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Missing aflows #126

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Jul 29, 2024
Merged

Missing aflows #126

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fb5bd35
fix(datasets): multiprocessed check for protein conformations #125
jyaacoub Jul 24, 2024
822e277
refactor(datasets): return code to map to if mismatch seq #125
jyaacoub Jul 25, 2024
4463dfa
fix(dataset): undefined name 'process_protein_multiprocessing' #125
jyaacoub Jul 25, 2024
60e8a70
fix: filter out mismatched and deleted csvs #126
jyaacoub Jul 25, 2024
bbeecea
revert(datasets): using codes is a bad idea for pdbbind alphaflow fil…
jyaacoub Jul 25, 2024
c395e7b
results(pdbbind): #113 gvpl and gvpl_aflow
jyaacoub Jul 29, 2024
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230 changes: 143 additions & 87 deletions playground.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,39 +1,154 @@
# %%
#%%
import os
import logging
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
train_df = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/full/XY.csv')
test_df = pd.read_csv('/cluster/home/t122995uhn/projects/MutDTA/splits/davis/test.csv')
train_df = train_df[~train_df.prot_id.isin(set(test_df.prot_id))]

# %%
from src.utils.residue import Chain
from multiprocessing import Pool, cpu_count
from src.data_prep.datasets import BaseDataset

# df = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base.csv", index_col=0)
df = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base_filtered.csv", index_col=0)
logging.getLogger().setLevel(logging.DEBUG)

def process_protein_multiprocessing(args):
"""
Checks if protein has conf file and correct sequence, returns:
- None, None - if it has a conf file and is correct
- pid, None - is missing a conf file
- pid, seq - has a conf file but is not correct sequence.
"""
group_codes, code, pid, seq, af_conf_dir, is_pdbbind, files = args
MIN_MODEL_COUNT = 5

correct_seq = False
matching_code = None
af_confs = []
if is_pdbbind:
for c in group_codes:
af_fp = os.path.join(af_conf_dir, f'{c}.pdb')
if os.path.exists(af_fp):
af_confs = [af_fp]
matching_code = code
if Chain(af_fp).sequence == seq:
correct_seq = True
break

else:
af_confs = [os.path.join(af_conf_dir, f) for f in files if f.startswith(pid)]

if len(af_confs) == 0:
return pid, None

# either all models in one pdb file (alphaflow) or spread out across multiple files (AF2 msa subsampling)
model_count = len(af_confs) if len(af_confs) > 1 else 5# Chain.get_model_count(af_confs[0])

if model_count < MIN_MODEL_COUNT:
return pid, None
elif not correct_seq: # final check
af_seq = Chain(af_confs[0]).sequence
if seq != af_seq:
logging.debug(f'Mismatched sequence for {pid}')
# if matching_code == code: # something wrong here -> incorrect seq but for the right code?
# return pid, af_seq
return pid, matching_code

if matching_code != code:
return None, matching_code
return None, None

#%% check_missing_confs method
af_conf_dir:str = '/cluster/home/t122995uhn/projects/data/pdbbind/alphaflow_io/out_pdb_MD-distilled/'
is_pdbbind=True

df_unique:pd.DataFrame = df.drop_duplicates('prot_id')
df_pid_groups = df.groupby(['prot_id']).groups

missing = set()
mismatched = {}
# total of 3728 unique proteins with alphaflow confs (named by pdb ID)
files = None
if not is_pdbbind:
files = [f for f in os.listdir(af_conf_dir) if f.endswith('.pdb')]

with Pool(processes=cpu_count()) as pool:
tasks = [(df_pid_groups[pid], code, pid, seq, af_conf_dir, is_pdbbind, files) \
for code, (pid, seq) in df_unique[['prot_id', 'prot_seq']].iterrows()]

for pid, new_seq in tqdm(pool.imap_unordered(process_protein_multiprocessing, tasks),
desc='Filtering out proteins with missing PDB files for multiple confirmations',
total=len(tasks)):
if new_seq is not None:
mismatched[pid] = new_seq
elif pid is not None: # just pid -> missing af files
missing.add(pid)

print(len(missing),len(mismatched))

#%% make subsitutions for rows
df = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base.csv", index_col=0)
df_mismatched = pd.DataFrame.from_dict(mismatched, orient='index', columns=['code'])
df_mismatched_sub = df.loc[df_mismatched['code']][['prot_id', 'prot_seq']].reset_index()
df_mismatched = df_mismatched.merge(df_mismatched_sub, on='code')

df_mismatched = df_mismatched.merge(df_mismatched_sub, on='code')
dff = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base_filtered.csv")
dffm = dff.merge(df_mismatched, on='code')
#%%
from src.data_prep.datasets import BaseDataset
import pandas as pd
csv_p = "/cluster/home/t122995uhn/projects/data/PDBbindDataset/nomsa_binary_original_binary/full/XY.csv"

davis_test_df = pd.read_csv(f"/home/jean/projects/MutDTA/splits/davis/test.csv")
davis_test_df['gene'] = davis_test_df['prot_id'].str.split('(').str[0]

#%% ONCO KB MERGE
onco_df = pd.read_csv("../data/oncoKB_DrugGenePairList.csv")
davis_join_onco = davis_test_df.merge(onco_df.drop_duplicates("gene"), on="gene", how="inner")
df = pd.read_csv(csv_p, index_col=0)

# %%
onco_df = pd.read_csv("../data/oncoKB_DrugGenePairList.csv")
onco_df.merge(davis_test_df.drop_duplicates("gene"), on="gene", how="inner").value_counts("gene")








import os
from tqdm import tqdm
alphaflow_dir = "/cluster/home/t122995uhn/projects/data/pdbbind/alphaflow_io/out_pdb_MD-distilled/"
ln_dir = "/cluster/home/t122995uhn/projects/data/pdbbind/alphaflow_io/out_pid_ln/"

os.makedirs(ln_dir, exist_ok=True)

# First, check and remove any broken links in the destination directory
for link_file in tqdm(os.listdir(ln_dir), desc="Checking for broken links"):
ln_p = os.path.join(ln_dir, link_file)
if os.path.islink(ln_p) and not os.path.exists(ln_p):
print(f"Removing broken link: {ln_p}")
os.remove(ln_p)


# %% files are .pdb with 50 "models" in each
for file in tqdm(os.listdir(alphaflow_dir)):
if not file.endswith('.pdb'):
continue

code, _ = os.path.splitext(file)
pid = df.loc[code].prot_id
src, dst = f"{alphaflow_dir}/{file}", f"{ln_dir}/{pid}.pdb"
if not os.path.exists(dst):
os.symlink(src,dst)

# %%
from src.train_test.splitting import resplit
#%%
########################################################################
########################## BUILD DATASETS ##############################
########################################################################
import os
from src.data_prep.init_dataset import create_datasets
from src import cfg
import logging
cfg.logger.setLevel(logging.DEBUG)

db_p = lambda x: f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/nomsa_{x}_gvp_binary'

db = resplit(dataset=db_p('binary'), split_files=db_p('aflow'), use_train_set=True)

splits = '/cluster/home/t122995uhn/projects/MutDTA/splits/davis/'
create_datasets(cfg.DATA_OPT.davis,
feat_opt=cfg.PRO_FEAT_OPT.nomsa,
edge_opt=[cfg.PRO_EDGE_OPT.aflow, cfg.PRO_EDGE_OPT.binary],
ligand_features=[cfg.LIG_FEAT_OPT.original, cfg.LIG_FEAT_OPT.gvp],
ligand_edges=cfg.LIG_EDGE_OPT.binary, overwrite=True,
k_folds=5,
test_prots_csv=f'{splits}/test.csv',
val_prots_csv=[f'{splits}/val{i}.csv' for i in range(5)],
data_root=os.path.abspath('../data/test/'))


# %%
Expand Down Expand Up @@ -69,62 +184,3 @@
models=models, metrics=['cindex', 'mse'],
fig_scale=(10,5), add_stats=True, title_postfix=" validation set performance")
plt.xticks(rotation=45)

#%%
########################################################################
########################## BUILD DATASETS ##############################
########################################################################
from src.data_prep.init_dataset import create_datasets
from src import cfg
import logging
cfg.logger.setLevel(logging.DEBUG)

splits = '/cluster/home/t122995uhn/projects/MutDTA/splits/pdbbind/'
create_datasets(cfg.DATA_OPT.PDBbind,
feat_opt=cfg.PRO_FEAT_OPT.nomsa,
edge_opt=[cfg.PRO_EDGE_OPT.binary, cfg.PRO_EDGE_OPT.aflow],
ligand_features=[cfg.LIG_FEAT_OPT.original, cfg.LIG_FEAT_OPT.gvp],
ligand_edges=cfg.LIG_EDGE_OPT.binary,
k_folds=5,
test_prots_csv=f'{splits}/test.csv',
val_prots_csv=[f'{splits}/val{i}.csv' for i in range(5)])

# %%
from src.utils.loader import Loader

db_aflow = Loader.load_dataset('../data/DavisKibaDataset/davis/nomsa_aflow_original_binary/full')
db = Loader.load_dataset('../data/DavisKibaDataset/davis/nomsa_binary_original_binary/full')

# %%
# 5-fold cross validation + test set
import pandas as pd
from src import cfg
from src.train_test.splitting import balanced_kfold_split
from src.utils.loader import Loader
test_df = pd.read_csv('/cluster/home/t122995uhn/projects/MutDTA/splits/pdbbind_test.csv')
test_prots = set(test_df.prot_id)
db = Loader.load_dataset(f'{cfg.DATA_ROOT}/PDBbindDataset/nomsa_binary_original_binary/full/')

train, val, test = balanced_kfold_split(db,
k_folds=5, test_split=0.1, val_split=0.1,
test_prots=test_prots, random_seed=0, verbose=True
)

#%%
db.save_subset_folds(train, 'train')
db.save_subset_folds(val, 'val')
db.save_subset(test, 'test')

#%%
import shutil, os

src = "/cluster/home/t122995uhn/projects/data/PDBbindDataset/nomsa_binary_original_binary/"
dst = "/cluster/home/t122995uhn/projects/MutDTA/splits/pdbbind"
os.makedirs(dst, exist_ok=True)

for i in range(5):
sfile = f"{src}/val{i}/XY.csv"
dfile = f"{dst}/val{i}.csv"
shutil.copyfile(sfile, dfile)

# %%
14 changes: 12 additions & 2 deletions results/v113/model_media/model_stats.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ DGM_kiba3D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_o
DGM_kiba1D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.6572547696170707,0.4910169881324348,0.4223030308489566,0.5056608650145961,0.4887140404038343,0.7110983511544631
DGM_kiba0D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7034265325632763,0.5925355722791278,0.5277625252545413,0.4342528236949791,0.4414575484107895,0.6589786215765874
DGM_kiba2D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7048145943290888,0.5949726563519002,0.5309972890766975,0.4319786589236721,0.4397173516202035,0.657250834098879
DGM_kiba4D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.701385350951551,0.5879330884340543,0.5238489684293576,0.43711720760821254,0.44305434527103205,0.6611484005941575
DGM_kiba4D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.701385350951551,0.5879330884340543,0.5238489684293576,0.4371172076082125,0.443054345271032,0.6611484005941575
DGM_PDBbind0D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_originalLF_binaryLE,0.6655853516013849,0.4884783169365537,0.4768266531852068,2.716448143336347,1.29524747639392,1.648165083763258
DGM_PDBbind1D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_originalLF_binaryLE,0.6616134720685233,0.4760514005042608,0.465349083935074,2.7772597147478297,1.309049959958126,1.6665112405104952
DGM_PDBbind2D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_originalLF_binaryLE,0.6547720831157129,0.4674109621848075,0.4473761042353416,2.911396055546887,1.350302681084595,1.7062813529857517
Expand All @@ -46,6 +46,16 @@ GVPLM_kiba4D_nomsaF_binaryE_32B_3.372637625954074e-05LR_0.09399264336737133D_200
GVPLM_kiba2D_nomsaF_binaryE_32B_3.372637625954074e-05LR_0.09399264336737133D_2000E_gvpLF_binaryLE,0.6999996243009051,0.5844167249718527,0.5172933123278608,1.1060115689114345,0.8743866055307609,1.051670846278166
GVPLM_kiba4D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.7028775926753292,0.5511820968815552,0.5274587057439738,0.5191856014604727,0.464434987594827,0.7205453500373676
GVPLM_kiba3D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.6974533040075589,0.5366909353583093,0.5092679820627712,0.5267452909346243,0.4756001094118965,0.7257722031978245
GVPLM_kiba2D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.6914933128392398,0.5535320315157304,0.49916906416324947,0.5167043183823063,0.46841519355949945,0.7188214787986696
GVPLM_kiba2D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.6914933128392398,0.5535320315157304,0.4991690641632494,0.5167043183823063,0.4684151935594994,0.7188214787986696
GVPLM_kiba0D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.667835957156033,0.5118338017508158,0.4512406693094591,0.529732104745498,0.4987863530176246,0.7278269744558098
GVPLM_kiba1D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.673244203521782,0.5584520336614822,0.4601543459719329,0.4634166140494674,0.4859944296833685,0.6807470999199831
DGM_PDBbind0D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5,,,3.4182907626961336,1.4994075870215695,1.8488620182956144
DGM_PDBbind1D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5007066821937181,0.038214548483191,0.0037893479451263,3.4747609352089994,1.5185222442497732,1.864071064956752
DGM_PDBbind2D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5000473143129389,0.0065482511633214,0.0077853856938565,3.55397849703766,1.5411690941151477,1.8851998559934329
DGM_PDBbind3D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5,,,3.6010645245915747,1.553675811370748,1.8976471022272752
DGM_PDBbind4D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5,,,3.472537530411251,1.5178422226480432,1.8634745853945127
GVPLM_PDBbind0D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.5675107531975828,0.2012549479389283,0.1972868580568649,3.521371768804649,1.5340581613048625,1.876531845934049
GVPLM_PDBbind1D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.6351386534675363,0.4162576037033809,0.393776329694548,2.952582892735166,1.3614234164872832,1.7183081483643048
GVPLM_PDBbind2D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.6073794252279185,0.300904510298725,0.3141960696982953,3.5527159664685373,1.497829202900039,1.8848649730069624
GVPLM_PDBbind3D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.5544194125815396,0.1962498205943712,0.1602389373350445,3.559013089849939,1.5262607894024975,1.886534677616592
GVPLM_PDBbind4D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.6222734472562935,0.38283753210340116,0.3603062880559139,3.0631035175351524,1.389417946315663,1.7501724250870692
12 changes: 11 additions & 1 deletion results/v113/model_media/model_stats_val.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ DGM_kiba3D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_o
DGM_kiba1D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7169797416293802,0.6527045139380728,0.5592661699267665,0.3368578002681257,0.3921905937173818,0.5803945212251109
DGM_kiba0D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7128444726133566,0.6469405510839773,0.5484484334329713,0.336774733412883,0.4184534194805188,0.5803229561312244
DGM_kiba2D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7033453012745192,0.6072209463563496,0.5174763874796517,0.4390207180018329,0.4649270940615934,0.6625863853127628
DGM_kiba4D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7023830999958538,0.639949133525287,0.5151496892582399,0.40105347469958885,0.4409018537119612,0.6332878292684843
DGM_kiba4D_nomsaF_aflowE_64B_0.0001139464546302261LR_0.4321620419748407D_2000E_originalLF_binaryLE,0.7023830999958538,0.639949133525287,0.5151496892582399,0.4010534746995888,0.4409018537119612,0.6332878292684843
DGM_PDBbind0D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_originalLF_binaryLE,0.667185125919345,0.4916330475527956,0.4742033800137094,2.573515761007047,1.247796092498501,1.604218115159858
DGM_PDBbind1D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_originalLF_binaryLE,0.6918743579083576,0.5545547118073524,0.5483030524092783,2.511534059876986,1.2495642605282011,1.5847820228274254
DGM_PDBbind2D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_originalLF_binaryLE,0.6664571269002308,0.5086157319827234,0.4766146235124788,2.7894558492182133,1.3283703922279295,1.6701664136301548
Expand All @@ -49,3 +49,13 @@ GVPLM_kiba3D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_200
GVPLM_kiba2D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.7926024955332317,0.7374112888096318,0.7063431040772125,0.32468252989801,0.3579667507508575,0.5698092048203591
GVPLM_kiba0D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.6669854507035494,0.5525438589938947,0.4355859541059588,0.4115128362890195,0.4456888421443692,0.6414926626930503
GVPLM_kiba1D_nomsaF_aflowE_16B_0.00010990897170411903LR_0.03599877069828837D_2000E_gvpLF_binaryLE,0.6989377849970573,0.6478510815311954,0.515693387777559,0.3561253610107396,0.4216625264430977,0.5967623991261007
DGM_PDBbind0D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5125715405607719,0.2793381742071084,0.1926536143404282,3.346598030093213,1.4805904418459894,1.829370938353732
DGM_PDBbind1D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5107971942443016,0.0312432003865885,0.0308374960271485,3.5631682917172403,1.5458264870189105,1.887635635316636
DGM_PDBbind2D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5277274707626068,0.358671071267003,0.2830406326274937,3.6322764505727583,1.5248606967243108,1.905853208033808
DGM_PDBbind3D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.51955000029021,0.3102068840467967,0.2386819219389774,3.410729951552425,1.478064934361954,1.8468161661498488
DGM_PDBbind4D_nomsaF_aflowE_128B_0.0009185598967356679LR_0.22880989869337157D_2000E_originalLF_binaryLE,0.5275270713061986,0.3631722071531414,0.2847957039355699,3.1869078443560386,1.4534702110049291,1.7851912626819677
GVPLM_PDBbind0D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.5596497975350556,0.2543017795335043,0.1766358807164376,3.113015575178155,1.445382858916236,1.7643739896003214
GVPLM_PDBbind1D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.6600679137627058,0.4711013339427787,0.4650543790596504,2.884138926557529,1.3550213658092014,1.698275279970102
GVPLM_PDBbind2D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.6141731947137768,0.3644634107975009,0.3328003860926398,3.1989141378067982,1.408361079518094,1.788550848538223
GVPLM_PDBbind3D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.5536881344717306,0.192581089791613,0.1606937190574807,3.4912679264408366,1.4810306765899366,1.8684934911422184
GVPLM_PDBbind4D_nomsaF_aflowE_128B_0.00020048122460779208LR_0.042268679447260635D_2000E_gvpLF_binaryLE,0.628428758706968,0.3807074065686098,0.3759327635419598,2.9551973425576046,1.3590244197523815,1.7190687428249065
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