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import jax.numpy as jnp
import numpy as np
import re
import copy
import random
import os
import joblib
from tqdm import tqdm
from .modules import RunModel
from .utils import parse_PDB, StructureDatasetPDB, tied_featurize, _S_to_seq
from colabdesign.shared.prng import SafeKey
from colabdesign.mpnn.jax_weights import __file__ as mpnn_path
class MPNN_wrapper:
def __init__(self,
model_name="v_48_020", verbose=False):
self.model_name = model_name
backbone_noise = 0.00 # Standard deviation of Gaussian noise to add to backbone atoms
hidden_dim = 128
num_layers = 3
path = os.path.join(os.path.dirname(mpnn_path), f'{model_name}.pkl')
checkpoint = joblib.load(path)
params = jax.tree_util.tree_map(jnp.array, checkpoint['model_state_dict'])
if verbose:
print('Number of edges:', checkpoint['num_edges'])
noise_level_print = checkpoint['noise_level']
print(f'Training noise level: {noise_level_print}A')
config = {'num_letters': 21,
'node_features': hidden_dim,
'edge_features': hidden_dim,
'hidden_dim': hidden_dim,
'num_encoder_layers': num_layers,
'num_decoder_layers': num_layers,
'augment_eps': backbone_noise,
'k_neighbors': checkpoint['num_edges'],
'dropout': 0.0
}
model = RunModel(config)
model.params = params
self.model = model
self.alphabet = 'ACDEFGHIKLMNPQRSTVWYX'
self.max_length = 20000
seed = random.randint(0,2147483647)
seed = jax.random.PRNGKey(seed)
self.safe_key = SafeKey(seed)
def prep_inputs(self, pdb_path,
target_chain, fixed_chain=None,
ishomomer=False, omit_AAs='X'):
"""generate input for score and sampling function
Args:
pdb_path (str): the path of the pdb file
target_chain (str): chain ID of the protein sequence
fixed_chain (str, optional): chain ID of the protein sequence that should be fixed. Defaults to None.
ishomomer (bool, optional): for tie sampling. Defaults to False.
omit_AAs (str, optional): aas should not be generated in sampling. Defaults to 'X'.
Returns:
dict: input dictionary
"""
# initialize some var
fixed_positions_dict = None
pssm_dict = None
omit_AA_dict = None
bias_by_res_dict = None
bias_AAs_np = np.zeros(len(self.alphabet))
pssm_threshold = 0.0
pssm_multi = 0.0
pssm_log_odds_flag = 0
pssm_bias_flag = 0
# fixed chain
if fixed_chain is None:
fixed_chain = ''
fixed_chain_list = []
else:
fixed_chain_list = re.sub("[^A-Za-z]+",",", fixed_chain).split(",")
# design chains
if target_chain == '':
designed_chain_list = []
else:
designed_chain_list = re.sub("[^A-Za-z]+",",", target_chain).split(",")
#chain list
chain_list = list(set(designed_chain_list + fixed_chain_list))
# omit AAs
omit_AAs_list = omit_AAs
omit_AAs_np = np.array([AA in omit_AAs_list for AA in self.alphabet]).astype(np.float32)
# prepare input
pdb_dict_list = parse_PDB(pdb_path, input_chain_list=chain_list)
dataset_valid = StructureDatasetPDB(pdb_dict_list, truncate=None, max_length=self.max_length)
chain_id_dict = {}
chain_id_dict[pdb_dict_list[0]['name']]= (designed_chain_list, fixed_chain_list)
if ishomomer:
# haven't tested
tied_positions_dict = self.make_tied_positions_for_homomers(pdb_dict_list)
else:
tied_positions_dict = None
return {'dataset_valid': dataset_valid,
'chain_id_dict': chain_id_dict,
'fixed_positions_dict': fixed_positions_dict,
'omit_AA_dict': omit_AA_dict,
'tied_positions_dict': tied_positions_dict,
'pssm_dict': pssm_dict,
'bias_by_res_dict': bias_by_res_dict,
'pssm_threshold': pssm_threshold,
'omit_AAs_np': omit_AAs_np,
'bias_AAs_np': bias_AAs_np,
'pssm_multi': pssm_multi,
'pssm_log_odds_flag': pssm_log_odds_flag,
'pssm_bias_flag': pssm_bias_flag,
}
def score(self, inputs, seq=None, order=None, key=None, unconditional=False):
"""get the output of MPNN
Args:
inputs (dict): output of the prep_input function
seq (str, optional): the input sequence.
If not provided, the original sequence will be used.
Defaults to None.
order (array, optional): the decoding order.
If not provided, the decoding order is random.
Defaults to None.
key (jax.random.PRNGkey, optional): the random seed. Defaults to None.
Returns:
logits
log_probs
"""
protein = inputs['dataset_valid'][0]
batch_clones = [copy.deepcopy(protein)]
(X, S, mask, lengths, chain_M, chain_idx, chain_list_list,
visible_list_list, masked_list_list, masked_chain_length_list_list,
chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask,
tied_pos_list_of_lists_list, pssm_coef, pssm_bias,
pssm_log_odds_all, bias_by_res_all, tied_beta) = tied_featurize(batch_clones,
inputs['chain_id_dict'], inputs['fixed_positions_dict'],
inputs['omit_AA_dict'], inputs['tied_positions_dict'],
inputs['pssm_dict'], inputs['bias_by_res_dict'])
score_input = {'X': X,
'S': S,
'mask': mask,
'chain_M': chain_M * chain_M_pos,
'residue_idx': residue_idx,
'chain_idx': chain_idx}
if unconditional:
score_input["S"] = None
else:
if seq is not None:
S = np.asarray([self.alphabet.index(a) for a in seq], dtype=np.int32)
S = S[None, :]
score_input['S'] = jnp.array(S)
if order is None:
if key is not None:
self.safe_key = SafeKey(key)
self.safe_key, used_key = self.safe_key.split()
order = jax.random.normal(used_key.get(), (chain_M.shape[1],))
score_input['randn'] = jnp.expand_dims(order, 0)
self.safe_key, used_key = self.safe_key.split()
return self.model.score(self.model.params, used_key.get(), score_input)
def sampling(self, inputs,
sample_num, batch_size,
sampling_temp=0.1, order=None, key=None):
"""sample sequences from the given protein structure
Args:
inputs (dict): output of the prep_input function
sample_num (int): number of sequences you want to generate
batch_size (int): size of one batch
sampling_temp (float, optional): sampling temperature. Defaults to 0.1.
order (array, optional): the sampling order.
If not provided, the order is random.
Defaults to None.
key (jax.random.PRNGkey, optional): the random seed. Defaults to None.
Returns:
seq_gen (list): generated sequence
"""
NUM_BATCHES = sample_num//batch_size
BATCH_COPIES = batch_size
if key is not None:
self.safe_key = SafeKey(key)
protein = inputs['dataset_valid'][0]
batch_clones = [copy.deepcopy(protein) for i in range(BATCH_COPIES)]
(X, S, mask, lengths, chain_M, chain_idx, chain_list_list,
visible_list_list, masked_list_list, masked_chain_length_list_list,
chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask,
tied_pos_list_of_lists_list, pssm_coef, pssm_bias,
pssm_log_odds_all, bias_by_res_all, tied_beta) = tied_featurize(batch_clones,
inputs['chain_id_dict'], inputs['fixed_positions_dict'],
inputs['omit_AA_dict'], inputs['tied_positions_dict'],
inputs['pssm_dict'], inputs['bias_by_res_dict'])
pssm_log_odds_mask = jax.lax.convert_element_type((pssm_log_odds_all > inputs['pssm_threshold']),
jnp.float32) # 1.0 for true, 0.0 for false
if order is None:
self.safe_key, used_key = self.safe_key.split()
order = jax.random.normal(used_key.get(), (chain_M.shape[1],))
randn_1 = jnp.expand_dims(order, 0)
# sample input
sample_input = {'X': X,
'randn': randn_1,
'S_true': S,
'chain_mask': chain_M,
'chain_idx': chain_idx,
'residue_idx': residue_idx,
'mask': mask,
'temperature': sampling_temp,
'omit_AAs_np': inputs['omit_AAs_np'],
'bias_AAs_np': inputs['bias_AAs_np'],
'chain_M_pos': chain_M_pos,
'omit_AA_mask': omit_AA_mask,
'pssm_coef': pssm_coef,
'pssm_bias': pssm_bias,
'pssm_multi': inputs['pssm_multi'],
'pssm_log_odds_flag': bool(inputs['pssm_log_odds_flag']),
'pssm_log_odds_mask': pssm_log_odds_mask,
'pssm_bias_flag': bool(inputs['pssm_bias_flag']),
'bias_by_res': bias_by_res_all
}
seq_gen = []
for _ in tqdm(range(NUM_BATCHES)):
self.safe_key, used_key = self.safe_key.split()
sample_input.update({'key': used_key.get()})
self.safe_key, used_key = self.safe_key.split()
if inputs['tied_positions_dict'] is None:
sample_dict = self.model.sample(self.model.params, used_key.get(), sample_input)
else:
sample_input.update({'tied_pos': tied_pos_list_of_lists_list[0],
'tied_beta': tied_beta,
'bias_by_res': bias_by_res_all,
})
sample_dict = self.model.tied_sample(self.model.params, used_key.get(), sample_input)
S_sample = sample_dict["S"]
for b_ix in range(BATCH_COPIES):
masked_chain_length_list = masked_chain_length_list_list[b_ix]
masked_list = masked_list_list[b_ix]
seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix])
start = 0
end = 0
list_of_AAs = []
for mask_l in masked_chain_length_list:
end += mask_l
list_of_AAs.append(seq[start:end])
start = end
seq = "".join(list(np.array(list_of_AAs)[np.argsort(masked_list)]))
l0 = 0
for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]:
l0 += mc_length
seq = seq[:l0] + '/' + seq[l0:]
l0 += 1
seq_gen.append(seq)
return seq_gen
@staticmethod
def make_tied_positions_for_homomers(pdb_dict_list):
my_dict = {}
for result in pdb_dict_list:
all_chain_list = sorted([item[-1:] for item in list(result) if item[:9]=='seq_chain']) # A, B, C, ...
tied_positions_list = []
chain_length = len(result[f"seq_chain_{all_chain_list[0]}"])
for i in range(1,chain_length+1):
temp_dict = {}
for j, chain in enumerate(all_chain_list):
temp_dict[chain] = [i] #needs to be a list
tied_positions_list.append(temp_dict)
my_dict[result['name']] = tied_positions_list
return my_dict
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