MTS-Finder

MTS-Finder is a desktop application for discovering mitochondrial targeting sequences (MTS / presequences) in peptide-level shotgun (bottom-up) proteomics data. It cross-references identified peptides against curated Uniprot presequence annotations and TargetP-2.0 cleavage-site predictions, flags peptides that fall within a mitochondrial presequence, fetches gene symbols from Uniprot, and computes quantitative comparisons between experimental conditions.

Current version: v3.6 (2026) — see CHANGELOG.md for the full release history. v3.6 is the first fully working release; the original v3.4 (2023) was non-functional.

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Features

• MTS / presequence detection — matches each peptide's master-protein accession against two reference sets:
  • Uniprot curated presequence (transit-peptide) annotations (files/Uniprot_MTS.xlsx)
  • TargetP-2.0 mitochondrial cleavage-site predictions for the MitoCarta3 proteome (files/TargetP2_0_prediction_mitocarta3.xlsx)
• Cleavage-site aware filtering — a peptide is reported only when it lies within the presequence, i.e. its last residue ends at or before the predicted/annotated cleavage site.
• Automatic gene-symbol lookup — resolves accessions to gene symbols via the EBI Proteins API, with a retry and a graceful fallback to the accession ID when offline.
• Flexible input — reads Proteome Discoverer–style peptide exports as either Excel (.xlsx) or tab-delimited text (.txt).
• Normalized or raw abundances — pick "Abundances (Normalized)" or raw "Abundance" channels with a single toggle.
• Channel-to-condition mapping — a simple comma-separated list assigns each abundance channel to an experimental condition; skip and Boost channels are dropped automatically.
• Built-in quantification — for each requested comparison pair it computes:
  • Log2(ratio) of the group means
  • pvalue from an unpaired (Welch-style, equal_var=True) two-sample t-test
  • -Log10 pvalue (volcano-plot ready)
  • Degenerate rows (zero/negative ratios, empty groups) yield NaN instead of crashing the run.
• Excel output — results are written to an .xlsx workbook with one row per matched peptide, and an Open button launches the file when done.
• Simple GUI — a Tkinter interface with file browsers and editable Conditions/Pairs boxes; long-running analysis executes on a background thread so the window stays responsive.

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Requirements

• Python 3.8+
• Python packages:
  • pandas
  • scipy
  • requests
  • openpyxl (Excel read/write)
  • tkinter (ships with most Python installations; on Linux install python3-tk)
• Internet access (optional but recommended) for gene-symbol lookups via the EBI Proteins API.

Install the dependencies:

pip install pandas scipy requests openpyxl

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Installation

git clone https://github.com/science64/MTS-Finder.git
cd MTS-Finder
pip install pandas scipy requests openpyxl

Make sure the files/ folder (shipped with the repository) stays next to main.py — it contains the reference databases and the application icon that the tool loads at runtime.

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Usage

Launch the application from the project root:

python main.py

Then, in the window:

1. Browse and select your peptide file (.xlsx or tab-delimited .txt).
2. Choose Normalized values or Non-Normalized values.
3. Enter an Output Name for the result workbook.
4. Review/edit the Conditions and Pairs boxes (these are pre-filled from condtions.txt and pairs.txt; you can also load them from a file with their Browse buttons).
5. Click RUN. Progress is shown in the status box.
6. When finished, click Open to view the generated <Output Name>.xlsx, saved next to your input file.

Note: the chosen Conditions and Pairs are also written back as condtions.txt and pairs.txt in the output folder, so your last configuration is preserved.

Conditions

A single comma-separated line with one label per abundance channel, in the same order as the channels appear in the file. Special labels are dropped before analysis:

• skip — ignore this channel
• Boost — ignore the carrier/boost channel

Example:

Light,WT,WT,WT,WT,KO,KO,KO,KO,skip,Boost

Pairs

Semicolon-separated comparisons, each written as numerator/denominator. Each pair produces a Log2, pvalue, and -Log10 pvalue column. For a KO/WT comparison, KO is the numerator (group 2) and WT is the denominator (group 1):

KO/WT

Multiple comparisons:

KO/WT; Rotenone/0DMSO; Antimycin/0DMSO

Expected input columns

The peptide file should contain the typical Proteome Discoverer peptide-export columns, including:

• Positions in Master Proteins (e.g. P49189 [275-293], or multiple ; -separated accessions)
• Modifications
• Abundance channels — either Abundances (Normalized)... / Abundances Normalized... or Abundance: ... / Abundance...

Output columns

Column	Description
Accession	Uniprot accession of the master protein
Gene Symbol	Gene symbol resolved from Uniprot (falls back to accession)
Positions in Master Proteins	Gene symbol with peptide position range
Modifications	Peptide modifications
Uniprot / Uniprot Location	Whether matched via Uniprot annotation, and the cleavage-site position
TargetP / TargetP Location	Whether matched via TargetP-2.0 prediction, and the cleavage-site position
(condition channels)	Abundance values, renamed to your condition labels
Log2(num/den)	Log2 ratio of group means, per pair
pvalue(num/den)	Unpaired two-sample t-test p-value, per pair
-Log10 pvalue(num/den)	−log10 of the p-value, per pair

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Reference data

The files/ directory contains the bundled reference databases used by the matcher:

• Uniprot_MTS.xlsx — Uniprot presequence (transit-peptide) locations
• TargetP2_0_prediction_mitocarta3.xlsx — TargetP-2.0 mitochondrial cleavage-site predictions
• Total_accession_precurser.xlsx — combined accession list used for fast pre-filtering
• icon.ico — application icon

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Project structure

MTS-Finder/
├── main.py          # Tkinter GUI and analysis orchestration
├── functions.py     # MTS-finding engine, Uniprot/TargetP matching, statistics
├── condtions.txt    # Default conditions line
├── pairs.txt        # Default comparison pairs
├── files/           # Reference databases and icon
├── CHANGELOG.md
├── LICENSE
└── README.md

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License

Released under the MIT License. Copyright © 2023 Süleyman Bozkurt. See LICENSE for the full text.

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Citation / contact

Developed by Süleyman Bozkurt. For questions or issues, please open an issue on the project repository.