Example of usage 
================

         MSCI is a Python package designed to evaluate the information content of peptide fragmentation spectra. Our objective was to compute an information-content index for all peptides within a given proteome. This would allow us to devise data acquisition and analysis strategies that generate and prioritize the most informative fragment ions for peptide quantification.

#Download MSCI package and necessary installations

.. code:: python

    #!git clone https://github.com/proteomicsunitcrg/MSCI.git
    #! pip install matchms
    # do not restart session if asked (press cancel matchms since probably you already have matchms installed )
    #%cd MSCI
    #import sys
    #sys.path.append('/content/MSCI')
    

.. code:: python

    ! pip install MSCI==0.2.0



.. parsed-literal::

    Collecting MSCI==0.2.0
      Downloading MSCI-0.2.0-py2.py3-none-any.whl.metadata (903 bytes)
    Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.10/dist-packages (from MSCI==0.2.0) (8.1.7)
    Successfully installed MSCI-0.2.0 gitdb-4.0.11 gitpython-3.1.43 pydeck-0.9.1 smmap-5.0.1 streamlit-1.37.1 tenacity-8.5.0 watchdog-4.0.2
    



.. parsed-literal::

    Requirement already satisfied: biopython in /usr/local/lib/python3.10/dist-packages (1.84)
    Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from biopython) (1.26.4)
    Requirement already satisfied: matchms in /usr/local/lib/python3.10/dist-packages (0.27.0)
    Requirement already satisfied: deprecated>=1.2.14 in /usr/local/lib/python3.10/dist-packages (from matchms) (1.2.14)
  
    

Import
------

.. code:: python

    from MSCI.Preprocessing.Koina import PeptideProcessor
    from MSCI.Grouping_MS1.Grouping_mw_irt import process_peptide_combinations
    from MSCI.Preprocessing.read_msp_file import read_msp_file
    from MSCI.Similarity.spectral_angle_similarity import process_spectra_pairs
    from MSCI.data.digest import parse_fasta_and_digest, tryptic_digest, peptides_to_csv
    from matchms.importing import load_from_msp
    import random
    import numpy as np
    import pandas as pd
    

Generate predicted dataset
---------------------------

Parse fasta file
----------------

.. code:: python

    result = parse_fasta_and_digest("https://raw.githubusercontent.com/proteomicsunitcrg/MSCI/refs/heads/main/tutorial/sp_human_2023_04.fasta", digest_type="trypsin")
    peptides_to_csv(result, "random_tryptic_peptides.txt")

Download the list of peptides of interest
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    import random
    
    # List of standard amino acids
    amino_acids = 'ACDEFGHIKLMNPQRSTVWY'
    
    # Function to generate a single tryptic peptide
    def generate_tryptic_peptide(min_length=8, max_length=20):
        length = random.randint(min_length, max_length - 1)
        peptide = ''.join(random.choices(amino_acids, k=length))
        peptide += random.choice('KR')
        return peptide
    
    # Generate a list of 90 random tryptic peptides
    tryptic_peptides = [generate_tryptic_peptide() for _ in range(90)]
    
    # Generate 5 pairs of peptides that are permutations of each other and print them
    permuted_pairs = []
    for _ in range(5):
        peptide = generate_tryptic_peptide()
        # Ensure the peptide has at least 2 characters to swap
        if len(peptide) < 2:
            continue
        # Select two different positions to swap
        pos1, pos2 = random.sample(range(len(peptide) - 1), 2)
        permuted_peptide_list = list(peptide)
        permuted_peptide_list[pos1], permuted_peptide_list[pos2] = permuted_peptide_list[pos2], permuted_peptide_list[pos1]
        permuted_peptide = ''.join(permuted_peptide_list)
        tryptic_peptides.append(peptide)
        tryptic_peptides.append(permuted_peptide)
        permuted_pairs.append((peptide, permuted_peptide))
    
    # Ensure the last peptide meets the length requirement
    last_peptide_length = random.randint(5, 20)
    last_peptide = ''.join(random.choices(amino_acids, k=last_peptide_length))
    tryptic_peptides[-1] = last_peptide
    
    # Shuffle the list to mix the pairs with the other peptides
    random.shuffle(tryptic_peptides)
    # Save the peptides to a file
    with open('random_tryptic_peptides.txt', 'w') as f:
        for peptide in tryptic_peptides:
            f.write(f"{peptide}\n")
    
    
    print("Generated 100 random tryptic peptides with permutation pairs and saved to 'random_tryptic_peptides.txt'.")
    


.. parsed-literal::

    Generated 100 random tryptic peptides with permutation pairs and saved to 'random_tryptic_peptides.txt'.
    

Predict with Koina
~~~~~~~~~~~~~~~~~~

If available your own list of peptides
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: python

    processor = PeptideProcessor(
        input_file="random_tryptic_peptides.txt",
        collision_energy=30,
        charge=2,
        model_intensity="Prosit_2020_intensity_HCD",
        model_irt="Prosit_2019_irt"
    )
    
    processor.process('random_tryptic_peptides.msp')

Load dataset
------------

.. code:: python

    # You can use your own spectra
    File= 'random_tryptic_peptides.msp'
    spectra = list(load_from_msp(File))


.. parsed-literal::

    2024-08-22 13:30:02,993:WARNING:matchms:add_precursor_mz:No precursor_mz found in metadata.


Group within MS1 tolerance
----------------

.. code:: python

    mz_tolerance = 1
    irt_tolerance = 5
    
    
    mz_irt_df = read_msp_file(File)
    Groups_df = process_peptide_combinations(mz_irt_df, mz_tolerance, irt_tolerance, use_ppm=False)
    
    Groups_df


.. parsed-literal::

    Results DataFrame Columns: Index(['index1', 'index2', 'peptide 1', 'peptide 2', 'm/z  1', 'm/z 2',
           'iRT 1', 'iRT 2'],
          dtype='object')
    



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Calculate similarity within fragment tolerance
----------------

.. code:: python

    Groups_df.columns = Groups_df.columns.str.strip()
    index_array = Groups_df[['index1','index2']].values.astype(int)
    result = process_spectra_pairs(index_array, spectra,  mz_irt_df, tolerance =0, ppm=10)
    result.to_csv("output.csv", index=False)
    result


.. parsed-literal::

    0.002814877157520823
    0.0
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    <svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24"
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          --hover-bg-color: #E2EBFA;
          --hover-fill-color: #174EA6;
          --disabled-fill-color: #AAA;
          --disabled-bg-color: #DDD;
      }
    
      [theme=dark] .colab-df-quickchart {
          --bg-color: #3B4455;
          --fill-color: #D2E3FC;
          --hover-bg-color: #434B5C;
          --hover-fill-color: #FFFFFF;
          --disabled-bg-color: #3B4455;
          --disabled-fill-color: #666;
      }
    
      .colab-df-quickchart {
        background-color: var(--bg-color);
        border: none;
        border-radius: 50%;
        cursor: pointer;
        display: none;
        fill: var(--fill-color);
        height: 32px;
        padding: 0;
        width: 32px;
      }
    
      .colab-df-quickchart:hover {
        background-color: var(--hover-bg-color);
        box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);
        fill: var(--button-hover-fill-color);
      }
    
      .colab-df-quickchart-complete:disabled,
      .colab-df-quickchart-complete:disabled:hover {
        background-color: var(--disabled-bg-color);
        fill: var(--disabled-fill-color);
        box-shadow: none;
      }
    
      .colab-df-spinner {
        border: 2px solid var(--fill-color);
        border-color: transparent;
        border-bottom-color: var(--fill-color);
        animation:
          spin 1s steps(1) infinite;
      }
    
      @keyframes spin {
        0% {
          border-color: transparent;
          border-bottom-color: var(--fill-color);
          border-left-color: var(--fill-color);
        }
        20% {
          border-color: transparent;
          border-left-color: var(--fill-color);
          border-top-color: var(--fill-color);
        }
        30% {
          border-color: transparent;
          border-left-color: var(--fill-color);
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          border-color: transparent;
          border-right-color: var(--fill-color);
          border-top-color: var(--fill-color);
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        60% {
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          border-right-color: var(--fill-color);
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        80% {
          border-color: transparent;
          border-right-color: var(--fill-color);
          border-bottom-color: var(--fill-color);
        }
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          border-color: transparent;
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      <script>
        async function quickchart(key) {
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                'suggestCharts', [key], {});
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            console.error('Error during call to suggestCharts:', error);
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      <div id="id_1b57c727-f70a-4635-99e8-9c6ae8ac2f79">
        <style>
          .colab-df-generate {
            background-color: #E8F0FE;
            border: none;
            border-radius: 50%;
            cursor: pointer;
            display: none;
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          .colab-df-generate:hover {
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          [theme=dark] .colab-df-generate {
            background-color: #3B4455;
            fill: #D2E3FC;
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          [theme=dark] .colab-df-generate:hover {
            background-color: #434B5C;
            box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);
            filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));
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        <button class="colab-df-generate" onclick="generateWithVariable('result')"
                title="Generate code using this dataframe."
                style="display:none;">
    
      <svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24"
           width="24px">
        <path d="M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z"/>
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        </button>
        <script>
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          const buttonEl =
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          buttonEl.style.display =
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          buttonEl.onclick = () => {
            google.colab.notebook.generateWithVariable('result');
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          })();
        </script>
      </div>
    
        </div>
      </div>
    



Plot results
---------

Plot spectra of interest using matchms
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    import matplotlib.pyplot as plt
    print(mz_irt_df.iloc[19])
    print(mz_irt_df.iloc[36])
    spectra[19].plot_against(spectra[36])
    plt.savefig('spectra_comparison.png')


.. parsed-literal::

    Name    MRIGTPEPWSTQSDKR/2
    MW              944.970342
    iRT              41.258202
    Name: 19, dtype: object
    Name    QAIMSISYHSCYNMFR/2
    MW              975.936599
    iRT              93.540787
    Name: 36, dtype: object