Repository: a-r-j/graphein (language: python)

Issue:
Add sidechain vector node feature
**Is your feature request related to a problem? Please describe.**
Add vector from node (CA, centroid, etc) to the centre of mass of the sidechain R-Group as node feature.



Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 08b1a229..9c0cc44c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,12 @@
+### 1.2.1 - UNRELEASED
+
+* [Feature] - #124 adds support for vector features associated protein protein geometry. #120 #122
+* [Feature] - #124 adds visualisation of vector features in 3D graph plots.
+
+#### Breaking Changes
+
+* #124 refactors `graphein.protein.graphs.compute_rgroup_dataframe` and moves it to `graphein.protein.utils`. All internal references have been moved accordingly.
+
 ### 1.2.0 - 4/3/2022
 
 * [Feature] - #104 adds support for asteroid plots and distance matrix visualisation.
diff --git a/docs/source/modules/graphein.protein.rst b/docs/source/modules/graphein.protein.rst
index 208c6191..27819e14 100644
--- a/docs/source/modules/graphein.protein.rst
+++ b/docs/source/modules/graphein.protein.rst
@@ -39,6 +39,8 @@ Node
     :members:
 .. automodule:: graphein.protein.features.nodes.dssp
     :members:
+.. automodule:: graphein.protein.features.nodes.geometry
+    :members:
 
 Sequence
 ^^^^^^^^^
diff --git a/graphein/protein/features/nodes/__init__.py b/graphein/protein/features/nodes/__init__.py
index 91e32eaa..0e2ab1d0 100644
--- a/graphein/protein/features/nodes/__init__.py
+++ b/graphein/protein/features/nodes/__init__.py
@@ -1,2 +1,3 @@
 from .amino_acid import *
 from .dssp import *
+from .geometry import *
diff --git a/graphein/protein/features/nodes/geometry.py b/graphein/protein/features/nodes/geometry.py
new file mode 100644
index 00000000..c4a4363a
--- /dev/null
+++ b/graphein/protein/features/nodes/geometry.py
@@ -0,0 +1,169 @@
+"""Provides geometry-based featurisation functions."""
+# Graphein
+# Author: Arian Jamasb <arian@jamasb.io>
+# License: MIT
+# Project Website: https://github.com/a-r-j/graphein
+# Code Repository: https://github.com/a-r-j/graphein
+import logging
+
+import networkx as nx
+import numpy as np
+import pandas as pd
+
+from graphein.protein.utils import filter_dataframe
+
+
+def add_sidechain_vector(
+    g: nx.Graph, scale: bool = True, reverse: bool = False
+):
+    """Adds vector from node to average position of sidechain atoms.
+
+    We compute the mean of the sidechain atoms for each node. For this we use the ``rgroup_df`` dataframe.
+    If the graph does not contain the ``rgroup_df`` dataframe, we compute it from the ``raw_pdb_df``.
+    If scale, we scale the vector to the unit vector. If reverse is True,
+    we reverse the vector (``sidechain - node``). If reverse is false (default) we compute (``node - sidechain``).
+
+    :param g: Graph to add vector to.
+    :type g: nx.Graph
+    :param scale: Scale vector to unit vector. Defaults to ``True``.
+    :type scale: bool
+    :param reverse: Reverse vector. Defaults to ``False``.
+    :type reverse: bool
+    """
+    # Get or compute R-Group DF
+    if "rgroup_df" not in g.graph.keys():
+        g.graph["rgroup_df"] = compute_rgroup_dataframe(g.graph["raw_pdb_df"])
+
+    sc_centroid = g.graph["rgroup_df"].groupby("node_id").mean()
+
+    # Iterate over nodes and compute vector
+    for n, d in g.nodes(data=True):
+        if d["residue_name"] == "GLY":
+            # If GLY, set vector to 0
+            vec = np.array([0, 0, 0])
+        else:
+            if reverse:
+                vec = d["coords"] - np.array(
+                    sc_centroid.loc[n][["x_coord", "y_coord", "z_coord"]]
+                )
+            else:
+                vec = (
+                    np.array(
+                        sc_centroid.loc[n][["x_coord", "y_coord", "z_coord"]]
+                    )
+                    - d["coords"]
+                )
+
+            if scale:
+                vec = vec / np.linalg.norm(vec)
+
+        d["sidechain_vector"] = vec
+
+
+def add_beta_carbon_vector(
+    g: nx.Graph, scale: bool = True, reverse: bool = False
+):
+    """Adds vector from node (typically alpha carbon) to position of beta carbon.
+
+    Glycine does not have a beta carbon, so we set it to ``np.array([0, 0, 0])``.
+    We extract the position of the beta carbon from the unprocessed atomic PDB dataframe.
+    For this we use the ``raw_pdb_df`` dataframe.
+    If scale, we scale the vector to the unit vector. If reverse is True,
+    we reverse the vector (``C beta - node``). If reverse is false (default) we compute (``node - C beta``).
+
+    :param g: Graph to add vector to.
+    :type g: nx.Graph
+    :param scale: Scale vector to unit vector. Defaults to ``True``.
+    :type scale: bool
+    :param reverse: Reverse vector. Defaults to ``False``.
+    :type reverse: bool
+    """
+
+    c_beta_coords = filter_dataframe(
+        g.graph["raw_pdb_df"], "atom_name", ["CB"], boolean=True
+    )
+    c_beta_coords.index = c_beta_coords["node_id"]
+
+    # Iterate over nodes and compute vector
+    for n, d in g.nodes(data=True):
+        if d["residue_name"] == "GLY":
+            vec = np.array([0, 0, 0])
+        else:
+            if reverse:
+                vec = d["coords"] - np.array(
+                    c_beta_coords.loc[n][["x_coord", "y_coord", "z_coord"]]
+                )
+            else:
+                vec = (
+                    np.array(
+                        c_beta_coords.loc[n][["x_coord", "y_coord", "z_coord"]]
+                    )
+                    - d["coords"]
+                )
+
+            if scale:
+                vec = vec / np.linalg.norm(vec)
+        d["c_beta_vector"] = vec
+
+
+def add_sequence_neighbour_vector(
+    g: nx.Graph, scale: bool = True, reverse: bool = False, n_to_c: bool = True
+):
+    """Computes vector from node to adjacent node in sequence.
+    Typically used with ``CA`` (alpha carbon) graphs.
+
+    If ``n_to_c`` is ``True`` (default), we compute the vectors from the N terminus to the C terminus (canonical direction).
+    If ``reverse`` is ``False`` (default), we compute ``Node_i - Node_{i+1}``.
+    If ``reverse is ``True``, we compute ``Node_{i+1} - Node_i``.
+    :param g: Graph to add vector to.
+    :type g: nx.Graph
+    :param scale: Scale vector to unit vector. Defaults to ``True``.
+    :type scale: bool
+    :param reverse: Reverse vector. Defaults to ``False``.
+    :type reverse: bool
+    :param n_to_c: Compute vector from N to C or C to N. Defaults to ``True``.
+    :type n_to_c: bool
+    """
+    suffix = "n_to_c" if n_to_c else "c_to_n"
+    # 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
+        ]
+
+        if not n_to_c:
+            chain_residues.reverse()
+
+        # Iterate over every residue in chain
+        for i, residue in enumerate(chain_residues):
+            # Checks not at chain terminus - is this versatile enough?
+            if i == len(chain_residues) - 1:
+                residue[1][f"sequence_neighbour_vector_{suffix}"] = np.array(
+                    [0, 0, 0]
+                )
+                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 compute the vector
+            if (cond_1) and (cond_2):
+                vec = chain_residues[i + 1][1]["coords"] - residue[1]["coords"]
+
+                if reverse:
+                    vec = -vec
+                if scale:
+                    vec = vec / np.linalg.norm(vec)
+
+            residue[1][f"sequence_neighbour_vector_{suffix}"] = vec
diff --git a/graphein/protein/graphs.py b/graphein/protein/graphs.py
index c6d4c3d2..62baab26 100644
--- a/graphein/protein/graphs.py
+++ b/graphein/protein/graphs.py
@@ -24,6 +24,7 @@
 from graphein.protein.edges.distance import compute_distmat
 from graphein.protein.resi_atoms import BACKBONE_ATOMS
 from graphein.protein.utils import (
+    compute_rgroup_dataframe,
     filter_dataframe,
     get_protein_name_from_filename,
     three_to_one_with_mods,
@@ -189,17 +190,6 @@ def filter_hetatms(
     return [df.loc[df["residue_name"] == hetatm] for hetatm in keep_hets]
 
 
-def compute_rgroup_dataframe(pdb_df: pd.DataFrame) -> pd.DataFrame:
-    """Return the atoms that are in R-groups and not the backbone chain.
-
-    :param pdb_df: DataFrame to compute R group dataframe from.
-    :type pdb_df: pd.DataFrame
-    :returns: Dataframe containing R-groups only (backbone atoms removed).
-    :rtype: pd.DataFrame
-    """
-    return filter_dataframe(pdb_df, "atom_name", BACKBONE_ATOMS, False)
-
-
 def process_dataframe(
     protein_df: pd.DataFrame,
     atom_df_processing_funcs: Optional[List[Callable]] = None,
diff --git a/graphein/protein/utils.py b/graphein/protein/utils.py
index f10118b7..13b472f5 100644
--- a/graphein/protein/utils.py
+++ b/graphein/protein/utils.py
@@ -16,7 +16,7 @@
 import wget
 from Bio.PDB import PDBList
 
-from .resi_atoms import RESI_THREE_TO_1
+from .resi_atoms import BACKBONE_ATOMS, RESI_THREE_TO_1
 
 log = logging.getLogger(__name__)
 
@@ -105,6 +105,17 @@ def filter_dataframe(
     return df
 
 
+def compute_rgroup_dataframe(pdb_df: pd.DataFrame) -> pd.DataFrame:
+    """Return the atoms that are in R-groups and not the backbone chain.
+
+    :param pdb_df: DataFrame to compute R group dataframe from.
+    :type pdb_df: pd.DataFrame
+    :returns: Dataframe containing R-groups only (backbone atoms removed).
+    :rtype: pd.DataFrame
+    """
+    return filter_dataframe(pdb_df, "atom_name", BACKBONE_ATOMS, False)
+
+
 def download_alphafold_structure(
     uniprot_id: str,
     out_dir: str = ".",
diff --git a/graphein/protein/visualisation.py b/graphein/protein/visualisation.py
index 4ea58f5e..e3fec612 100644
--- a/graphein/protein/visualisation.py
+++ b/graphein/protein/visualisation.py
@@ -406,6 +406,106 @@ def plot_protein_structure_graph(
     return ax
 
 
+def add_vector_to_plot(
+    g: nx.Graph,
+    fig,
+    vector: str = "sidechain_vector",
+    scale: float = 5,
+    colour: str = "red",
+    width: int = 10,
+) -> go.Figure:
+    """Adds representations of vector features to the protein graph.
+
+    Requires all nodes have a vector feature (1 x 3 array).
+
+    :param g: Protein graph containing vector features
+    :type g: nx.Graph
+    :param fig: 3D plotly figure to add vectors to.
+    :type fig: go.Figure
+    :param vector: Name of node vector feature to add, defaults to "sidechain_vector"
+    :type vector: str, optional
+    :param scale: How much to scale the vectors by, defaults to 5
+    :type scale: float, optional
+    :param colour: Colours for vectors, defaults to "red"
+    :type colour: str, optional
+    :return: 3D Plotly plot with vectors added.
+    :rtype: go.Figure
+    """
+    # Compute line segment positions
+    x_edges = []
+    y_edges = []
+    z_edges = []
+    edge_text = []
+    for _, d in g.nodes(data=True):
+        x_edges.extend(
+            [d["coords"][0], d["coords"][0] + d[vector][0] * scale, None]
+        )
+        y_edges.extend(
+            [d["coords"][1], d["coords"][1] + d[vector][1] * scale, None]
+        )
+        z_edges.extend(
+            [d["coords"][2], d["coords"][2] + d[vector][2] * scale, None]
+        )
+        edge_text.extend([None, f"{vector}", None])
+
+    edge_trace = go.Scatter3d(
+        x=x_edges,
+        y=y_edges,
+        z=z_edges,
+        mode="lines",
+        line={"color": colour, "width": width},
+        text=3 * [f"{vector}" for _ in range(len(g))],
+        hoverinfo="text",
+    )
+    # Compute cone positions.
+    arrow_tip_ratio = 0.1
+    arrow_starting_ratio = 0.98
+    x = []
+    y = []
+    z = []
+    u = []
+    v = []
+    w = []
+    for _, d in g.nodes(data=True):
+        x.extend(
+            [d["coords"][0] + d[vector][0] * scale * arrow_starting_ratio]
+        )
+        y.extend(
+            [d["coords"][1] + d[vector][1] * scale * arrow_starting_ratio]
+        )
+        z.extend(
+            [d["coords"][2] + d[vector][2] * scale * arrow_starting_ratio]
+        )
+        u.extend([d[vector][0]])  # * arrow_tip_ratio])
+        v.extend([d[vector][1]])  # * arrow_tip_ratio])
+        w.extend([d[vector][2]])  # * arrow_tip_ratio])
+
+    if colour == "red":
+        colour = [[0, "rgb(255,0,0)"], [1, "rgb(255,0,0)"]]
+    elif colour == "blue":
+        colour = [[0, "rgb(0,0,255)"], [1, "rgb(0,0,255)"]]
+    elif colour == "green":
+        colour = [[0, "rgb(0,255,0)"], [1, "rgb(0,255,0)"]]
+
+    cone_trace = go.Cone(
+        x=x,
+        y=y,
+        z=z,
+        u=u,
+        v=v,
+        w=w,
+        text=[f"{vector}" for _ in range(len(g.nodes()))],
+        hoverinfo="u+v+w+text",
+        colorscale=colour,
+        showlegend=False,
+        showscale=False,
+        sizemode="absolute",
+    )
+    fig.add_trace(edge_trace)
+    fig.add_trace(cone_trace)
+    return fig
+
+
 def plot_distance_matrix(
     g: Optional[nx.Graph],
     dist_mat: Optional[np.ndarray] = None,
diff --git a/notebooks/residue_graphs.ipynb b/notebooks/residue_graphs.ipynb
index a4cf3e56..c5030efb 100644
--- a/notebooks/residue_graphs.ipynb
+++ b/notebooks/residue_graphs.ipynb
@@ -13,18 +13,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Save: wrote \"/tmp/1ldf.obj\".\n",
-      " Save: wrote \"/tmp/1ldf.obj\".\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Install Graphein if necessary\n",
     "# !pip install graphein\n",
@@ -77,28 +68,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "To use the Graphein submodule graphein.protein.features.sequence.embeddings, you need to install biovec.\n",
-      "\n",
-      "biovec cannot be installed via conda\n",
-      "To use the Graphein submodule graphein.protein.visualisation, you need to install pytorch3d.\n",
-      "\n",
-      "To do so, use the following command:\n",
-      "\n",
-      "    conda install -c pytorch3d pytorch3d\n",
-      "To use the Graphein submodule graphein.protein.meshes, you need to install pytorch3d.\n",
-      "\n",
-      "To do so, use the following command:\n",
-      "\n",
-      "    conda install -c pytorch3d pytorch3d\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -118,7 +90,7 @@
        " 'dssp_config': None}"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +154,7 @@
        " 'dssp_config': None}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -247,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -8307,7 +8279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -10267,39 +10239,39 @@
          "line": {
           "color": [
            [
-            0.494877,
-            0.01199,
-            0.657865,
+            0.050383,
+            0.029803,
+            0.527975,
             1
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-            0.494877,
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             1
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-            0.494877,
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@@ -10321,183 +10293,201 @@
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@@ -13571,9 +13543,9 @@
           "peptide_bond",
           "peptide_bond",
           "peptide_bond",
-          "hbond / peptide_bond",
-          "hbond / peptide_bond",
-          "hbond / peptide_bond",
+          "peptide_bond / hbond",
+          "peptide_bond / hbond",
+          "peptide_bond / hbond",
           "peptide_bond",
           "peptide_bond",
           "peptide_bond",
@@ -15772,7 +15744,7 @@
         }
        ],
        "layout": {
-        "height": 1000,
+        "height": 650,
         "margin": {
          "t": 100
         },
@@ -16629,7 +16601,7 @@
         "title": {
          "text": "Protein graph with peptide backbone and H-Bonds. \n Nodes coloured by sequence position. Edges coloured by type."
         },
-        "width": 1500
+        "width": 620
        }
       }
      },
@@ -16652,7 +16624,6 @@
     "    label_node_ids=False,\n",
     "    plot_title=\"Protein graph with peptide backbone and H-Bonds. \\n Nodes coloured by sequence position. Edges coloured by type.\",\n",
     "    node_size_multiplier=1,\n",
-    "    figsize=(1500, 1000)\n",
     "    )\n",
     "p.show()"
    ]
@@ -16666,7 +16637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -18629,57 +18600,57 @@
          "line": {
           "color": [
            [
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   },
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   },
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   },
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   },
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    "cell_type": "code",
-   "execution_count": 39,
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    "metadata": {},
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     {
@@ -108598,23 +108569,29 @@
      "text": [
       "DEBUG:graphein.protein.graphs:Deprotonating protein. This removes H atoms from the pdb_df dataframe\n",
       "DEBUG:graphein.protein.graphs:Detected 174 total nodes\n",
-      "Using cache found in /home/atj39/.cache/torch/hub/facebookresearch_esm_master\n",
-      "INFO:gensim.utils:loading Word2Vec object from /home/atj39/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model\n",
-      "DEBUG:smart_open.smart_open_lib:{'uri': '/home/atj39/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model', 'mode': 'rb', 'buffering': -1, 'encoding': None, 'errors': None, 'newline': None, 'closefd': True, 'opener': None, 'ignore_ext': False, 'compression': None, 'transport_params': None}\n",
-      "INFO:gensim.utils:loading vocabulary recursively from /home/atj39/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model.vocabulary.* with mmap=None\n",
-      "INFO:gensim.utils:loading wv recursively from /home/atj39/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model.wv.* with mmap=None\n",
+      "Using cache found in /Users/arianjamasb/.cache/torch/hub/facebookresearch_esm_master\n",
+      "/Users/arianjamasb/opt/anaconda3/envs/graphein-wip/lib/python3.8/site-packages/torchvision/io/image.py:11: UserWarning:\n",
+      "\n",
+      "Failed to load image Python extension: dlopen(/Users/arianjamasb/opt/anaconda3/envs/graphein-wip/lib/python3.8/site-packages/torchvision/image.so, 0x0006): Symbol not found: __ZN2at4_ops19empty_memory_format4callEN3c108ArrayRefIxEENS2_8optionalINS2_10ScalarTypeEEENS5_INS2_6LayoutEEENS5_INS2_6DeviceEEENS5_IbEENS5_INS2_12MemoryFormatEEE\n",
+      "  Referenced from: /Users/arianjamasb/opt/anaconda3/envs/graphein-wip/lib/python3.8/site-packages/torchvision/image.so\n",
+      "  Expected in: /Users/arianjamasb/opt/anaconda3/envs/graphein-wip/lib/python3.8/site-packages/torch/lib/libtorch_cpu.dylib\n",
+      "\n",
+      "INFO:gensim.utils:loading Word2Vec object from /Users/arianjamasb/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model\n",
+      "DEBUG:smart_open.smart_open_lib:{'uri': '/Users/arianjamasb/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model', 'mode': 'rb', 'buffering': -1, 'encoding': None, 'errors': None, 'newline': None, 'closefd': True, 'opener': None, 'ignore_ext': False, 'compression': None, 'transport_params': None}\n",
+      "INFO:gensim.utils:loading vocabulary recursively from /Users/arianjamasb/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model.vocabulary.* with mmap=None\n",
+      "INFO:gensim.utils:loading wv recursively from /Users/arianjamasb/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model.wv.* with mmap=None\n",
       "INFO:gensim.utils:setting ignored attribute vectors_norm to None\n",
-      "INFO:gensim.utils:loading trainables recursively from /home/atj39/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model.trainables.* with mmap=None\n",
+      "INFO:gensim.utils:loading trainables recursively from /Users/arianjamasb/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model.trainables.* with mmap=None\n",
       "INFO:gensim.utils:setting ignored attribute cum_table to None\n",
-      "INFO:gensim.utils:loaded /home/atj39/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model\n"
+      "INFO:gensim.utils:loaded /Users/arianjamasb/github/graphein/graphein/protein/features/pretrained_models/swissprot-reviewed-protvec.model\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ESM: [-0.02279946  0.16464779 -0.0046199  ...  0.02556622 -0.19798389\n",
-      "  0.09682209]\n",
+      "ESM: [-0.02279943  0.16464774 -0.00461995 ...  0.02556622 -0.19798394\n",
+      "  0.09682215]\n",
       "biovec: [array([ -8.865931  ,   5.65769   ,  -1.5052402 ,   3.2893941 ,\n",
       "        -6.6228924 ,   2.4890645 ,   1.0635861 ,   2.4991994 ,\n",
       "        -2.2740204 ,  -7.6614437 ,   0.3739053 ,   4.285517  ,\n",
@@ -108805,7 +108782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
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    "metadata": {},
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     {
@@ -109009,7 +108986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
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    "metadata": {},
    "outputs": [
     {
@@ -109017,7 +108994,8 @@
      "output_type": "stream",
      "text": [
       "DEBUG:graphein.protein.graphs:Deprotonating protein. This removes H atoms from the pdb_df dataframe\n",
-      "DEBUG:graphein.protein.graphs:Detected 174 total nodes\n"
+      "DEBUG:graphein.protein.graphs:Detected 174 total nodes\n",
+      "DEBUG:graphein.protein.features.nodes.amino_acid:Reading Expasy protein scales from: /Users/arianjamasb/github/graphein/graphein/protein/features/nodes/amino_acid_properties.csv\n"
      ]
     },
     {
@@ -109050,6 +109028,18113 @@
     "    break"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Geometric features\n",
+    "\n",
+    "[API Reference](https://graphein.ai/modules/graphein.protein.html#graphein.protein.features.nodes.geometry)\n",
+    "\n",
+    "We can also add geometric features to protein structure graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "width": 620
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from graphein.protein.features.nodes.geometry import add_sidechain_vector, add_beta_carbon_vector, add_sequence_neighbour_vector\n",
+    "from graphein.protein.visualisation import add_vector_to_plot\n",
+    "\n",
+    "add_sidechain_vector(g)\n",
+    "add_beta_carbon_vector(g)\n",
+    "add_sequence_neighbour_vector(g)\n",
+    "\n",
+    "fig = plotly_protein_structure_graph(g, node_size_multiplier=1)\n",
+    "fig = add_vector_to_plot(g, fig, \"sidechain_vector\", colour=\"red\", scale=1.5)\n",
+    "fig = add_vector_to_plot(g, fig, \"c_beta_vector\", colour=\"blue\", scale=1.5)\n",
+    "fig = add_vector_to_plot(g, fig, \"sequence_neighbour_vector_n_to_c\", colour=\"green\", scale=1.5)\n",
+    "fig"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},


Test patch:
diff --git a/tests/protein/nodes/features/test_geometry.py b/tests/protein/nodes/features/test_geometry.py
new file mode 100644
index 00000000..acaa4f25
--- /dev/null
+++ b/tests/protein/nodes/features/test_geometry.py
@@ -0,0 +1,126 @@
+"""Tests for graphein.protein.features.nodes.geometry"""
+# Graphein
+# Author: Arian Jamasb <arian@jamasb.io>, Eric Ma
+# License: MIT
+# Project Website: https://github.com/a-r-j/graphein
+# Code Repository: https://github.com/a-r-j/graphein
+
+from functools import partial
+
+import numpy as np
+
+from graphein.protein.config import ProteinGraphConfig
+from graphein.protein.features.nodes.geometry import (
+    add_beta_carbon_vector,
+    add_sidechain_vector,
+)
+from graphein.protein.graphs import construct_graph
+
+
+def test_add_beta_carbon_vector():
+    config = ProteinGraphConfig(
+        edge_construction_functions=[
+            partial(add_beta_carbon_vector, scale=True)
+        ],
+    )
+    g = construct_graph(pdb_code="1lds", config=config)
+
+    raw_pdb = g.graph["raw_pdb_df"]
+    for n, d in g.nodes(data=True):
+        # Check that the node has the correct attributes
+        assert "c_beta_vector" in d.keys()
+        # Check the vector is of the correct dimensionality
+        assert d["c_beta_vector"].shape == (3,)
+
+        # check glycines are zero
+        if d["residue_name"] == "GLY":
+            np.testing.assert_equal(
+                d["c_beta_vector"], np.array([0.0, 0.0, 0.0])
+            )
+        else:
+            # Check scaled vector has norm close 1
+            np.testing.assert_almost_equal(
+                np.linalg.norm(d["c_beta_vector"]), 1.0
+            )
+
+    # Test unscaled vector
+    config = ProteinGraphConfig(
+        edge_construction_functions=[
+            partial(add_beta_carbon_vector, scale=False)
+        ],
+    )
+    g = construct_graph(pdb_code="1lds", config=config)
+
+    for n, d in g.nodes(data=True):
+        # check glycines are zero
+        if d["residue_name"] == "GLY":
+            np.testing.assert_equal(
+                d["c_beta_vector"], np.array([0.0, 0.0, 0.0])
+            )
+        else:
+            # Check the vector is pointing in the correct direction
+            cb_true = np.array(
+                raw_pdb[raw_pdb["node_id"] == n][raw_pdb["atom_name"] == "CB"][
+                    ["x_coord", "y_coord", "z_coord"]
+                ]
+            ).T.squeeze()
+            np.testing.assert_almost_equal(
+                cb_true, d["coords"] + d["c_beta_vector"]
+            )
+
+
+def test_add_sidechain_vector():
+    config = ProteinGraphConfig(
+        edge_construction_functions=[
+            partial(add_sidechain_vector, scale=True)
+        ],
+    )
+    g = construct_graph(pdb_code="1lds", config=config)
+
+    for n, d in g.nodes(data=True):
+        # Check that the node has the correct attributes
+        assert "sidechain_vector" in d.keys()
+        # Check the vector is of the correct dimensionality
+        assert d["sidechain_vector"].shape == (3,)
+
+        # check glycines are zero
+        if d["residue_name"] == "GLY":
+            np.testing.assert_equal(
+                d["sidechain_vector"], np.array([0.0, 0.0, 0.0])
+            )
+        else:
+            # Check scaled vector has norm close 1
+            np.testing.assert_almost_equal(
+                np.linalg.norm(d["sidechain_vector"]), 1.0
+            )
+
+    # Test unscaled vector
+    config = ProteinGraphConfig(
+        edge_construction_functions=[
+            partial(add_sidechain_vector, scale=False)
+        ],
+    )
+    g = construct_graph(pdb_code="1lds", config=config)
+
+    for n, d in g.nodes(data=True):
+        # check glycines are zero
+        if d["residue_name"] == "GLY":
+            np.testing.assert_equal(
+                d["sidechain_vector"], np.array([0.0, 0.0, 0.0])
+            )
+        else:
+            # Check the vector is pointing in the correct direction
+            sc_true = np.array(
+                g.graph["rgroup_df"]
+                .groupby("node_id")
+                .mean()
+                .loc[n][["x_coord", "y_coord", "z_coord"]]
+            )
+            np.testing.assert_almost_equal(
+                sc_true, d["coords"] + d["sidechain_vector"]
+            )
+
+
+if __name__ == "__main__":
+    test_add_beta_carbon_vector()
+    test_add_sidechain_vector()