dymean 维度

Let me help analyze the residue position embedding from the code.

Looking at the AminoAcidEmbedding class in the first file, there are two options for position embeddings:

1. Relative Position Embedding:

if relative_position:
    self.res_pos_embedding = SinusoidalPositionEmbedding(res_embed_size)  # relative positional encoding

2. Absolute Position Embedding:

else:
    self.res_pos_embedding = nn.Embedding(max_position, res_embed_size)  # absolute position encoding  
    # max_position=192 by default

The maximum length of residue position embedding depends on which option is used:

• For relative position embedding (using SinusoidalPositionEmbedding), there is no fixed maximum length limitation since it uses sinusoidal functions to encode positions. This allows it to theoretically handle any sequence length.

• For absolute position embedding, there is a fixed maximum length of 192 positions, as specified in the default parameter max_position=192 in the AminoAcidEmbedding constructor.

Looking at the usage in the code, the relative position embedding appears to be the default choice, as seen in the model instantiation:

self.aa_embedding = AminoAcidEmbedding(
    self.num_aa_type, self.num_atom_type, self.num_atom_pos,
    embed_size, embed_size, self.atom_pad_idx, relative_position=True)

So in summary:

• By default, it uses relative positional encoding which has no fixed maximum length
• If switched to absolute positional encoding (relative_position=False), then it has a fixed maximum length of 192 positions
• This max_position parameter of 192 appears to be chosen based on IMGT numbering scheme for antibodies, as commented in the code

i am wondering whether antigen, heavy chain, light chain using different position?

Yes, antigen, heavy chain, and light chain use different position indices. Let me explain by looking at the code:

In AminoAcidFeature class, there's a method _construct_residue_pos that handles this:

def _construct_residue_pos(self, S):
    # construct residue position. global node is 1, the first residue is 2, ... (0 for padding)
    glbl_node_mask = self._is_global(S)  # Identifies global nodes (BOA, BOH, BOL)
    glbl_node_idx = torch.nonzero(glbl_node_mask).flatten()  # [batch_size * 3] (boa, boh, bol)
    
    # Calculate shifts between global nodes
    shift = F.pad(glbl_node_idx[:-1] - glbl_node_idx[1:] + 1, (1, 0), value=1)
    
    # Initialize all positions with ones
    residue_pos = torch.ones_like(S)
    residue_pos[glbl_node_mask] = shift
    
    # Cumulative sum to generate positions
    residue_pos = torch.cumsum(residue_pos, dim=0)

Let's break down how the positions are assigned:

1. The sequence starts with three special tokens:

   • BOA (Begin of Antigen)
   • BOH (Begin of Heavy chain)
   • BOL (Begin of Light chain)

2. After each special token, position numbering starts fresh:

   • Antigen residues: Start after BOA token
   • Heavy chain residues: Start after BOH token
   • Light chain residues: Start after BOL token

3. The positioning is done through cumulative sum, ensuring:

   • Each section (antigen/heavy/light) has its own independent position numbering
   • The positions are reset at each global token (BOA/BOH/BOL)

For example, a sequence might be numbered like this:

BOA   Ag1  Ag2  Ag3   BOH   H1   H2   H3   BOL   L1   L2   L3
1     2    3    4     1     2    3    4    1     2    3    4

This separate position numbering for each chain type helps the model distinguish between residues from different chains while maintaining relative positional information within each chain.

so, is it for this setting, for a residue, it will potentail has 14 position, 14 atoms in each position?

No - each residue has up to 14 atoms total (4 backbone + up to 10 sidechain), and each atom has one 3D position (x,y,z coordinates). The positions in a residue correspond 1:1 with its atoms - there aren't multiple positions per atom.

The code handles this through n_channel=14 to accommodate all possible atoms from any residue type, with padding for residues that have fewer than 14 atoms.

i mean the max situation, it will have 14 position

Yes, that's correct. In the max case scenario (like TRP residue), it will have 14 positions corresponding to all possible atoms:

• 4 backbone positions (N, CA, C, O)
• 10 sidechain positions (CB, CG, CD1, CD2, etc.)

For residues with fewer atoms, the remaining positions are padded.

I'll help you explore better network architectures for handling potentially sparse coordinate matrices. Looking at your code, I notice you're transforming coordinate differences into a radial representation. Let me suggest some improvements:

I've proposed two alternative implementations to replace your current radial_linear layer:

1. SparseRadialNet: This network is designed specifically for processing sparse coordinate matrices with these key features:

   • Uses a sparse linear layer with reduced parameters
   • Incorporates group-wise processing to capture local patterns in the 14x14 coordinate structure
   • Combines global and local features for better representation
   • Includes a learnable scaling factor for adaptive normalization

2. SparseAttentionRadialNet: This implementation uses sparse attention mechanisms:

   • Multi-head attention to capture different aspects of the coordinate relationships
   • More efficient parameter usage through attention mechanisms
   • Built-in sparsity through attention weights
   • Automatic handling of local and global patterns

To use either of these in your existing code, you would replace:

self.radial_linear = nn.Linear(196, radial_nf)

with:

self.radial_linear = SparseRadialNet(196, radial_nf)
# or
self.radial_linear = SparseAttentionRadialNet(196, radial_nf)

The advantages of these approaches over a simple linear layer include:

1. Better handling of sparse patterns in coordinate matrices
2. More efficient parameter usage
3. Ability to capture both local and global patterns
4. Automatic feature selection through attention or grouping
5. Built-in normalization and scaling

Would you like me to explain any particular aspect of these implementations in more detail?