Unspecific search - peptidomics

[ErikHartman]

Hi,

I'm trying to use sage to search some peptidomics data. Doing a search with PEAKS X resulted in ~3k peptides. I used the splitting tactic suggested in #154 and #97 to split the fasta into chunks and concatenate the results. In the end, the search resulted in 0 peptides. I likely did something stupid, but I don't know what.

Here is my config file:

{
  "database": {
    "bucket_size": 8192,
    "enzyme": {
      "missed_cleavages": 2,
      "min_len": 7,
      "max_len": 50,
      "cleave_at": ""
    },
    "fragment_min_mz": 150.0,
    "fragment_max_mz": 2000.0,
    "peptide_min_mass": 500.0,
    "peptide_max_mass": 5000.0,
    "ion_kinds": [
      "b",
      "y"
    ],
    "min_ion_index": 2,
    "max_variable_mods": 3,
    "static_mods": {
      "C": 57.0215
    },
    "variable_mods": {
      "M": 15.994
    },
    "decoy_tag": "rev_",
    "generate_decoys": true,
    "fasta": "./UP000005640_9606.fasta"
  },
  "quant": {
    "lfq": true,
    "lfq_settings": {
      "peak_scoring": "Hybrid",
      "integration": "Sum",
      "spectral_angle": 0.6,
      "ppm_tolerance": 5.0
    }
  },
  "precursor_tol": {
    "ppm": [
      -20.0,
      20.0
    ]
  },
  "fragment_tol": {
    "ppm": [
      -20.0,
      20.0
    ]
  },
  "isotope_errors": [
    0,
    2
  ],
  "deisotope": true,
  "min_peaks": 15,
  "max_peaks": 150,
  "max_fragment_charge": 1,
  "min_matched_peaks": 4,
  "predict_rt": true,
  "output_directory": ".",
  "mzml_paths": [
    "./SK_T2501_75uL_100SPE_60SPD_250116_4_S1-A1_1_3536.d"
  ]
}

Is there some blatant error here?

Example output for one chunk:

WARNING - Sage stderr for slice 4:
[2025-01-29T12:27:49Z WARN  sage_core::modification] Variable modifications must be specified as a list of modifications: [15.994]. This will become a HARD ERROR by v0.15
[2025-01-29T12:31:41Z INFO  sage] generated 7732957836 fragments, 278827623 peptides in 232567ms
[2025-01-29T12:31:41Z INFO  sage] processing files 0 .. 1 
[2025-01-29T12:31:42Z INFO  sage] - file IO:     1224 ms
[2025-01-29T12:32:01Z INFO  sage] - search:     18922 ms (1477 spectra/s)
[2025-01-29T12:32:01Z INFO  sage_core::ml::retention_alignment] aligning file #0: y = 1.0000x + 0.0000
[2025-01-29T12:32:01Z INFO  sage_core::ml::retention_alignment] aligned retention times across 1 files
[2025-01-29T12:32:01Z INFO  sage_core::ml::retention_model] - fit retention time model, rsq = 0.9703626922080913
[2025-01-29T12:32:01Z INFO  sage_core::ml::mobility_model] - fit mobility model, rsq = 0.9830665423653131, mse = 0.00006177281566545257
[2025-01-29T12:32:02Z INFO  sage_core::lfq] tracing MS1 features
[2025-01-29T12:32:02Z INFO  sage_core::lfq] integrating MS1 features
[2025-01-29T12:32:02Z INFO  sage] discovered 0 target MS1 peaks at 5% FDR
[2025-01-29T12:32:02Z INFO  sage] discovered 193 target peptide-spectrum matches at 1% FDR
[2025-01-29T12:32:02Z INFO  sage] discovered 0 target peptides at 1% FDR
[2025-01-29T12:32:02Z INFO  sage] discovered 0 target proteins at 1% FDR
[2025-01-29T12:32:03Z INFO  sage] finished in 253s
[2025-01-29T12:32:03Z INFO  sage] cite: "Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale" https://doi.org/10.1021/acs.jproteome.3c00486

[lazear]

Nothing looks terribly wrong from first glance - maybe try widening tolerances and see if it helps?

I would also recommend checking to see what the overlap of peptides/PSMs is versus PEAKS (ignoring FDR control, since sage will write everything to output) as a sanity check. It's possible that LDA isn't a great pick for rescoring in this case, so you could try something like using Mokapot to aggregate all of the results across slices.