PSP - 蛋白质复合物结构预测 Template Pair 特征 Mask 可视化

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在蛋白质复合物结构预测中，在 TemplatePairEmbedderMultimer 层中 ，构建 Template Pair 特征的源码，即：

• 将特征 template_dgram、pseudo_beta_mask_2d、aatype_one_hot、backbone_mask_2d、unit_vector(x/y/z) 特征，通过 linear 层累加到一起。
• 其中，都需要使用 multichain_mask_2d 进行固定掩码，选择单链区域。
• 输出维度：([1, 1102, 1102, 64])，linear层的输出 c_out 维度是 64。

源码如下：

def forward(self,template_dgram: torch.Tensor,aatype_one_hot: torch.Tensor,query_embedding: torch.Tensor,pseudo_beta_mask: torch.Tensor,backbone_mask: torch.Tensor,multichain_mask_2d: torch.Tensor,unit_vector: geometry.Vec3Array,
) -> torch.Tensor:act = 0.0pseudo_beta_mask_2d = (pseudo_beta_mask[..., None] * pseudo_beta_mask[..., None, :])pseudo_beta_mask_2d = pseudo_beta_mask_2d * multichain_mask_2dtemplate_dgram = template_dgram * pseudo_beta_mask_2d[..., None]act += self.dgram_linear(template_dgram)act += self.pseudo_beta_mask_linear(pseudo_beta_mask_2d[..., None])aatype_one_hot = aatype_one_hot.to(template_dgram.dtype)act += self.aatype_linear_1(aatype_one_hot[..., None, :, :])act += self.aatype_linear_2(aatype_one_hot[..., None, :])backbone_mask_2d = backbone_mask[..., None] * backbone_mask[..., None, :]backbone_mask_2d = backbone_mask_2d * multichain_mask_2dx, y, z = [coord * backbone_mask_2d for coord in unit_vector]act += self.x_linear(x[..., None])act += self.y_linear(y[..., None])act += self.z_linear(z[..., None])act += self.backbone_mask_linear(backbone_mask_2d[..., None])query_embedding = self.query_embedding_layer_norm(query_embedding)act += self.query_embedding_linear(query_embedding)return act

template_dgram 特征：

template_dgram 特征与 multichain_mask_2d：

backbone_mask_2d 特征：

backbone_mask_2d 特征与 multichain_mask_2d：

写入特征，即：

tmp_dict = dict()
tmp_dict["pseudo_beta_mask_2d_prev"] = pseudo_beta_mask_2d.cpu().numpy()
tmp_dict["pseudo_beta_mask_2d_post"] = pseudo_beta_mask_2d.cpu().numpy()
tmp_dict["template_dgram_post"] = template_dgram.cpu().numpy()
tmp_dict["backbone_mask_2d_prev"] = backbone_mask_2d.cpu().numpy()
tmp_dict["backbone_mask_2d_post"] = backbone_mask_2d.cpu().numpy()import pickle
with open("template_pair_embedder_multimer.pkl", "wb") as f:pickle.dump(tmp_dict, f)
logger.info(f"[CL] saved template_pair_embedder_multimer!")

读取特征，即：

def load_tensor_dict(input_path):"""加载特征文件['template_dgram', 'z', 'pseudo_beta_mask', 'backbone_mask', 'multichain_mask_2d','unit_vector_x', 'unit_vector_y', 'unit_vector_z']"""import picklewith open(input_path, "rb") as f:obj = pickle.load(f)print(f"[Info] feat_dict: {obj.keys()}")return objdef process_template_pair_embedder_multimer_dict(feat_dict, output_dir):print(f"[Info] feat_dict.keys: {feat_dict.keys()}")draw_tensor_2d(feat_dict["pseudo_beta_mask_2d_prev"], os.path.join(output_dir, "pseudo_beta_mask_2d_prev.png"))draw_tensor_2d(feat_dict["pseudo_beta_mask_2d_post"], os.path.join(output_dir, "pseudo_beta_mask_2d_prev.png"))draw_template_dgram(feat_dict["template_dgram_post"], os.path.join(output_dir, "template_dgram_post.png"))draw_tensor_2d(feat_dict["backbone_mask_2d_prev"], os.path.join(output_dir, "backbone_mask_2d_prev.png"))draw_tensor_2d(feat_dict["backbone_mask_2d_post"], os.path.join(output_dir, "backbone_mask_2d_post.png"))def draw_tensor_2d(feat, output_path):"""backbone_mask: torch.Size([1, 1102])"""feat = np.squeeze(feat)f, ax_arr = plt.subplots(1, 1, figsize=(8, 5))im = ax_arr.imshow(feat)f.colorbar(im, ax=ax_arr)plt.savefig(output_path, bbox_inches='tight', format='png')plt.show()def draw_template_dgram(feat, output_path):"""template_dgram: torch.Size([1, 1102, 1102, 39])"""f, ax_arr = plt.subplots(6, 7, figsize=(24, 15))ax_arr = ax_arr.flatten()feat = np.squeeze(feat)print(f"[Info] feat: {feat.shape}")for i in range(0, 42):if i <= 38:im = ax_arr[i].imshow(feat[:, :, i], interpolation='none')f.colorbar(im, ax=ax_arr[i])else:ax_arr[i].set_axis_off()plt.savefig(output_path, bbox_inches='tight', format='png')plt.show()