Merge pull request #122 from jyaacoub/sequence-alignment

Build pairwise alignment and graph masking functions

src/utils/pocket_alignment.py

@@ -0,0 +1,106 @@
+"""
+A collection of functions that generate a graph
+mask from a binding pocket sequence.
+"""
+
+from Bio import Align
+from Bio.Align import substitution_matrices
+import torch
+
+
+def create_pocket_mask(target_seq: str, query_seq: str) -> list[bool]:
+    """
+    Return an index mask of a pocket on a protein sequence.
+    
+    Parameters
+    ----------
+    target_seq : str
+        The protein sequence you want to query in
+    query_seq : str
+        The binding pocket sequence for the protein
+
+    Returns
+    -------
+    index_mask : list[bool]
+        A boolean list of indices that are True if the residue at that
+        position is part of the binding pocket and false otherwise
+    """
+    # Taken from tutorial https://biopython.org/docs/dev/Tutorial/chapter_pairwise.html
+    aligner = Align.PairwiseAligner()
+    # Pairwise alignment parameters as specified in paragraph 2
+    # of Methods - Structure and sequence data in "Calibrated 
+    # geometric deep learning improves kinase-drug binding predictions"
+    # by Luo et al. (https://www.nature.com/articles/s42256-023-00751-0)
+    aligner.substitution_matrix = substitution_matrices.load('BLOSUM62')
+    aligner.open_gap_score = -10
+    aligner.extend_gap_score = -0.5
+    alignments = aligner.align(target_seq, query_seq)
+    alignment = alignments[0]
+
+    index_mask = [False] * len(target_seq)
+    for index_range in alignment.aligned[0]:
+        start, end = index_range[0], index_range[1]
+        for i in range(start, end):
+            index_mask[i] = True
+    return index_mask
+
+
+def mask_graph(data, mask: list[bool]):
+    """
+    Apply a binding pocket mask to a torch_geometric graph.
+    Remove nodes that aren't in the binding pocket and remove
+    edges corresponding to these removed nodes.
+    
+    Parameters
+    ----------
+    data : torch_geometric.data.Data
+        -x: node feature matrix with shape [num_residues, num_features]
+        -edge_index: pairs of indices that share an edge with shape [2, num_total_edges]
+        -pro_seq: full target protein sequence
+        -prot_id: protein ID with mutations if applicable
+    mask: list[bool]
+        A boolean list of indices that are True if the residue at that
+        position is part of the binding pocket and false otherwise
+
+    Return
+    ------
+    data : torch_geometric.data.Data
+        The same data object that is in the parameters, with the following
+        additional attributes:
+        -pocket_mask : list[bool]
+            The mask specified by the mask parameter of dimension [full_seuqence_length]
+        -pocket_mask_x : torch.Tensor
+            The nodes of only the pocket of the protein sequence of dimension
+            [pocket_sequence_length, num_features]
+        -pocket_mask_edge_index : torch.Tensor
+            The edge connections in COO format only relating to 
+            the pocket nodes of the protein sequence of dimension [2, num_pocket_edges]
+    """
+    nodes = data.x[mask]
+    edges = data.edge_index
+    edge_mask = []
+    for i in range(edges.shape[1]):
+        # Throw out edges that are connected to at least one node not in the
+        # binding pocket
+        node_1, node_2 = edges[:,i][0], edges[:,i][1]
+        edge_mask.append(True) if mask[node_1] and mask[node_2] else edge_mask.append(False)  
+    edges = torch.transpose(torch.transpose(edges, 0, 1)[edge_mask], 0, 1)
+
+    data.pocket_mask = mask
+    data.pocket_mask_x = nodes
+    data.pocket_mask_edge_index = edges
+    return data
+
+
+if __name__ == '__main__':
+    graph_data = torch.load('sample_pro_data.torch')
+    seq = graph_data.pro_seq
+    seq = seq[:857] + 'R' + seq[858:]
+    graph_data.pro_seq = seq
+    torch.save(graph_data, 'sample_pro_data_unmutated.torch')
+    binding_pocket_sequence = 'KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA'
+    mask = create_pocket_mask(
+        graph_data.pro_seq,
+        binding_pocket_sequence
+    )
+    masked_data = mask_graph(graph_data, mask)