Delete proteins_viz.py

proteins_viz.py

@@ -1,136 +0,0 @@
-import pandas as pd
-from biopandas.pdb import PandasPdb
-from prody import parsePDBHeader
-
-
-
-
-def read_pdb_to_dataframe(
-    pdb_path,
-    model_index: int = 1,
-    parse_header: bool = True,
-    ) -> pd.DataFrame:
-    """
-    Read a PDB file, and return a Pandas DataFrame containing the atomic coordinates and metadata.
-
-    Args:
-        pdb_path (str, optional): Path to a local PDB file to read. Defaults to None.
-        model_index (int, optional): Index of the model to extract from the PDB file, in case
-            it contains multiple models. Defaults to 1.
-        parse_header (bool, optional): Whether to parse the PDB header and extract metadata.
-            Defaults to True.
-
-    Returns:
-        pd.DataFrame: A DataFrame containing the atomic coordinates and metadata, with one row
-            per atom
-    """
-    atomic_df = PandasPdb().read_pdb(pdb_path)
-    if parse_header:
-        header = parsePDBHeader(pdb_path)
-    else:
-        header = None
-    atomic_df = atomic_df.get_model(model_index)
-    if len(atomic_df.df["ATOM"]) == 0:
-        raise ValueError(f"No model found for index: {model_index}")
-
-    return pd.concat([atomic_df.df["ATOM"], atomic_df.df["HETATM"]]), header
-
-from graphein.protein.graphs import label_node_id
-
-def process_dataframe(df: pd.DataFrame, granularity='CA') -> pd.DataFrame:
-    """
-    Process a DataFrame of protein structure data to reduce ambiguity and simplify analysis.
-
-    This function performs the following steps:
-    1. Handles alternate locations for an atom, defaulting to keep the first one if multiple exist.
-    2. Assigns a unique node_id to each residue in the DataFrame, using a helper function label_node_id.
-    3. Filters the DataFrame based on specified granularity (defaults to 'CA' for alpha carbon).
-
-    Parameters
-    ----------
-    df : pd.DataFrame
-        The DataFrame containing protein structure data to process. It is expected to contain columns 'alt_loc' and 'atom_name'.
-        
-    granularity : str, optional
-        The level of detail or perspective at which the DataFrame should be analyzed. Defaults to 'CA' (alpha carbon).
-    """
-    # handle the case of alternative locations,
-    # if so default to the 1st one = A
-    if 'alt_loc' in df.columns:
-      df['alt_loc'] = df['alt_loc'].replace('', 'A')
-      df = df.loc[(df['alt_loc']=='A')]
-    df = label_node_id(df, granularity)
-    df = df.loc[(df['atom_name']==granularity)]
-    return df
-
-
-from graphein.protein.graphs import initialise_graph_with_metadata
-from graphein.protein.graphs import add_nodes_to_graph
-from graphein.protein.visualisation import plotly_protein_structure_graph
-from PIL import Image
-import networkx as nx
-
-def take_care(pdb_path):
-    
-
-    df, header = read_pdb_to_dataframe(pdb_path)
-    process_df = process_dataframe(df)
-
-    g = initialise_graph_with_metadata(protein_df=process_df, # from above cell
-                                        raw_pdb_df=df, # Store this for traceability
-                                        pdb_code = '3nir', #and again
-                                        granularity = 'CA' # Store this so we know what kind of graph we have
-                                        )
-    g = add_nodes_to_graph(g)
-
-    
-    def add_backbone_edges(G: nx.Graph) -> nx.Graph:
-        # Iterate over every chain
-        for chain_id in G.graph["chain_ids"]:
-            # Find chain residues
-            chain_residues = [
-                (n, v) for n, v in G.nodes(data=True) if v["chain_id"] == chain_id
-            ]
-            # Iterate over every residue in chain
-            for i, residue in enumerate(chain_residues):
-                try:
-                    # Checks not at chain terminus
-                    if i == len(chain_residues) - 1:
-                        continue
-                    # Asserts residues are on the same chain
-                    cond_1 = ( residue[1]["chain_id"] == chain_residues[i + 1][1]["chain_id"])
-                    # Asserts residue numbers are adjacent
-                    cond_2 = (abs(residue[1]["residue_number"] - chain_residues[i + 1][1]["residue_number"])== 1)
-
-                    # If this checks out, we add a peptide bond
-                    if (cond_1) and (cond_2):
-                        # Adds "peptide bond" between current residue and the next
-                        if G.has_edge(i, i + 1):
-                            G.edges[i, i + 1]["kind"].add('backbone_bond')
-                        else:
-                            G.add_edge(residue[0],chain_residues[i + 1][0],kind={'backbone_bond'},)
-                except IndexError as e:
-                    print(e)
-        return G
-
-    g = add_backbone_edges(g)
-
-    
-
-    p = plotly_protein_structure_graph(
-        g,
-        colour_edges_by="kind",
-        colour_nodes_by="seq_position",
-        label_node_ids=False,
-        plot_title="Backbone Protein Graph",
-        node_size_multiplier=1,
-    )
-    image_file = "protein_graph.png"
-    p.write_image(image_file, format='png')
-
-
-    # Load the PNG image into a PIL image
-    image = Image.open(image_file)
-
-    
-    return image