加密网站,深圳网站设计首选刻,百度知道首页登录入口,北京手机网站开发公司欢迎关注我的CSDN#xff1a;https://spike.blog.csdn.net/ 本文地址#xff1a;https://spike.blog.csdn.net/article/details/134328447 在 蛋白质复合物结构预测 的过程中#xff0c;模版 (Template) 起到重要作用#xff0c;提供预测结果的关于三维结构的先验信息… 欢迎关注我的CSDNhttps://spike.blog.csdn.net/ 本文地址https://spike.blog.csdn.net/article/details/134328447 在 蛋白质复合物结构预测 的过程中模版 (Template) 起到重要作用提供预测结果的关于三维结构的先验信息在多链的情况需要进行模版配对即 Template Pair核心函数是 template_pair_embedder结合 AlphaFold2 的论文分析具体输入与输出的特征。
核心逻辑 template_pair_embedder()输入和输出特征维度
[CL] TemplateEmbedderMultimer - template_dgram: torch.Size([1, 1102, 1102, 39])
[CL] TemplateEmbedderMultimer - z: torch.Size([1102, 1102, 128])
[CL] TemplateEmbedderMultimer - pseudo_beta_mask: torch.Size([1, 1102])
[CL] TemplateEmbedderMultimer - backbone_mask: torch.Size([1, 1102])
[CL] TemplateEmbedderMultimer - multichain_mask_2d: torch.Size([1102, 1102])
[CL] TemplateEmbedderMultimer - unit_vector: torch.Size([1, 1102, 1102])
[CL] TemplateEmbedderMultimer - pair_act: torch.Size([1, 1102, 1102, 64])函数
# openfold/model/embedders.py
pair_act self.template_pair_embedder(template_dgram,aatype_one_hot,z,pseudo_beta_mask,backbone_mask,multichain_mask_2d,unit_vector,
)t torch.sum(t, dim-4) / n_templ
t torch.nn.functional.relu(t)
t self.linear_t(t) # 从 c_t 维度 转换 成 c_z 维度更新 z
template_embeds[template_pair_embedding] t# openfold/model/model.py
template_embeds self.template_embedder(template_feats,z,pair_mask.to(dtypez.dtype),no_batch_dims,chunk_sizeself.globals.chunk_size,multichain_mask_2dmultichain_mask_2d,use_faself.globals.use_fa,
)
z z template_embeds[template_pair_embedding] # line 13 in Alg.2逻辑图 1. template_dgram 特征
template_dgram特征计算不同点距离其他点的远近划分共划分 39 个binTemplate 的 no_bin 是 1.25 计算 1 个值即 (50.75 - 3.25) / 38 1.25即
template_dgram dgram_from_positions(template_positions,infself.config.inf,**self.config.distogram,
)def dgram_from_positions(pos: torch.Tensor,min_bin: float 3.25,max_bin: float 50.75,no_bins: float 39,inf: float 1e8,
):dgram torch.sum((pos[..., None, :] - pos[..., None, :, :]) ** 2, dim-1, keepdimTrue)lower torch.linspace(min_bin, max_bin, no_bins, devicepos.device) ** 2upper torch.cat([lower[1:], lower.new_tensor([inf])], dim-1)dgram ((dgram lower) * (dgram upper)).type(dgram.dtype)return dgramTemplate 的 no_bin 是 1.25 计算 1 个值即 (50.75 - 3.25) / 38 1.25长度是 39。 其中template_positions 特征如下
输入 template_pseudo_beta 与 template_pseudo_beta_mask输出坐标 template_positions即 template_pseudo_beta 的值
template_positions, pseudo_beta_mask (single_template_feats[template_pseudo_beta],single_template_feats[template_pseudo_beta_mask],
)其中pseudo_beta 特征的处理来源于 openfold/data/data_transforms_multimer.py 即
输入特征template_aatype、template_all_atom_positions、template_all_atom_mask原理是选择 CA 或 CB 原子的坐标与 Mask
源码即调用关系如下
# 输入特征模型预测结构
# run_pretrained_openfold.py
processed_feature_dict, _ feature_processor.process_features(feature_dict, is_multimer, modepredict
)
output_dict predict_structure_single_dev(args,model_name,current_model,fasta_path,processed_feature_dict,config,
)# openfold/data/feature_pipeline.py
processed_feature, label np_example_to_features_multimer(np_exampleraw_features,configself.config,modemode,
)# openfold/data/feature_pipeline.py
features, label input_pipeline_multimer.process_tensors_from_config(tensor_dict,cfg.common,cfg[mode],cfg.data_module,
)# openfold/data/input_pipeline_multimer.py
nonensembled nonensembled_transform_fns(common_cfg,mode_cfg,
)
tensors compose(nonensembled)(tensors)# openfold/data/input_pipeline_multimer.py
operators.extend([data_transforms_multimer.make_atom14_positions,data_transforms_multimer.atom37_to_frames,data_transforms_multimer.atom37_to_torsion_angles(),data_transforms_multimer.make_pseudo_beta(),data_transforms_multimer.get_backbone_frames,data_transforms_multimer.get_chi_angles,]
)# openfold/data/data_transforms_multimer.py
def make_pseudo_beta(protein, prefix):Create pseudo-beta (alpha for glycine) position and mask.assert prefix in [, template_](protein[prefix pseudo_beta],protein[prefix pseudo_beta_mask],) pseudo_beta_fn(protein[template_aatype if prefix else aatype],protein[prefix all_atom_positions],protein[template_all_atom_mask if prefix else all_atom_mask],)return protein# openfold/data/data_transforms_multimer.py
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_mask):Create pseudo beta features.if aatype.shape[0] 0:is_gly torch.eq(aatype, rc.restype_order[G])ca_idx rc.atom_order[CA]cb_idx rc.atom_order[CB]pseudo_beta torch.where(torch.tile(is_gly[..., None], [1] * len(is_gly.shape) [3]),all_atom_positions[..., ca_idx, :],all_atom_positions[..., cb_idx, :],)else:pseudo_beta all_atom_positions.new_zeros(*aatype.shape, 3)if all_atom_mask is not None:if aatype.shape[0] 0:pseudo_beta_mask torch.where(is_gly, all_atom_mask[..., ca_idx], all_atom_mask[..., cb_idx])else:pseudo_beta_mask torch.zeros_like(aatype).float()return pseudo_beta, pseudo_beta_maskelse:return pseudo_betatemplate_pseudo_beta_mask: Mask indicating if the beta carbon (alpha carbon for glycine) atom has coordinates for the template at this residue. template_dgram 特征 [1, 1102, 1102, 39]即 2. z 特征
z 特征作为输入直接传入来源于 protein[target_feat]来源于protein[]between_segment_residues] 即
日志target_feat: torch.Size([1102, 21])不包括 “-”只包括21201个氨基酸包括X将 [1102, 21] 经过线性层转换成 c_z 维度即128维。再通过 outer sum 操作 转换成 LxLxC 维度其实 z 就是 Pair Representation即 [1102, 1102, 128] 维度。
# openfold/model/embedders.py
def forward(self,batch,z,padding_mask_2d,templ_dim,chunk_size,multichain_mask_2d,use_faFalse,
):# openfold/model/model.py
template_embeds self.template_embedder(template_feats,z,pair_mask.to(dtypez.dtype),no_batch_dims,chunk_sizeself.globals.chunk_size,multichain_mask_2dmultichain_mask_2d,use_faself.globals.use_fa,
)# openfold/model/model.py
m, z self.input_embedder(feats)# openfold/model/embedders.py#InputEmbedderMultimer
def forward(self, batch) - Tuple[torch.Tensor, torch.Tensor]:# ...Returns:msa_emb:[*, N_clust, N_res, C_m] MSA embeddingpair_emb:[*, N_res, N_res, C_z] pair embeddingtf batch[target_feat]msa batch[msa_feat]# [*, N_res, c_z]tf_emb_i self.linear_tf_z_i(tf)tf_emb_j self.linear_tf_z_j(tf)# [*, N_res, N_res, c_z]pair_emb tf_emb_i[..., None, :] tf_emb_j[..., None, :, :]pair_emb pair_emb self.relpos(batch) # 计算相对位置# [*, N_clust, N_res, c_m]n_clust msa.shape[-3]tf_m (self.linear_tf_m(tf).unsqueeze(-3).expand(((-1,) * len(tf.shape[:-2]) (n_clust, -1, -1))))msa_emb self.linear_msa_m(msa) tf_mreturn msa_emb, pair_emb# openfold/data/data_transforms_multimer.py
def create_target_feat(batch):Create the target featuresbatch[target_feat] torch.nn.functional.one_hot(batch[aatype], 21).to(torch.float32)return batch# openfold/data/input_pipeline_multimer.py
operators.extend([data_transforms_multimer.cast_to_64bit_ints,# todo: randomly_replace_msa_with_unknown may need to be confirmed and tried in training.# data_transforms_multimer.randomly_replace_msa_with_unknown(0.0),data_transforms_multimer.make_msa_profile,data_transforms_multimer.create_target_feat,data_transforms_multimer.make_atom14_masks,]
)InputEmbedderMultimer 的框架图
其中的 21 个氨基酸即
ID_TO_HHBLITS_AA {0: A,1: C, # Also U.2: D, # Also B.3: E, # Also Z.4: F,5: G,6: H,7: I,8: K,9: L,10: M,11: N,12: P,13: Q,14: R,15: S,16: T,17: V,18: W,19: Y,20: X, # Includes J and O.21: -,
}z 特征 (mean 和 max)[1102, 1102, 128]即 3. pseudo_beta_mask 与 backbone_mask 特征
pseudo_beta_mask 特征 参考 template_dgram 的 template_pseudo_beta 部分的源码
关注 CA 与 CB 的 Mask 信息
# openfold/data/data_transforms_multimer.py
def make_pseudo_beta(protein, prefix):Create pseudo-beta (alpha for glycine) position and mask.assert prefix in [, template_](protein[prefix pseudo_beta],protein[prefix pseudo_beta_mask],) pseudo_beta_fn(protein[template_aatype if prefix else aatype],protein[prefix all_atom_positions],protein[template_all_atom_mask if prefix else all_atom_mask],)return protein# openfold/data/data_transforms_multimer.py
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_mask):Create pseudo beta features.if aatype.shape[0] 0:is_gly torch.eq(aatype, rc.restype_order[G])ca_idx rc.atom_order[CA]cb_idx rc.atom_order[CB]pseudo_beta torch.where(torch.tile(is_gly[..., None], [1] * len(is_gly.shape) [3]),all_atom_positions[..., ca_idx, :],all_atom_positions[..., cb_idx, :],)else:pseudo_beta all_atom_positions.new_zeros(*aatype.shape, 3)if all_atom_mask is not None:if aatype.shape[0] 0:pseudo_beta_mask torch.where(is_gly, all_atom_mask[..., ca_idx], all_atom_mask[..., cb_idx])else:pseudo_beta_mask torch.zeros_like(aatype).float()return pseudo_beta, pseudo_beta_maskelse:return pseudo_beta其中openfold/data/msa_pairing.py#merge_chain_features() 合并(merge)多链特征输出 Template Feature即:
[CL] template features, template_aatype : (4, 1102)
[CL] template features, template_all_atom_positions : (4, 1102, 37, 3)
[CL] template features, template_all_atom_mask : (4, 1102, 37)注意 template_all_atom_mask即 37 个原子的 mask 信息。
其中37 个原子(Atom)
{N: 0, CA: 1, C: 2, CB: 3, O: 4, CG: 5, CG1: 6, CG2: 7, OG: 8, OG1: 9, SG: 10,
CD: 11, CD1: 12, CD2: 13, ND1: 14, ND2: 15, OD1: 16, OD2: 17, SD: 18, CE: 19, CE1: 20,
CE2: 21, CE3: 22, NE: 23, NE1: 24, NE2: 25, OE1: 26, OE2: 27, CH2: 28, NH1: 29, NH2: 30,
OH: 31, CZ: 32, CZ2: 33, CZ3: 34, NZ: 35, OXT: 36}backbone_mask 特征只关注 N、CA、C 三类原子的 Mask 信息参考
一般情况下都与 pseudo_beta_mask 相同因为要么存在残基要么不存在残基。
# openfold/utils/all_atom_multimer.py
def make_backbone_affine(positions: geometry.Vec3Array,mask: torch.Tensor,aatype: torch.Tensor,
) - Tuple[geometry.Rigid3Array, torch.Tensor]:a rc.atom_order[N]b rc.atom_order[CA]c rc.atom_order[C]rigid_mask mask[..., a] * mask[..., b] * mask[..., c]rigid make_transform_from_reference(a_xyzpositions[..., a],b_xyzpositions[..., b],c_xyzpositions[..., c],)return rigid, rigid_maskpseudo_beta_mask 与 backbone_mask 相同[1, 1102]即 4. multichain_mask_2d 特征
非常简单就是链内 Mask源码
# openfold/model/model.py
multichain_mask_2d (asym_id[..., None] asym_id[..., None, :]
) # [N_res, N_res]multichain_mask_2d[1102, 1102]即 5. unit_vector 特征
unit_vector 是 Rot3Array 对象与角度相关的单位向量源码
# openfold/model/embedders.py
rigid, backbone_mask all_atom_multimer.make_backbone_affine(atom_pos,single_template_feats[template_all_atom_mask],single_template_feats[template_aatype],
)
points rigid.translation
rigid_vec rigid[..., None].inverse().apply_to_point(points)
unit_vector rigid_vec.normalized()# openfold/utils/all_atom_multimer.py
def make_backbone_affine(positions: geometry.Vec3Array,mask: torch.Tensor,aatype: torch.Tensor,
) - Tuple[geometry.Rigid3Array, torch.Tensor]:a rc.atom_order[N]b rc.atom_order[CA]c rc.atom_order[C]rigid_mask mask[..., a] * mask[..., b] * mask[..., c]rigid make_transform_from_reference(a_xyzpositions[..., a],b_xyzpositions[..., b],c_xyzpositions[..., c],)return rigid, rigid_mask# openfold/utils/all_atom_multimer.py
def make_transform_from_reference(a_xyz: geometry.Vec3Array, b_xyz: geometry.Vec3Array, c_xyz: geometry.Vec3Array
) - geometry.Rigid3Array:Returns rotation and translation matrices to convert from reference.Note that this method does not take care of symmetries. If you provide thecoordinates in the non-standard way, the A atom will end up in the negativey-axis rather than in the positive y-axis. You need to take care of suchcases in your code.Args:a_xyz: A Vec3Array.b_xyz: A Vec3Array.c_xyz: A Vec3Array.Returns:A Rigid3Array which, when applied to coordinates in a canonicalizedreference frame, will give coordinates approximately equalthe original coordinates (in the global frame).rotation geometry.Rot3Array.from_two_vectors(c_xyz - b_xyz, a_xyz - b_xyz)return geometry.Rigid3Array(rotation, b_xyz)# openfold/utils/geometry/rotation_matrix.py
classmethod
def from_two_vectors(cls, e0: vector.Vec3Array, e1: vector.Vec3Array) - Rot3Array:Construct Rot3Array from two Vectors.Rot3Array is constructed such that in the corresponding frame e0 lies onthe positive x-Axis and e1 lies in the xy plane with positive sign of y.Args:e0: Vectore1: VectorReturns:Rot3Array# Normalize the unit vector for the x-axis, e0.e0 e0.normalized()# make e1 perpendicular to e0.c e1.dot(e0)e1 (e1 - c * e0).normalized()# Compute e2 as cross product of e0 and e1.e2 e0.cross(e1)return cls(e0.x, e1.x, e2.x, e0.y, e1.y, e2.y, e0.z, e1.z, e2.z)解释每个残基的局部框架内所有残基的 α \alpha α 碳原子位移的单位向量。 这些局部框架的计算方式与目标结构相同。 The unit vector of the displacement of the alpha carbon atom of all residues within the local frame of each residue. These local frames are computed in the same way as for the target structure. 计算逻辑 unit_vector 包括x、y、z等3个分量[1, 1102, 1102, 3]即
