtain the precise theoretical mass of peptides, which can aid in classifying new MS spectra.

  1. Create the peptide table to supplement as below.
Taxon Marker Sequence Mass PTM Hel Gene SeqID Begin End
Mus musculus A 1178.6 COL1A2
Mus musculus A 1194.6 COL1A2
Mus musculus B 1453.7 COL1A2
Mus musculus C 1592.8 COL1A2

The empty columns are for the fields that you want to compute: the peptide sequence, the PTMs, the position in the helical region, the Sequence identifier, the begin and end positions in the sequence.

Download the file: table_mouse_ABC.tsv

  1. Type the command line
python3 pampa_craft.py --fillin -p table_mouse_ABC.tsv -f murine.fasta -e 0.1 -o output_mouse_ABC.tsv

Here, we re-use the same FASTA file as in Example 1. The option -e is mandatory in this example: it allows to specify a error margin tolerance between the observed m/z, in the table, and the theoretical mass computed by PAMPA.

  1. The result file is as follows.
Taxon Marker Sequence Mass PTM Hel Gene SeqID Begin End
Mus musculus A SGQPGPVGPAGVR 1178.62764612314 0H 978 COL1A2 NP_031769.2 1074 1086
Mus musculus A SGQPGPVGPAGVR 1194.62256112314 1H 978 COL1A2 NP_031769.2 1074 1086
Mus musculus B GLPGEFGLPGPAGPR 1453.74340491225 2H 484 COL1A2 NP_031769.2 580 594
Mus musculus C GEPGPAGSVGPVGAVGPR 1592.8027106935199 2H 889 COL1A2 NP_031769.2 985 1002
Mus musculus C GATGLPGVAGAPGLPGPR 1592.83909658268 3H 220 COL1A2 NP_031769.2 316 333

As you can see, the program correctly infers the peptide sequences, PTMs, and positions for markers A and B. For marker C, two suggestions are provided: one at position 889 and another at position 220. Since C is known to be at position 889, the peptide at position 220 should be discarded. This can be done by manually modifying the output file.

Download the file: output_mouse_ABC.tsv

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