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Make the path handling more platform agnostic. #88

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Make the path handling more platform agnostic. #88

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a9f022c
Make the path handling more platform agnostic.
Feb 9, 2024
7bc7879
Remove whitespace.
Feb 9, 2024
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6 changes: 3 additions & 3 deletions helper_scripts/parse_multiple_chains.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,8 @@ def main(args):

import numpy as np
import os, time, gzip, json
import glob
import glob
from os import path

folder_with_pdbs_path = args.input_path
save_path = args.output_path
Expand Down Expand Up @@ -138,8 +139,7 @@ def parse_PDB_biounits(x, atoms=['N','CA','C'], chain=None):
coords_dict_chain['O_chain_' + letter] = xyz[:, 3, :].tolist()
my_dict['coords_chain_'+letter]=coords_dict_chain
s += 1
fi = biounit.rfind("/")
my_dict['name']=biounit[(fi+1):-4]
my_dict['name']=path.basename(biounit)[:-4]
my_dict['num_of_chains'] = s
my_dict['seq'] = concat_seq
if s < len(chain_alphabet):
Expand Down
33 changes: 15 additions & 18 deletions protein_mpnn_run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,24 +35,22 @@ def main(args):


if args.path_to_model_weights:
model_folder_path = args.path_to_model_weights
if model_folder_path[-1] != '/':
model_folder_path = model_folder_path + '/'
# This adds the trailing slash if missing
model_folder_path = os.path.join(args.path_to_model_weights, '')
else:
file_path = os.path.realpath(__file__)
k = file_path.rfind("/")
file_path = os.path.dirname(os.path.realpath(__file__))
if args.ca_only:
print("Using CA-ProteinMPNN!")
model_folder_path = file_path[:k] + '/ca_model_weights/'
model_folder_path = os.path.join(file_path, 'ca_model_weights', '')
if args.use_soluble_model:
print("WARNING: CA-SolubleMPNN is not available yet")
sys.exit()
else:
if args.use_soluble_model:
print("Using ProteinMPNN trained on soluble proteins only!")
model_folder_path = file_path[:k] + '/soluble_model_weights/'
model_folder_path = os.path.join(file_path, 'soluble_model_weights', '')
else:
model_folder_path = file_path[:k] + '/vanilla_model_weights/'
model_folder_path = os.path.join(file_path, 'vanilla_model_weights', '')

checkpoint_path = model_folder_path + f'{args.model_name}.pt'
folder_for_outputs = args.out_folder
Expand Down Expand Up @@ -188,9 +186,8 @@ def main(args):
print(f'Training noise level: {noise_level_print}A')

# Build paths for experiment
base_folder = folder_for_outputs
if base_folder[-1] != '/':
base_folder = base_folder + '/'
# Add trailing slash if missing
base_folder = os.path.join(folder_for_outputs, "")
if not os.path.exists(base_folder):
os.makedirs(base_folder)

Expand Down Expand Up @@ -243,9 +240,9 @@ def main(args):
loop_c = len(fasta_seqs)
for fc in range(1+loop_c):
if fc == 0:
structure_sequence_score_file = base_folder + '/score_only/' + batch_clones[0]['name'] + f'_pdb'
structure_sequence_score_file = os.path.join(base_folder, 'score_only', batch_clones[0]['name'] + f'_pdb')
else:
structure_sequence_score_file = base_folder + '/score_only/' + batch_clones[0]['name'] + f'_fasta_{fc}'
structure_sequence_score_file = os.path.join(base_folder, 'score_only', batch_clones[0]['name'] + f'_fasta_{fc}')
native_score_list = []
global_native_score_list = []
if fc > 0:
Expand Down Expand Up @@ -285,7 +282,7 @@ def main(args):
elif args.conditional_probs_only:
if print_all:
print(f'Calculating conditional probabilities for {name_}')
conditional_probs_only_file = base_folder + '/conditional_probs_only/' + batch_clones[0]['name']
conditional_probs_only_file = os.path.join(base_folder, 'conditional_probs_only', batch_clones[0]['name'])
log_conditional_probs_list = []
for j in range(NUM_BATCHES):
randn_1 = torch.randn(chain_M.shape, device=X.device)
Expand All @@ -297,7 +294,7 @@ def main(args):
elif args.unconditional_probs_only:
if print_all:
print(f'Calculating sequence unconditional probabilities for {name_}')
unconditional_probs_only_file = base_folder + '/unconditional_probs_only/' + batch_clones[0]['name']
unconditional_probs_only_file = os.path.join(base_folder, 'unconditional_probs_only', batch_clones[0]['name'])
log_unconditional_probs_list = []
for j in range(NUM_BATCHES):
log_unconditional_probs = model.unconditional_probs(X, mask, residue_idx, chain_encoding_all)
Expand All @@ -314,9 +311,9 @@ def main(args):
global_scores = _scores(S, log_probs, mask) #score the whole structure-sequence
global_native_score = global_scores.cpu().data.numpy()
# Generate some sequences
ali_file = base_folder + '/seqs/' + batch_clones[0]['name'] + '.fa'
score_file = base_folder + '/scores/' + batch_clones[0]['name'] + '.npz'
probs_file = base_folder + '/probs/' + batch_clones[0]['name'] + '.npz'
ali_file = os.path.join(base_folder, 'seqs', batch_clones[0]['name'] + '.fa')
score_file = os.path.join(base_folder, 'scores', batch_clones[0]['name'] + '.npz')
probs_file = os.path.join(base_folder, 'probs', batch_clones[0]['name'] + '.npz')
if print_all:
print(f'Generating sequences for: {name_}')
t0 = time.time()
Expand Down
4 changes: 2 additions & 2 deletions protein_mpnn_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import json, time, os, sys, glob
import shutil
import numpy as np
from os import path
import torch
from torch import optim
from torch.utils.data import DataLoader
Expand Down Expand Up @@ -177,8 +178,7 @@ def parse_PDB(path_to_pdb, input_chain_list=None, ca_only=False):
coords_dict_chain['O_chain_' + letter] = xyz[:, 3, :].tolist()
my_dict['coords_chain_'+letter]=coords_dict_chain
s += 1
fi = biounit.rfind("/")
my_dict['name']=biounit[(fi+1):-4]
my_dict['name']=path.basename(biounit)[:-4]
my_dict['num_of_chains'] = s
my_dict['seq'] = concat_seq
if s <= len(chain_alphabet):
Expand Down