.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/gallery/structure/protein/sheet_arrangement.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_gallery_structure_protein_sheet_arrangement.py: Arrangement of beta-sheets ========================== This scripts plots the arrangements of strands in selected β-sheets of a protein structure. The information is entirely taken from the ``struct_sheet_order`` and ``struct_sheet_range`` categories of the corresponding *PDBx* file in *BinaryCIF* format. In this case the β-barrel of a split fluorescent protein is shown, but the script can be customized to show the β-sheets of any protein you like. You just need to adjust the options shown below. .. GENERATED FROM PYTHON SOURCE LINES 16-61 .. code-block:: Python # Code source: Patrick Kunzmann # License: BSD 3 clause import matplotlib.pyplot as plt import networkx as nx import numpy as np from matplotlib.patches import FancyArrow import biotite import biotite.database.rcsb as rcsb import biotite.structure.io.pdbx as pdbx ##### OPTIONS ##### PDB_ID = "3AKO" SHEETS = ["A"] FIG_SIZE = (8.0, 4.0) # Figure size in inches Y_LIMIT = 2.0 # Vertical plot limits SHEET_DISTANCE = 3.0 # Separation of strands in different sheets ARROW_TAIL_WITH = 0.4 # Width of the arrow tails ARROW_HEAD_WITH = 0.7 # Width of the arrow heads ARROW_HEAD_LENGTH = 0.25 # Length of the arrow heads ARROW_LINE_WIDTH = 1 # Width of the arrow edges ARROW_COLORS = [ # Each chain is colored differently biotite.colors["darkgreen"], biotite.colors["dimorange"], biotite.colors["lightgreen"], biotite.colors["brightorange"], ] CONNECTION_COLOR = "black" # Color of the connection lines CONNECTION_LINE_WIDTH = 1.5 # Width of the connection lines CONNECTION_HEIGHT = 0.1 # Minimum height of the connection lines CONNECTION_SEPARATION = 0.1 # Minimum vertical distance between the connection lines RES_ID_HEIGHT = ( -0.2 ) # The vertical distance of the residue ID labels from the arrow ends RES_ID_FONT_SIZE = 8 # The font size of the residue ID labels RES_ID_FONT_WEIGHT = "bold" # The font weight of the residue ID labels ADAPTIVE_ARROW_LENGTHS = ( True # If true, the arrow length is proportional to the number of its residues ) SHOW_SHEET_NAMES = False # If true, the sheets are labeled below the plot SHEET_NAME_FONT_SIZE = 14 # The font size of the sheet labels ##### SNOITPO ##### .. GENERATED FROM PYTHON SOURCE LINES 62-70 The ``struct_sheet_order`` category of the *BinaryCIF* file gives us the information about the existing sheets, the strands these sheets contain and which of these strands are connected with one another in either parallel or anti-parallel orientation. We can use this to select only strands that belong to those sheets, we are interested in. The strand adjacency and relative orientation is also saved for later. .. GENERATED FROM PYTHON SOURCE LINES 70-98 .. code-block:: Python bcif_file = pdbx.BinaryCIFFile.read(rcsb.fetch(PDB_ID, "bcif")) sheet_order = bcif_file.block["struct_sheet_order"] # Create a boolean mask that covers the selected sheets # or all sheets if none is given if SHEETS is None: sele = np.full(sheet_order.row_count, True) else: sele = np.array([sheet in SHEETS for sheet in sheet_order["sheet_id"].as_array()]) sheet_ids = sheet_order["sheet_id"].as_array()[sele] is_parallel_list = sheet_order["sense"].as_array()[sele] == "parallel" adjacent_strands = np.array( [ (strand_i, strand_j) for strand_i, strand_j in zip( sheet_order["range_id_1"].as_array()[sele], sheet_order["range_id_2"].as_array()[sele], ) ] ) print("Adjacent strands (sheet ID, strand ID):") for sheet_id, (strand_i, strand_j) in zip(sheet_ids, adjacent_strands): print(f"{sheet_id, strand_i} <-> {sheet_id, strand_j}") .. rst-class:: sphx-glr-script-out .. code-block:: none Adjacent strands (sheet ID, strand ID): (np.str_('A'), np.int32(1)) <-> (np.str_('A'), np.int32(2)) (np.str_('A'), np.int32(2)) <-> (np.str_('A'), np.int32(3)) (np.str_('A'), np.int32(3)) <-> (np.str_('A'), np.int32(4)) (np.str_('A'), np.int32(4)) <-> (np.str_('A'), np.int32(5)) (np.str_('A'), np.int32(5)) <-> (np.str_('A'), np.int32(6)) (np.str_('A'), np.int32(6)) <-> (np.str_('A'), np.int32(7)) (np.str_('A'), np.int32(7)) <-> (np.str_('A'), np.int32(8)) (np.str_('A'), np.int32(8)) <-> (np.str_('A'), np.int32(9)) (np.str_('A'), np.int32(9)) <-> (np.str_('A'), np.int32(10)) (np.str_('A'), np.int32(10)) <-> (np.str_('A'), np.int32(11)) (np.str_('A'), np.int32(11)) <-> (np.str_('A'), np.int32(12)) .. GENERATED FROM PYTHON SOURCE LINES 99-108 The ``struct_sheet_range`` category of the *mmCIF* file tells us which residues compose each strand in terms of chain and residue IDs. Later the plot shall display connections between consecutive strands in a protein chain. Although, this category does not provide this connection information directly, we can sort the strands by their beginning chain and residue IDs and then simply connect successive entries. .. GENERATED FROM PYTHON SOURCE LINES 108-169 .. code-block:: Python sheet_range = bcif_file.block["struct_sheet_range"] # Again, create a boolean mask that covers the selected sheets sele = np.array([sheet in sheet_ids for sheet in sheet_range["sheet_id"].as_array()]) strand_chain_ids = sheet_range["beg_auth_asym_id"].as_array()[sele] strand_res_id_begs = sheet_range["beg_auth_seq_id"].as_array(int)[sele] strand_res_id_ends = sheet_range["end_auth_seq_id"].as_array(int)[sele] # Secondarily sort by residue ID order = np.argsort(strand_res_id_begs, kind="stable") # Primarily sort by chain ID order = order[np.argsort(strand_chain_ids[order], kind="stable")] sorted_strand_ids = sheet_range["id"].as_array()[sele][order] sorted_sheet_ids = sheet_range["sheet_id"].as_array()[sele][order] sorted_chain_ids = strand_chain_ids[order] sorted_res_id_begs = strand_res_id_begs[order] sorted_res_id_ends = strand_res_id_ends[order] # Remove duplicate entries, # i.e. entries with the same chain ID and residue ID # Duplicate entries appear e.g. in beta-barrel structure files # Draw one of each duplicate as orphan -> no connections non_duplicate_mask = np.diff(strand_res_id_begs[order], prepend=[-1]) != 0 connections = [] non_duplicate_indices = np.arange(len(sorted_strand_ids))[non_duplicate_mask] for i in range(len(non_duplicate_indices) - 1): current_i = non_duplicate_indices[i] next_i = non_duplicate_indices[i + 1] if sorted_chain_ids[current_i] != sorted_chain_ids[next_i]: # No connection between separate chains continue connections.append( ( (sorted_sheet_ids[current_i], sorted_strand_ids[current_i]), (sorted_sheet_ids[next_i], sorted_strand_ids[next_i]), ) ) print("Connected strands (sheet ID, strand ID):") for strand_i, strand_j in connections: print(f"{strand_i} -> {strand_j}") # Save the start and end residue IDs for each strand for labeling ranges = { (sheet_id, strand_id): (begin, end) for sheet_id, strand_id, begin, end in zip( sorted_sheet_ids, sorted_strand_ids, sorted_res_id_begs, sorted_res_id_ends ) } # Save the chains ID for each strand for coloring chain_ids = { (sheet_id, strand_id): chain_id for sheet_id, strand_id, chain_id in zip( sorted_sheet_ids, sorted_strand_ids, sorted_chain_ids ) } unique_chain_ids = np.unique(sorted_chain_ids) .. rst-class:: sphx-glr-script-out .. code-block:: none Connected strands (sheet ID, strand ID): (np.str_('A'), np.int32(1)) -> (np.str_('A'), np.int32(2)) (np.str_('A'), np.int32(2)) -> (np.str_('A'), np.int32(3)) (np.str_('A'), np.int32(3)) -> (np.str_('A'), np.int32(9)) (np.str_('A'), np.int32(9)) -> (np.str_('A'), np.int32(10)) (np.str_('A'), np.int32(10)) -> (np.str_('A'), np.int32(11)) (np.str_('A'), np.int32(11)) -> (np.str_('A'), np.int32(6)) (np.str_('A'), np.int32(7)) -> (np.str_('A'), np.int32(8)) (np.str_('A'), np.int32(8)) -> (np.str_('A'), np.int32(5)) (np.str_('A'), np.int32(5)) -> (np.str_('A'), np.int32(4)) .. GENERATED FROM PYTHON SOURCE LINES 170-178 So far we only know which strands to plot adjacent to each other, but we still need to determine the position in the plot for each strand. For this purpose we will later use one of *NetworkX*'s layouting algorithms. For now the information about the adjacent strands is stored in a *NetworkX* graph, one for each sheet: The strand IDs are nodes and the adjacency is represented by edges. The relative strand orientation is stored as edge attribute. .. GENERATED FROM PYTHON SOURCE LINES 178-192 .. code-block:: Python sheet_graphs = {} for sheet_id in np.unique(sheet_ids): # Select only strands from the current sheet sheet_mask = sheet_ids == sheet_id sheet_graphs[sheet_id] = nx.Graph( [ (strand_i, strand_j, {"is_parallel": is_parallel}) for (strand_i, strand_j), is_parallel in zip( adjacent_strands[sheet_mask], is_parallel_list[sheet_mask] ) ] ) .. GENERATED FROM PYTHON SOURCE LINES 193-204 Another missing information is the direction of the plotted arrows, we only know their relative orientations. To solve this, we initially let the arrow for the first strand of each sheet point upwards and then iteratively determine the direction of the other arrows from the relative orientations. For example, strand ``'1'`` is set to point upward, strand ``'2'`` is anti-parallel to strand ``'1'``, so it points downward, strand ``'3'`` is parallel to strand ``'2'`` so it points also downward. The calculated arrow direction is stored as node attribute. .. GENERATED FROM PYTHON SOURCE LINES 204-229 .. code-block:: Python for graph in sheet_graphs.values(): initial_strand = adjacent_strands[0, 0] graph.nodes[initial_strand]["is_upwards"] = True for strand in graph.nodes: if strand == initial_strand: continue this_strand_is_upwards = [] for adj_strand in graph.neighbors(strand): is_upwards = graph.nodes[adj_strand].get("is_upwards") if is_upwards is None: # The arrow direction for this adjacent strand is not # yet determined continue is_parallel = graph.edges[(strand, adj_strand)]["is_parallel"] this_strand_is_upwards.append(is_upwards ^ ~is_parallel) if len(this_strand_is_upwards) == 0: raise ValueError("Cannot determine arrow direction from adjacent strands") elif all(this_strand_is_upwards): graph.nodes[strand]["is_upwards"] = True elif not any(this_strand_is_upwards): graph.nodes[strand]["is_upwards"] = False else: raise ValueError("Conflicting arrow directions from adjacent strands") .. GENERATED FROM PYTHON SOURCE LINES 230-231 No we have got all positioning information we need to start plotting. .. GENERATED FROM PYTHON SOURCE LINES 231-391 .. code-block:: Python fig, ax = plt.subplots(figsize=FIG_SIZE) ### Plot arrows MAX_ARROW_LENGTH = 2 # from y=-1 to y=1 arrow_length_per_seq_length = MAX_ARROW_LENGTH / np.max( [end - beg + 1 for beg, end in ranges.values()] ) # The coordinates of the arrow ends are stored in this dictionary # for each strand, accessed via a tuple of sheet and strand ID coord_dict = {} current_position = 0 # Plot each sheet separately, # the start position of each sheet is given by 'current_position' for sheet_id, graph in sheet_graphs.items(): # Use *NetworkX*'s layouting algorithm to find the arrow positions # As we arrange the sheets along the x-axis, # there is only one dimension positions = nx.kamada_kawai_layout(graph, dim=1) strand_ids = np.array(list(positions.keys())) positions = np.array(list(positions.values())) # Each position has only one dimension # -> Remove the last dimension positions = positions[:, 0] # Transform positions to achieve a spacing of at least 1.0 dist_matrix = np.abs(positions[:, np.newaxis] - positions[np.newaxis, :]) positions /= np.min(dist_matrix[dist_matrix != 0]) # Transform positions, so that they start at 'current_position' positions -= np.min(positions) positions += np.min(current_position) current_position = np.max(positions) + SHEET_DISTANCE # Draw an arrow for each strand for strand_id, pos in zip(strand_ids, positions): chain_id = chain_ids[sheet_id, strand_id] color_index = unique_chain_ids.tolist().index(chain_id) if ADAPTIVE_ARROW_LENGTHS: beg, end = ranges[sheet_id, strand_id] seq_length = end - beg + 1 arrow_length = arrow_length_per_seq_length * seq_length else: arrow_length = MAX_ARROW_LENGTH if graph.nodes[strand_id]["is_upwards"]: y = -arrow_length / 2 dy = arrow_length else: y = arrow_length / 2 dy = -arrow_length ax.add_patch( FancyArrow( x=pos, y=y, dx=0, dy=dy, length_includes_head=True, width=ARROW_TAIL_WITH, head_width=ARROW_HEAD_WITH, head_length=ARROW_HEAD_LENGTH, facecolor=ARROW_COLORS[color_index % len(ARROW_COLORS)], edgecolor=CONNECTION_COLOR, linewidth=ARROW_LINE_WIDTH, ) ) # Start and end coordinates of the respective arrow coord_dict[sheet_id, strand_id] = ((pos, y), (pos, y + dy)) ### Plot connections # Each connection is plotted on a different height in order to keep them # separable # Plot the short connections at low height # to decrease line intersections # -> sort connections by length of connection order = np.argsort( [ np.abs(coord_dict[strand_i][0][0] - coord_dict[strand_j][0][0]) for strand_i, strand_j in connections ] ) connections = [connections[i] for i in order] for i, (strand_i, strand_j) in enumerate(connections): horizontal_line_height = 1 + CONNECTION_HEIGHT + i * CONNECTION_SEPARATION coord_i_beg, coord_i_end = coord_dict[strand_i] coord_j_beg, coord_j_end = coord_dict[strand_j] if np.sign(coord_i_end[1]) == np.sign(coord_j_beg[1]): # Start and end are on the same side of the arrows x = (coord_i_end[0], coord_i_end[0], coord_j_beg[0], coord_j_beg[0]) y = ( coord_i_end[1], np.sign(coord_i_end[1]) * horizontal_line_height, np.sign(coord_j_beg[1]) * horizontal_line_height, coord_j_beg[1], ) else: # Start and end are on different sides offset = 0.4 if coord_j_beg[0] >= coord_i_end[0] else -0.4 x = ( coord_i_end[0], coord_i_end[0], coord_i_end[0] + offset, coord_i_end[0] + offset, coord_j_beg[0], coord_j_beg[0], ) y = ( coord_i_end[1], np.sign(coord_i_end[1]) * horizontal_line_height, np.sign(coord_i_end[1]) * horizontal_line_height, np.sign(coord_j_beg[1]) * horizontal_line_height, np.sign(coord_j_beg[1]) * horizontal_line_height, coord_j_beg[1], ) ax.plot( x, y, color=CONNECTION_COLOR, linewidth=CONNECTION_LINE_WIDTH, # Avoid intersection of the line's end with the arrow solid_capstyle="butt", ) ### Plot residue ID labels for strand, (res_id_beg, res_id_end) in ranges.items(): coord_beg, coord_end = coord_dict[strand] for coord, res_id in zip((coord_beg, coord_end), (res_id_beg, res_id_end)): ax.text( coord[0], np.sign(coord[1]) * (np.abs(coord[1]) + RES_ID_HEIGHT), str(res_id), ha="center", va="center", fontsize=RES_ID_FONT_SIZE, weight=RES_ID_FONT_WEIGHT, ) ### Plot sheet names as x-axis ticks if SHOW_SHEET_NAMES: tick_pos = [ np.mean([coord_dict[key][0][0] for key in coord_dict if key[0] == sheet_id]) for sheet_id in sheet_ids ] ax.set_xticks(tick_pos) ax.set_xticklabels([f"Sheet {sheet_id}" for sheet_id in sheet_ids]) ax.set_frame_on(False) ax.yaxis.set_visible(False) ax.xaxis.set_tick_params( bottom=False, top=False, labelbottom=True, labeltop=False, labelsize=SHEET_NAME_FONT_SIZE, ) else: ax.axis("off") ax.set_xlim(-1, current_position - SHEET_DISTANCE + 1) ax.set_ylim(-Y_LIMIT, Y_LIMIT) fig.tight_layout() plt.show() .. image-sg:: /examples/gallery/structure/protein/images/sphx_glr_sheet_arrangement_001.png :alt: sheet arrangement :srcset: /examples/gallery/structure/protein/images/sphx_glr_sheet_arrangement_001.png :class: sphx-glr-single-img .. _sphx_glr_download_examples_gallery_structure_protein_sheet_arrangement.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: sheet_arrangement.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: sheet_arrangement.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_