Peptide location fingerprinting (PLF) is a technique capable of identifying modified proteins and potential causal mechanisms in complex biological samples. In standard proteomics, proteins are trypsinised which generate peptides whose sequence identities and relative abundances are measured by LC-MS/MS. During this process most proteins are only partially digested, due to differing solubilities, stabilities and enzyme susceptibilities related to their higher order structures. By mapping and quantifying LC-MS/MS-detected peptides within specific regions, PLF enables the detection of statistical differences in the regional digestibility along the protein structure due to ageing and disease mechanisms.

Quick start

Instalation

!python version 3.9 is required If you have a different default version and you got miniconda/miniforge/anaconda installed you can create a python3.9 env with:

     conda create -n py39env python=3.9
     conda activate py39env

Then when you have checked that your python is 3.9 you can create a new virtual python enviroment

     python -m venv mplf_venv

Activate the enviroment (Linux and Mac):

       source mplf_venv/bin/activate

Activate the enviroment (Windows)

       .\mplf_venv\Scripts\activate.bat

Clone the PLF repo:

      git clone https://github.com/maxozo/PLF.git

Install requirements:

       cd PLF
       pip install -r requirements.txt          

Test data

To just try to run the analysis on test data please run (thats also available for download on our MPLF website ):

     python PLF.py --test --outname My_Test_Run

Note: MyTestRun can be a path/to/MyTestRun/Filename

Your own data

1. Prepeare a file that lists Protein name (optional if source protein not determined), Peptide sequence (remove any special characters from these), Sample of protein belionging and spectra (can be multiple columns as per: spectra_1,spectra_2, etc. -- these will be added up): as per this file.

| Protein | Sample | Peptide | spectra | spectra2 | spectra3 | spectra4 | | -------- | -------- | -------- | -------- | -------- | -------- | -------- | | FBLN1HUMAN | 20180601SherrattMMO15.raw (FullSkin15) | CLAFECPENYR | 0 | 1 | 0 | 0 | | FBLN1HUMAN | 20180601SherrattMMO15.raw (FullSkin_15) | CVDVDECAPPAEPCGK | 0 | 1 | 0 | 0 |

1. Prepeare a tsv file that lists the experimental design - as per this file. If paired make sure that the rows list matching pairs, otherwise any order is ok.

| forearm | buttock |
| -------- | -------- |
| sample1 | sample2 | | sample3 | sample4 | | sample6 | sample7 |

1. Run the MPLF pipeline:

    python ../../PLF.py --experimental_design Experiment_feed.tsv --peptides Sample_Data_For_Analysis.csv --spiecies HUMAN --domain_types DOMAINS,REGIONS,TOPO_DOM,TRANSMEM,REPEAT,50AA,75AA,100AA --paired True --outname MPLF_RUN --p_threshold 0.05
   

Params:

      Required:

      --experimental_design  This allows to provide the experimental defign file file

      --peptides   This allows to provide your peptides file

      --spiecies   The spiecies of the peptides

      --paired    Is the samples specified in experimental_design paired or unpaired

      --outname  The name of the output files

      Optional:

      --cpus     (default=max available) How many cpus to use for analysis.

      --p_threshold    (dafault=0.05) Only return proteins that has at least one domain with a significance threshold lover or equal to specified

1. Results will produce two files {outname specified}.tsv and {outname specified}.mplf file. TSV file will list all the domains, their p values, quantified data, normalised data etc. MPLF file can be uploaded to Manchester Proteome Location Fingerprinter (MPLF) to perform visualisations of the data.

Methods

For details please read our publication

References (please cite)

• Ozols, M., Le Blanc, A., & Eckersley, A. (2025). PLF: Peptide Location Fingerprinting (Version v1.1.0) [Computer software].
• Eckersley, A. et al. Proteomic fingerprints of damage in extracellular matrix assemblies. Matrix Biol. Plus 5, 100027 (2020).
• Ozols, M. et al. Peptide location fingerprinting reveals modification-associated biomarker candidates of ageing in human tissue proteomes. Aging Cell 20, e13355 (2021).
• Eckersley, A. et al. Peptide Location Fingerprinting Reveals Tissue Region-specific Differences in Protein Structures in an Ageing Human Organ. Int. J. Mol. Sci. 22, 10408 (2021).
• Eckersley, A., Morais, M. R. P. T., Ozols, M. & Lennon, R. Peptide location fingerprinting identifies structural alterations within basement membrane components in ageing kidney. Matrix Biol. 121, 167178 (2023).
• Eckersley, A. et al. Peptide location fingerprinting identifies species- and tissue-conserved structural remodelling of proteins as a consequence of ageing and disease. Matrix Biol. 114, 108137 (2022).