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# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model config."""
import copy
from alphafold.model.tf import shape_placeholders
import ml_collections
NUM_RES = shape_placeholders.NUM_RES
NUM_MSA_SEQ = shape_placeholders.NUM_MSA_SEQ
NUM_EXTRA_SEQ = shape_placeholders.NUM_EXTRA_SEQ
NUM_TEMPLATES = shape_placeholders.NUM_TEMPLATES
def model_config(name: str) -> ml_collections.ConfigDict:
"""Get the ConfigDict of a CASP14 model."""
if name not in CONFIG_DIFFS:
raise ValueError(f'Invalid model name {name}.')
cfg = copy.deepcopy(CONFIG)
cfg.update_from_flattened_dict(CONFIG_DIFFS[name])
return cfg
CONFIG_DIFFS = {
'model_1': {
# Jumper et al. (2021) Suppl. Table 5, Model 1.1.1
'data.common.max_extra_msa': 5120,
'data.common.reduce_msa_clusters_by_max_templates': True,
'data.common.use_templates': True,
'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
'model.embeddings_and_evoformer.template.enabled': True
},
'model_2': {
# Jumper et al. (2021) Suppl. Table 5, Model 1.1.2
'data.common.reduce_msa_clusters_by_max_templates': True,
'data.common.use_templates': True,
'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
'model.embeddings_and_evoformer.template.enabled': True
},
'model_3': {
# Jumper et al. (2021) Suppl. Table 5, Model 1.2.1
'data.common.max_extra_msa': 5120,
},
'model_4': {
# Jumper et al. (2021) Suppl. Table 5, Model 1.2.2
'data.common.max_extra_msa': 5120,
},
'model_5': {
# Jumper et al. (2021) Suppl. Table 5, Model 1.2.3
},
# The following models are fine-tuned from the corresponding models above
# with an additional predicted_aligned_error head that can produce
# predicted TM-score (pTM) and predicted aligned errors.
'model_1_ptm': {
'data.common.max_extra_msa': 5120,
'data.common.reduce_msa_clusters_by_max_templates': True,
'data.common.use_templates': True,
'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
'model.embeddings_and_evoformer.template.enabled': True,
'model.heads.predicted_aligned_error.weight': 0.1
},
'model_2_ptm': {
'data.common.reduce_msa_clusters_by_max_templates': True,
'data.common.use_templates': True,
'model.embeddings_and_evoformer.template.embed_torsion_angles': True,
'model.embeddings_and_evoformer.template.enabled': True,
'model.heads.predicted_aligned_error.weight': 0.1
},
'model_3_ptm': {
'data.common.max_extra_msa': 5120,
'model.heads.predicted_aligned_error.weight': 0.1
},
'model_4_ptm': {
'data.common.max_extra_msa': 5120,
'model.heads.predicted_aligned_error.weight': 0.1
},
'model_5_ptm': {
'model.heads.predicted_aligned_error.weight': 0.1
}
}
CONFIG = ml_collections.ConfigDict({
'data': {
'common': {
'masked_msa': {
'profile_prob': 0.1,
'same_prob': 0.1,
'uniform_prob': 0.1
},
'max_extra_msa': 1024,
'msa_cluster_features': True,
'num_recycle': 3,
'reduce_msa_clusters_by_max_templates': False,
'resample_msa_in_recycling': True,
'template_features': [
'template_all_atom_positions', 'template_sum_probs',
'template_aatype', 'template_all_atom_masks',
'template_domain_names'
],
'unsupervised_features': [
'aatype', 'residue_index', 'sequence', 'msa', 'domain_name',
'num_alignments', 'seq_length', 'between_segment_residues',
'deletion_matrix'
],
'use_templates': False,
},
'eval': {
'feat': {
'aatype': [NUM_RES],
'all_atom_mask': [NUM_RES, None],
'all_atom_positions': [NUM_RES, None, None],
'alt_chi_angles': [NUM_RES, None],
'atom14_alt_gt_exists': [NUM_RES, None],
'atom14_alt_gt_positions': [NUM_RES, None, None],
'atom14_atom_exists': [NUM_RES, None],
'atom14_atom_is_ambiguous': [NUM_RES, None],
'atom14_gt_exists': [NUM_RES, None],
'atom14_gt_positions': [NUM_RES, None, None],
'atom37_atom_exists': [NUM_RES, None],
'backbone_affine_mask': [NUM_RES],
'backbone_affine_tensor': [NUM_RES, None],
'bert_mask': [NUM_MSA_SEQ, NUM_RES],
'chi_angles': [NUM_RES, None],
'chi_mask': [NUM_RES, None],
'extra_deletion_value': [NUM_EXTRA_SEQ, NUM_RES],
'extra_has_deletion': [NUM_EXTRA_SEQ, NUM_RES],
'extra_msa': [NUM_EXTRA_SEQ, NUM_RES],
'extra_msa_mask': [NUM_EXTRA_SEQ, NUM_RES],
'extra_msa_row_mask': [NUM_EXTRA_SEQ],
'is_distillation': [],
'msa_feat': [NUM_MSA_SEQ, NUM_RES, None],
'msa_mask': [NUM_MSA_SEQ, NUM_RES],
'msa_row_mask': [NUM_MSA_SEQ],
'pseudo_beta': [NUM_RES, None],
'pseudo_beta_mask': [NUM_RES],
'random_crop_to_size_seed': [None],
'residue_index': [NUM_RES],
'residx_atom14_to_atom37': [NUM_RES, None],
'residx_atom37_to_atom14': [NUM_RES, None],
'resolution': [],
'rigidgroups_alt_gt_frames': [NUM_RES, None, None],
'rigidgroups_group_exists': [NUM_RES, None],
'rigidgroups_group_is_ambiguous': [NUM_RES, None],
'rigidgroups_gt_exists': [NUM_RES, None],
'rigidgroups_gt_frames': [NUM_RES, None, None],
'seq_length': [],
'seq_mask': [NUM_RES],
'target_feat': [NUM_RES, None],
'template_aatype': [NUM_TEMPLATES, NUM_RES],
'template_all_atom_masks': [NUM_TEMPLATES, NUM_RES, None],
'template_all_atom_positions': [
NUM_TEMPLATES, NUM_RES, None, None],
'template_backbone_affine_mask': [NUM_TEMPLATES, NUM_RES],
'template_backbone_affine_tensor': [
NUM_TEMPLATES, NUM_RES, None],
'template_mask': [NUM_TEMPLATES],
'template_pseudo_beta': [NUM_TEMPLATES, NUM_RES, None],
'template_pseudo_beta_mask': [NUM_TEMPLATES, NUM_RES],
'template_sum_probs': [NUM_TEMPLATES, None],
'true_msa': [NUM_MSA_SEQ, NUM_RES]
},
'fixed_size': True,
'subsample_templates': False, # We want top templates.
'masked_msa_replace_fraction': 0.15,
'max_msa_clusters': 512,
'max_templates': 4,
'num_ensemble': 1,
},
},
'model': {
'embeddings_and_evoformer': {
'evoformer_num_block': 48,
'evoformer': {
'msa_row_attention_with_pair_bias': {
'dropout_rate': 0.15,
'gating': True,
'num_head': 8,
'orientation': 'per_row',
'shared_dropout': True
},
'msa_column_attention': {
'dropout_rate': 0.0,
'gating': True,
'num_head': 8,
'orientation': 'per_column',
'shared_dropout': True
},
'msa_transition': {
'dropout_rate': 0.0,
'num_intermediate_factor': 4,
'orientation': 'per_row',
'shared_dropout': True
},
'outer_product_mean': {
'chunk_size': 128,
'dropout_rate': 0.0,
'num_outer_channel': 32,
'orientation': 'per_row',
'shared_dropout': True
},
'triangle_attention_starting_node': {
'dropout_rate': 0.25,
'gating': True,
'num_head': 4,
'orientation': 'per_row',
'shared_dropout': True
},
'triangle_attention_ending_node': {
'dropout_rate': 0.25,
'gating': True,
'num_head': 4,
'orientation': 'per_column',
'shared_dropout': True
},
'triangle_multiplication_outgoing': {
'dropout_rate': 0.25,
'equation': 'ikc,jkc->ijc',
'num_intermediate_channel': 128,
'orientation': 'per_row',
'shared_dropout': True
},
'triangle_multiplication_incoming': {
'dropout_rate': 0.25,
'equation': 'kjc,kic->ijc',
'num_intermediate_channel': 128,
'orientation': 'per_row',
'shared_dropout': True
},
'pair_transition': {
'dropout_rate': 0.0,
'num_intermediate_factor': 4,
'orientation': 'per_row',
'shared_dropout': True
}
},
'extra_msa_channel': 64,
'extra_msa_stack_num_block': 4,
'max_relative_feature': 32,
'custom_relative_features': False,
'msa_channel': 256,
'pair_channel': 128,
'prev_pos': {
'min_bin': 3.25,
'max_bin': 20.75,
'num_bins': 15
},
'recycle_features': True,
'recycle_pos': True,
'recycle_dgram': False,
'backprop_dgram': False,
'backprop_dgram_temp': 1.0,
'seq_channel': 384,
'template': {
'attention': {
'gating': False,
'key_dim': 64,
'num_head': 4,
'value_dim': 64
},
'dgram_features': {
'min_bin': 3.25,
'max_bin': 50.75,
'num_bins': 39
},
'backprop_dgram': False,
'backprop_dgram_temp': 1.0,
'embed_torsion_angles': False,
'enabled': False,
'template_pair_stack': {
'num_block': 2,
'triangle_attention_starting_node': {
'dropout_rate': 0.25,
'gating': True,
'key_dim': 64,
'num_head': 4,
'orientation': 'per_row',
'shared_dropout': True,
'value_dim': 64
},
'triangle_attention_ending_node': {
'dropout_rate': 0.25,
'gating': True,
'key_dim': 64,
'num_head': 4,
'orientation': 'per_column',
'shared_dropout': True,
'value_dim': 64
},
'triangle_multiplication_outgoing': {
'dropout_rate': 0.25,
'equation': 'ikc,jkc->ijc',
'num_intermediate_channel': 64,
'orientation': 'per_row',
'shared_dropout': True
},
'triangle_multiplication_incoming': {
'dropout_rate': 0.25,
'equation': 'kjc,kic->ijc',
'num_intermediate_channel': 64,
'orientation': 'per_row',
'shared_dropout': True
},
'pair_transition': {
'dropout_rate': 0.0,
'num_intermediate_factor': 2,
'orientation': 'per_row',
'shared_dropout': True
}
},
'max_templates': 4,
'subbatch_size': 128,
'use_template_unit_vector': False,
}
},
'global_config': {
'mixed_precision': False,
'deterministic': False,
'subbatch_size': 4,
'use_remat': False,
'zero_init': True
},
'heads': {
'distogram': {
'first_break': 2.3125,
'last_break': 21.6875,
'num_bins': 64,
'weight': 0.3
},
'predicted_aligned_error': {
# `num_bins - 1` bins uniformly space the
# [0, max_error_bin A] range.
# The final bin covers [max_error_bin A, +infty]
# 31A gives bins with 0.5A width.
'max_error_bin': 31.,
'num_bins': 64,
'num_channels': 128,
'filter_by_resolution': True,
'min_resolution': 0.1,
'max_resolution': 3.0,
'weight': 0.0,
},
'experimentally_resolved': {
'filter_by_resolution': True,
'max_resolution': 3.0,
'min_resolution': 0.1,
'weight': 0.01
},
'structure_module': {
'num_layer': 8,
'fape': {
'clamp_distance': 10.0,
'clamp_type': 'relu',
'loss_unit_distance': 10.0
},
'angle_norm_weight': 0.01,
'chi_weight': 0.5,
'clash_overlap_tolerance': 1.5,
'compute_in_graph_metrics': True,
'dropout': 0.1,
'num_channel': 384,
'num_head': 12,
'num_layer_in_transition': 3,
'num_point_qk': 4,
'num_point_v': 8,
'num_scalar_qk': 16,
'num_scalar_v': 16,
'position_scale': 10.0,
'sidechain': {
'atom_clamp_distance': 10.0,
'num_channel': 128,
'num_residual_block': 2,
'weight_frac': 0.5,
'length_scale': 10.,
},
'structural_violation_loss_weight': 1.0,
'violation_tolerance_factor': 12.0,
'weight': 1.0
},
'predicted_lddt': {
'filter_by_resolution': True,
'max_resolution': 3.0,
'min_resolution': 0.1,
'num_bins': 50,
'num_channels': 128,
'weight': 0.01
},
'masked_msa': {
'num_output': 23,
'weight': 2.0
},
},
'num_recycle': 3,
'backprop_recycle': False,
'resample_msa_in_recycling': True,
'add_prev': False,
'use_struct': True,
},
})