Our software aims at infering protein levels from observed peptide levels in a mass spectrometry experiment.
Example use:
import furious_fastas as ff
from pep2prot.graph_ops import get_minimal_protein_group_coverage
from pep2prot.readers import read_ms2rescore_peptide_report
fastas_path = "data/Human_2024_02_16_UniProt_Taxon9606_Reviewed_20434entries_contaminant_tenzer.fasta"
ms2rescore_input = "data/results.sage.ms2rescore.mokapot.peptides.txt"
fastas = [(f.header.split(" ", 1)[0][1:], f.sequence) for f in ff.fastas(fastas_path)]
peptide_report = read_ms2rescore_peptide_report(ms2rescore_input)
covering_protein_groups = get_minimal_protein_group_coverage(
peptides=list(set(peptide_report.raw_sequence)),
fastas=fastas,
min_number_of_peptides=3,
)This should result in a pandas dataframe, like so:
prot_idxs repr_prot_id peptide_cnt accessions prot_in_group_cnt pep_ids
group
12 [69] 69 3 [sp|A4UGR9|XIRP2_HUMAN] 1 [454, 1832, 2286]
79 [489] 489 3 [sp|O14939|PLD2_HUMAN] 1 [692, 2571, 2694]
96 [537] 537 4 [sp|O15078|CE290_HUMAN] 1 [398, 1583, 1841, 1880]
131 [738] 738 3 [sp|O43295|SRGP3_HUMAN] 1 [1118, 1629, 2230]
132 [743] 743 3 [sp|O43303|CP110_HUMAN] 1 [271, 859, 2877]
... ... ... ... ... ... ...
2517 [20436] 20436 60 [sp|P02769|ALBU_BOVIN_CONTA] 1 [202, 339, 394, 414, 596, 618, 629, 730, 905, ...
2519 [20566] 20566 3 [sp|P00761|TRYP_PIG_CONTA] 1 [887, 2869, 3111]
2520 [20567] 20567 23 [sp|P00924|ENO1_YEAST_CONTA] 1 [3, 21, 51, 63, 65, 70, 147, 251, 336, 646, 79...
2521 [20568] 20568 23 [sp|P00925|ENO2_YEAST_CONTA] 1 [21, 63, 110, 147, 251, 367, 598, 646, 668, 68...
2522 [20569] 20569 9 [sp|P49065|ALBU_RABIT_CONTA] 1 [618, 1710, 1816, 1893, 2334, 2525, 2573, 2891...
On Linux.
python3 -m venv ve_pep2prot
source ve_pep2prot/bin/activate
pip install -r requirements.txt