From b56c8e25cdf9b06f25d0a157dbdbe6ec97eeff29 Mon Sep 17 00:00:00 2001 From: Jean Charle Yaacoub <50300488+jyaacoub@users.noreply.github.com> Date: Mon, 6 Nov 2023 11:35:32 -0500 Subject: [PATCH 01/28] results: davis-shannon CV #46 (#56) * created ChemEsmDTA class * results: davis-shannon CV #46 --------- Co-authored-by: papiClafoutis Co-authored-by: Aleem Aamir --- ...nnonF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...nnonF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...nnonF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...nnonF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...nnonF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...ryE_64B_0.0001LR_0.4D_2000E_chemGPTLF.json | 221 + results/model_media/model_stats.csv | 8 +- results/model_media/model_stats_val.csv | 6 + src/models/lig_mod.py | 50 + src/utils/config.py | 2 +- src/utils/loader.py | 10 +- 11 files changed, 20328 insertions(+), 4 deletions(-) create mode 100644 results/model_media/davis/train_log/DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis1D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF.json diff --git a/results/model_media/davis/train_log/DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json b/results/model_media/davis/train_log/DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E.json new file mode 100644 index 00000000..089c6cc8 --- /dev/null +++ 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+DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8262970114209492,0.6902457009938884,0.6196677619083105,0.4657317363498777,0.3800800188713128,0.6824454090620566 +DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8387058574888743,0.7253796342930805,0.6397184730308061,0.418395340180838,0.3501044126973091,0.646834863145794 +DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8496612461728558,0.7437110771416391,0.6565426331150037,0.391104889452298,0.3245244547831619,0.6253837937237405 +DGM_davis1D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8312754273934078,0.6999447666182411,0.6286994684318588,0.448984799382394,0.354222957680865,0.6700632801328499 +DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8164859423418647,0.693536266777011,0.6013830893679215,0.46093123143684783,0.36639131860035223,0.6789191641402147 diff --git a/results/model_media/model_stats_val.csv b/results/model_media/model_stats_val.csv index f59dc6c7..5ccbcd91 100644 --- a/results/model_media/model_stats_val.csv +++ b/results/model_media/model_stats_val.csv @@ -4,3 +4,9 @@ DGM_davis0D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8124934102025569,0.69098278 DGM_davis3D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8195586154273969,0.717774769130841,0.5720718176514452,0.3657891513377326,0.3382081135692242,0.6048050523414406 DGM_davis2D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8233405467261639,0.7040322378359025,0.5687852322102779,0.3543421502907676,0.3433588875976263,0.5952664531877868 DGM_davis1D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.82715800662027,0.7083018179748569,0.6132060396459778,0.4895027135441312,0.3843769813986386,0.699644705221251 +DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF,0.7380363162506938,0.4574955465284291,0.4466001977109979,0.6649520759195586,0.551816601340746,0.8154459368465568 +DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.823990429488312,0.7117146794067782,0.600087030044339,0.4287954343848595,0.3553209463269589,0.654824735623861 +DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.827734580656503,0.702739642357159,0.5768866122321109,0.3738840686242872,0.3239032915296037,0.6114606026755013 +DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8292712666904243,0.7228505402132716,0.5833504792222683,0.3717008538318646,0.3222885614393665,0.6096727432253016 +DGM_davis1D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8248946179115073,0.7385253536342127,0.598720558202774,0.404841846456497,0.3498974830004453,0.6362718337758612 +DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8299184455370965,0.7248253045920677,0.6116541529841135,0.4180130896587404,0.3448278840382894,0.6465393179526984 diff --git a/src/models/lig_mod.py b/src/models/lig_mod.py index f7588cd2..413e926f 100644 --- a/src/models/lig_mod.py +++ b/src/models/lig_mod.py @@ -6,6 +6,8 @@ from selfies import encoder from src.models.prior_work import DGraphDTA +from src.models.pro_mod import EsmDTA + class ChemDTA(DGraphDTA): def __init__(self, mol_output_dim=128, dropout=0.2, *args, **kwargs): @@ -33,6 +35,54 @@ def forward_mol(self, data_mol): mol_x = data_mol.lig_seq + # get selifes from smile + selfies = [encoder(s) for s in mol_x] + + # get tokens + res = self.tokenizer(selfies, return_tensors="pt", padding=True) + + res['input_ids'] = res['input_ids'].to(data_mol.x.device) + res['attention_mask'] = res['attention_mask'].to(data_mol.x.device) + res['token_type_ids'] = res['token_type_ids'].to(data_mol.x.device) + + # model + model_output = self.model(**res).last_hidden_state + + # flatten to [L, 128] + x = torch.mean(model_output, dim=1) + + x = self.relu(self.mol_fc_g1(x)) + x = self.dropout(x) + x = self.mol_fc_g2(x) + x = self.dropout(x) + return x + +class ChemEsmDTA(EsmDTA): + def __init__(self, mol_output_dim=128, dropout=0.2, *args, **kwargs): + super(ChemEsmDTA, self).__init__(dropout=dropout, edge_weight_opt='binary', *args, **kwargs) + + print('DGraphDTA Loaded') + num_features_mol = 128 + + #### ChemGPT #### + + # get tokenizer and model + self.tokenizer = AutoTokenizer.from_pretrained("../hf_models/models--ncfrey--ChemGPT-4.7M/snapshots/7438a282460b3038e17a27e25b85b1376e9a23e2/", local_files_only=True) + self.model = AutoModel.from_pretrained("../hf_models/models--ncfrey--ChemGPT-4.7M/snapshots/7438a282460b3038e17a27e25b85b1376e9a23e2/", local_files_only=True) + + self.model.requires_grad_(False) # freeze weights + + # adding a new token '[PAD]' to the tokenizer, and then using it as the padding token + self.tokenizer.add_special_tokens({'pad_token': '[PAD]'}) + + self.mol_fc_g1 = nn.Linear(num_features_mol, 1024) + self.mol_fc_g2 = nn.Linear(1024, mol_output_dim) + + def forward_mol(self, data_mol): + # get smiles list input + mol_x = data_mol.lig_seq + + # get selifes from smile selfies = [encoder(s) for s in mol_x] diff --git a/src/utils/config.py b/src/utils/config.py index f6378654..85df2c3f 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -6,7 +6,7 @@ confProDy(verbosity='none') # stop printouts from prody -MODEL_OPT = ['DG', 'DGI', 'ED', 'EDA', 'EDI', 'EDAI', 'EAT', 'CD'] +MODEL_OPT = ['DG', 'DGI', 'ED', 'EDA', 'EDI', 'EDAI', 'EAT', 'CD', 'CED'] STRUCT_EDGE_OPT = ['anm', 'af2', 'af2-anm'] # edge options that require structural info (pdbs) EDGE_OPT = ['simple', 'binary'] + STRUCT_EDGE_OPT diff --git a/src/utils/loader.py b/src/utils/loader.py index 236b9cdc..439ea4f9 100644 --- a/src/utils/loader.py +++ b/src/utils/loader.py @@ -4,7 +4,7 @@ from torch.utils.data.distributed import DistributedSampler from torch_geometric.loader import DataLoader -from src.models.lig_mod import ChemDTA +from src.models.lig_mod import ChemDTA, ChemEsmDTA from src.models.pro_mod import EsmDTA, EsmAttentionDTA from src.models.prior_work import DGraphDTA, DGraphDTAImproved from src.data_processing.datasets import PDBbindDataset, DavisKibaDataset @@ -99,7 +99,13 @@ def init_model(model:str, pro_feature:str, pro_edge:str, dropout:float, elif model == 'CD': # this model only needs sequence, no additional features. model = ChemDTA(dropout=dropout) - + elif model == 'CED': + model = ChemEsmDTA(esm_head='facebook/esm2_t6_8M_UR50D', + num_features_pro=320, + pro_emb_dim=512, # increase embedding size + dropout=dropout, + pro_feat='esm_only', + edge_weight_opt=pro_edge) return model @staticmethod From 06e3b625ecf18ae614c7e2891fa3a4d19bf35fa2 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 6 Nov 2023 11:47:31 -0500 Subject: [PATCH 02/28] results: davis-msa CV #46 --- .gitignore | 1 + ..._msaF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ..._msaF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ..._msaF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ..._msaF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ..._msaF_binaryE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ results/model_media/model_stats.csv | 7 +- results/model_media/model_stats_val.csv | 5 + 8 files changed, 20047 insertions(+), 1 deletion(-) create mode 100644 results/model_media/davis/train_log/DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json diff --git a/.gitignore b/.gitignore index 2b2946a7..abfeec94 100644 --- a/.gitignore +++ b/.gitignore @@ -208,5 +208,6 @@ lib/mgltools_x86_64Linux2_1.5.7p1.tar.gz log_test/ slurm_out_DDP/ +slurm_out*/ /*.sh results/model_checkpoints/ours/*.model* diff --git a/results/model_media/davis/train_log/DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json b/results/model_media/davis/train_log/DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json new file mode 100644 index 00000000..e9e540a2 --- /dev/null +++ b/results/model_media/davis/train_log/DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E.json @@ -0,0 +1,4007 @@ +{ + "train_loss": [ + 3.749470305061195, + 1.0938301984129883, + 1.092295090372606, + 1.0771836521694573, + 1.0466250133041928, + 1.0590617959728328, + 1.0325610298072783, + 1.0012173459342704, + 0.9925382503467363, + 0.9843276312756466, + 0.9574051247682513, + 0.9638986230214558, + 0.9459823201660339, + 0.9411114946507463, + 0.9350660323824097, + 0.9222332728890384, + 0.9256189501867061, + 0.9177344391213321, + 0.9123923173174262, + 0.9143866011191432, + 0.9109021012966589, + 0.9067944392516483, + 0.8884477303249807, + 0.9020748644067746, + 0.9003685130851298, + 0.9043791578492013, 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0.4579036984558603, + 0.46609591300849146 + ], + "best_epoch": 873 +} \ No newline at end of file diff --git a/results/model_media/model_stats.csv b/results/model_media/model_stats.csv index 5c425f51..bbf81fd5 100644 --- a/results/model_media/model_stats.csv +++ b/results/model_media/model_stats.csv @@ -25,4 +25,9 @@ DGM_davis4D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8372000580825498,0.70972544 DGM_davis1D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8259045903365158,0.6727098716351115,0.5969862332191551,0.4777166877928197,0.3703959785636244,0.6911705200547978 DGM_davis3D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.842545467746034,0.7168413490003224,0.623085263864242,0.4159492493391408,0.3558861870119901,0.6449412758842008 DGM_davis2D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8219248012487749,0.6776357016678246,0.5900163003204868,0.4347317921133404,0.368497045519577,0.6593419386883717 -DGM_davis0D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8118490670490435,0.681294197609124,0.5718484991508404,0.42935783222492513,0.35005875178466256,0.6552540211436517 +DGM_davis0D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8118490670490435,0.681294197609124,0.5718484991508404,0.4293578322249251,0.3500587517846625,0.6552540211436517 +DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8293327722196834,0.7023779705235912,0.6229347721106537,0.4493630702908394,0.3565351886078822,0.6703454857689723 +DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8212757240173636,0.6902984939419878,0.6084680827736445,0.4717995428983164,0.3579402144362287,0.6868766577037805 +DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.826504184715153,0.6936343607518232,0.6220887747340503,0.4539645759565525,0.3707979739652987,0.6737689336534837 +DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8404119087102697,0.724508857426608,0.6426491268424466,0.419492275491693,0.3439134949420069,0.6476822334229132 +DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8234262476976882,0.6961441165080728,0.614144572586071,0.46013070593880473,0.38740075712053473,0.6783293491651416 diff --git a/results/model_media/model_stats_val.csv b/results/model_media/model_stats_val.csv index f59dc6c7..270402b6 100644 --- a/results/model_media/model_stats_val.csv +++ b/results/model_media/model_stats_val.csv @@ -4,3 +4,8 @@ DGM_davis0D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8124934102025569,0.69098278 DGM_davis3D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8195586154273969,0.717774769130841,0.5720718176514452,0.3657891513377326,0.3382081135692242,0.6048050523414406 DGM_davis2D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8233405467261639,0.7040322378359025,0.5687852322102779,0.3543421502907676,0.3433588875976263,0.5952664531877868 DGM_davis1D_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.82715800662027,0.7083018179748569,0.6132060396459778,0.4895027135441312,0.3843769813986386,0.699644705221251 +DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8363462603536834,0.7300972139373315,0.6247030031563877,0.4115841439930461,0.3520789737327426,0.6415482398020013 +DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.809188899866386,0.7272563430244341,0.5709758219318876,0.4267040368901719,0.3404274728645112,0.6532258697343301 +DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8256781625398019,0.6981605259116868,0.5810792177230069,0.3963907833649285,0.3506354182128197,0.6295957301037933 +DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8226805325700622,0.6879290668862432,0.5683617333205396,0.3934437094963169,0.3283228899705856,0.6272509143048872 +DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8160244295423997,0.7079263006757146,0.5860858700812804,0.4324321829954757,0.36991941054601357,0.6575957595631801 From 48a5e1de032fedae4862310e1d34043fcac3ff13 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 6 Nov 2023 19:30:26 -0500 Subject: [PATCH 03/28] style: Update ChemGPT model name with "CDM" #52 --- ...n => CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E.json} | 0 results/model_media/model_stats.csv | 2 +- results/model_media/model_stats_val.csv | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) rename results/model_media/davis/train_log/{DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF.json => CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E.json} (100%) diff --git a/results/model_media/davis/train_log/DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF.json b/results/model_media/davis/train_log/CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E.json similarity index 100% rename from results/model_media/davis/train_log/DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF.json rename to results/model_media/davis/train_log/CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E.json diff --git a/results/model_media/model_stats.csv b/results/model_media/model_stats.csv index 2a295356..cf754a52 100644 --- a/results/model_media/model_stats.csv +++ b/results/model_media/model_stats.csv @@ -31,7 +31,7 @@ DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8212757240173636,0.6902984939 DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.826504184715153,0.6936343607518232,0.6220887747340503,0.4539645759565525,0.3707979739652987,0.6737689336534837 DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8404119087102697,0.724508857426608,0.6426491268424466,0.419492275491693,0.3439134949420069,0.6476822334229132 DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8234262476976882,0.6961441165080728,0.614144572586071,0.46013070593880473,0.38740075712053473,0.6783293491651416 -DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF,0.7370126629894659,0.4531619146470319,0.4447325194344097,0.6708082424596539,0.5559361077674945,0.8190288410426423 +CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.7370126629894659,0.4531619146470319,0.4447325194344097,0.6708082424596539,0.5559361077674945,0.8190288410426423 DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8262970114209492,0.6902457009938884,0.6196677619083105,0.4657317363498777,0.3800800188713128,0.6824454090620566 DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8387058574888743,0.7253796342930805,0.6397184730308061,0.418395340180838,0.3501044126973091,0.646834863145794 DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8496612461728558,0.7437110771416391,0.6565426331150037,0.391104889452298,0.3245244547831619,0.6253837937237405 diff --git a/results/model_media/model_stats_val.csv b/results/model_media/model_stats_val.csv index 5c79f6c2..49d7b13a 100644 --- a/results/model_media/model_stats_val.csv +++ b/results/model_media/model_stats_val.csv @@ -9,7 +9,7 @@ DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.809188899866386,0.72725634302 DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8256781625398019,0.6981605259116868,0.5810792177230069,0.3963907833649285,0.3506354182128197,0.6295957301037933 DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8226805325700622,0.6879290668862432,0.5683617333205396,0.3934437094963169,0.3283228899705856,0.6272509143048872 DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8160244295423997,0.7079263006757146,0.5860858700812804,0.4324321829954757,0.36991941054601357,0.6575957595631801 -DGM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E_chemGPTLF,0.7380363162506938,0.4574955465284291,0.4466001977109979,0.6649520759195586,0.551816601340746,0.8154459368465568 +CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.7380363162506938,0.4574955465284291,0.4466001977109979,0.6649520759195586,0.551816601340746,0.8154459368465568 DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.823990429488312,0.7117146794067782,0.600087030044339,0.4287954343848595,0.3553209463269589,0.654824735623861 DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.827734580656503,0.702739642357159,0.5768866122321109,0.3738840686242872,0.3239032915296037,0.6114606026755013 DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8292712666904243,0.7228505402132716,0.5833504792222683,0.3717008538318646,0.3222885614393665,0.6096727432253016 From 7a6f7d47972d9673d4776cc6e285d1eb7974b599 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Tue, 7 Nov 2023 15:31:50 -0500 Subject: [PATCH 04/28] fix: overlap train test issue with new fold change --- docs/requirements.txt | 3 +++ playground.py | 23 ++++++++++------------- src/utils/arg_parse.py | 4 ++-- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 34160dc3..c4ac33f4 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -27,3 +27,6 @@ requests==2.31.0 submitit==1.4.5 ProDy==2.4.1 + +# for chemgpt +selfies==1.0.4 diff --git a/playground.py b/playground.py index fc14e957..7613312b 100644 --- a/playground.py +++ b/playground.py @@ -1,19 +1,16 @@ # %% -from src.data_analysis.figures import prepare_df, fig3_edge_feat -from src.utils import config +import pandas as pd -from transformers import AutoTokenizer, AutoModel +df = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/train/XY.csv', index_col=0) +dft = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/test/XY.csv', index_col=0) +dfv = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/val/XY.csv', index_col=0) +trainp = df['prot_id'].drop_duplicates() +testp = dft['prot_id'].drop_duplicates() +valp = dfv['prot_id'].drop_duplicates() -df = prepare_df('results/model_media/model_stats.csv') +overlap_train_test = trainp[trainp.isin(testp)] +overlap_train_val = trainp[trainp.isin(valp)] +overlap_test_val = testp[testp.isin(valp)] # %% -fig3_edge_feat(df, show=True, exclude=[]) - -# %% -print('test') - -#### ChemGPT #### - -tokenizer = AutoTokenizer.from_pretrained("ncfrey/ChemGPT-4.7M") -model = AutoModel.from_pretrained("ncfrey/ChemGPT-4.7M") \ No newline at end of file diff --git a/src/utils/arg_parse.py b/src/utils/arg_parse.py index 565979d7..33a8bcc1 100644 --- a/src/utils/arg_parse.py +++ b/src/utils/arg_parse.py @@ -138,8 +138,8 @@ def add_dataset_args(parser: argparse.ArgumentParser): ) parser.add_argument('-folds', '--fold_selection', - action='store', type=int, default=None, - help='Fold selection (default: None - no fold selection)' + action='store', type=int, default=0, + help='Fold selection (default: 0 - first fold)' ) return parser From 27695a659bcfeaf9aadbf1b55cd88cf1a0924445 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Tue, 7 Nov 2023 18:39:40 -0500 Subject: [PATCH 05/28] style(DDP): Update output dir naming convention --- train_test_DDP.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/train_test_DDP.py b/train_test_DDP.py index 595889cb..1723b2e8 100644 --- a/train_test_DDP.py +++ b/train_test_DDP.py @@ -17,7 +17,7 @@ os.makedirs(os.path.dirname(args.output_dir), exist_ok=True) # Model name and dataset cannot be added since we can provide a list of them -args.output_dir += f'_{"-".join(args.model_opt)}_{"-".join(args.data_opt)}_'+\ +args.output_dir += f'_{"-".join(args.model_opt)}_{"-".join(args.data_opt)}{args.fold_selection or ""}_'+\ f'{"-".join(args.edge_opt)}_{args.learning_rate}_{args.batch_size*args.slurm_nnodes*args.slurm_ngpus}' print("out_dir:", args.output_dir) @@ -41,7 +41,7 @@ slurm_gpus_per_node=f'v100:{args.slurm_ngpus}', tasks_per_node=args.slurm_ngpus, - slurm_job_name=f'DDP-{args.model_opt[0]}_{args.data_opt[0]}_{args.slurm_ngpus*args.slurm_nnodes}v100', + slurm_job_name=f'DDP-{args.model_opt[0]}_{args.data_opt[0]}{args.fold_selection or ""}_{args.slurm_ngpus*args.slurm_nnodes}v100', slurm_partition=("gpu" if cfg.ON_H4H else None), slurm_account=('kumargroup_gpu' if cfg.ON_H4H else None), slurm_mem=args.slurm_mem, From d90ad997e10bba71e3fc7a63f47652be4fc84eb1 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Wed, 8 Nov 2023 12:44:21 -0500 Subject: [PATCH 06/28] feat(figures): add `fig4_pro_feat_violin` #46 --- playground.py | 14 ++------------ src/data_analysis/figures.py | 37 ++++++++++++++++++++++++++++++++++-- 2 files changed, 37 insertions(+), 14 deletions(-) diff --git a/playground.py b/playground.py index fc14e957..8c748c6f 100644 --- a/playground.py +++ b/playground.py @@ -1,19 +1,9 @@ # %% from src.data_analysis.figures import prepare_df, fig3_edge_feat -from src.utils import config - -from transformers import AutoTokenizer, AutoModel - df = prepare_df('results/model_media/model_stats.csv') -# %% -fig3_edge_feat(df, show=True, exclude=[]) +# %% fig 2 with pro_feat +fig3_edge_feat(df) # %% -print('test') - -#### ChemGPT #### - -tokenizer = AutoTokenizer.from_pretrained("ncfrey/ChemGPT-4.7M") -model = AutoModel.from_pretrained("ncfrey/ChemGPT-4.7M") \ No newline at end of file diff --git a/src/data_analysis/figures.py b/src/data_analysis/figures.py index 3d393b88..4ccb3c44 100644 --- a/src/data_analysis/figures.py +++ b/src/data_analysis/figures.py @@ -75,7 +75,7 @@ def fig1_pro_overlap(df, sel_col='cindex', verbose=False, show=True): # Features -> nomsa, msa, shannon, and esm def fig2_pro_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, add_labels=True): # Extract relevant data - filtered_df = df[(df['edge'] == 'binary') & (~df['overlap'])] + filtered_df = df[(df['edge'] == 'binary') & (~df['overlap']) & (df['lig_feat'].isna())] # NOTE # Initialize lists to store cindex values for each dataset type nomsa = [] @@ -156,6 +156,34 @@ def fig2_pro_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, ad # reset stylesheet back to defaults mpl.rcParams.update(mpl.rcParamsDefault) +# similar to above but only for one dataset to show significance between ESM and other features +# this will be plotted as a jitter plot with error bars +def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], + show=True, add_labels=True, add_stats=False): + # Extract relevant data + filtered_df = df[(df['edge'] == 'binary') & (~df['overlap']) & (df['lig_feat'].isna())] + + # show all with fold info + filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')] + nomsa = filtered_df[(filtered_df['feat'] == 'nomsa')][sel_col] + msa = filtered_df[(filtered_df['feat'] == 'msa')][sel_col] + shannon = filtered_df[(filtered_df['feat'] == 'shannon')][sel_col] + esm = filtered_df[(filtered_df['feat'] == 'ESM')][sel_col] + + # Might have different length for each feature + ax = sns.violinplot(data=[nomsa, msa, shannon, esm]) + ax.set_xticklabels(['nomsa', 'msa', 'shannon', 'esm']) + ax.set_ylabel(sel_col) + ax.set_xlabel('Features') + ax.set_title(f'Feature {sel_col} for {sel_dataset}') + + # %% Annotation for stats + if add_stats: + pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon')] + annotator = Annotator(ax, pairs, data=filtered_df, x='feat', y=sel_col, verbose=verbose) + annotator.configure(test='Mann-Whitney', text_format='star', loc='outside') + annotator.apply_and_annotate() + # Figure 3 - Edge type cindex difference # Edges -> binary, simple, anm, af2 def fig3_edge_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, add_labels=True): @@ -245,7 +273,7 @@ def fig3_edge_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, a plt.show() # reset stylesheet back to defaults - mpl.rcParams.update(mpl.rcParamsDefault) + mpl.rcParams.update(mpl.rcParamsDefault) def prepare_df(csv_p:str, old_csv_p='results/model_media/old_model_stats.csv') -> pd.DataFrame: df = pd.read_csv(csv_p) @@ -262,11 +290,16 @@ def prepare_df(csv_p:str, old_csv_p='results/model_media/old_model_stats.csv') - df['improved'] = df['run'].str.contains('IM_') # postfix of model name will include I if "improved" df['batch_size'] = df['run'].str.extract(r'_(\d+)B_', expand=False) + # ESM models df.loc[df['run'].str.contains('EDM') & df['run'].str.contains('nomsaF'), 'feat'] = 'ESM' df.loc[df['run'].str.contains('EDAM'), 'feat'] += '-ESM' df.loc[df['run'].str.contains('EDIM') & df['run'].str.contains('nomsaF'), 'feat'] = 'ESM' df.loc[df['run'].str.contains('EDAIM'), 'feat'] += '-ESM' + # ChemGPT models + df.loc[df['run'].str.contains('CDM'), 'lig_feat'] = 'ChemGPT' + #TODO: account for ChemGPT with additional features + df['overlap'] = df['run'].str.contains('overlap') return df From ca006e919c0665e0718ed69d07865dcfdb3f5fe3 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Wed, 8 Nov 2023 13:29:59 -0500 Subject: [PATCH 07/28] feat: foldseek stub code #53 Fix target_to_graph function for scalability in features with new foldseek feature --- src/feature_extraction/protein.py | 69 ++++++++++++++++++------------- src/utils/config.py | 2 +- 2 files changed, 41 insertions(+), 30 deletions(-) diff --git a/src/feature_extraction/protein.py b/src/feature_extraction/protein.py index d6a33d1f..8d07392b 100644 --- a/src/feature_extraction/protein.py +++ b/src/feature_extraction/protein.py @@ -6,6 +6,7 @@ import os, math import pandas as pd +from src.utils import config as cfg from src.utils.residue import ResInfo, Chain from src.feature_extraction.protein_nodes import get_pfm, target_to_feature from src.feature_extraction.protein_edges import get_target_edge @@ -14,7 +15,8 @@ ###################### Protein Feature Extraction ###################### ######################################################################## def target_to_graph(target_sequence:str, contact_map:str or np.array, - threshold=10.5, aln_file:str=None, shannon=False) -> tuple[np.array,np.array]: + threshold=10.5, pro_feat='nomsa', aln_file:str=None, + pdb_fp:str=None) -> tuple[np.array,np.array]: """ Feature extraction for protein sequence using contact map to generate edge index and node features. @@ -31,50 +33,59 @@ def target_to_graph(target_sequence:str, contact_map:str or np.array, this to be anything **above** the value (useful for when the cmap is probability based, anything above 0.5 is considered in contact), by default 10.5 + `pro_feat` : str, optional + Type of protein node feature to use, by default 'nomsa' `aln_file` : str, optional Path to alignment file for PSSM matrix, by default None - `shannon` : bool, optional - If True, shannon entropy instead of PSSM matrix is used for - protein features, by default False. + `pdb_fp` : str, optional + Path to pdb file for foldseek feature, by default None Returns ------- Tuple[np.array] tuple of (target_feature, target_edge_index) """ + assert pro_feat in cfg.PRO_FEAT_OPT, \ + f'Invalid protein feature option: {pro_feat}, must be one of {cfg.PRO_FEAT_OPT}' + edge_index, _ = get_target_edge(target_sequence, contact_map, threshold) - # getting node features - # aln_dir = 'data/' + dataset + '/aln' - if aln_file is not None: - pssm, line_count = get_pfm(aln_file, target_sequence) - else: + # Geting simple residue one hot and property features. + pro_hot, pro_property = target_to_feature(target_sequence) # shapes=Lx21 and Lx12 + + # getting node features + if pro_feat == 'nomsa': #NOTE: DGraphDTA never uses pssm due to logic error # (see: https://github.com/595693085/DGraphDTA/issues/16) # returns Lx21 matrix of amino acid distribution for each node pssm = np.zeros((len(target_sequence), len(ResInfo.amino_acids))) line_count = 1 - - if shannon: - def entropy(col): - ent = 0.0 - for base in np.where(col > 0)[0]: # all bases being used - n_i = col[base] - P_i = n_i/line_count # number of res of type i/ total res in col - ent -= P_i*(math.log(P_i,2)) # entropy calc - - # "1 -" so that the larger the value the more conserved that amino acid is. - return 1 - (ent / math.log2(21)) # divided by log2(21) which is the max entropy score for any 21 dimension vector - - pssm = np.apply_along_axis(entropy, axis=1, arr=pssm) - pssm = pssm.reshape((len(target_sequence),1)) - #TODO: *1 if max prob matches target seq node *-1 otherwise + target_feature = np.concatenate((pssm, pro_hot, pro_property), axis=1) + elif pro_feat == 'msa' or pro_feat == 'shannon': + # get pssm matrix from alignment file + pssm, line_count = get_pfm(aln_file, target_sequence) + if pro_feat == 'shannon': + def entropy(col): + ent = 0.0 + for base in np.where(col > 0)[0]: # all bases being used + n_i = col[base] + P_i = n_i/line_count # number of res of type i/ total res in col + ent -= P_i*(math.log(P_i,2)) # entropy calc + # "1 -" so that the larger the value the more conserved that amino acid is. + return 1 - (ent / math.log2(21)) # divided by log2(21) which is the max entropy score for any 21 dimension vector + + pssm = np.apply_along_axis(entropy, axis=1, arr=pssm) + pssm = pssm.reshape((len(target_sequence),1)) + else: # normal pssm + pseudocount = 0.8 + pssm = (pssm + pseudocount / 4) / (float(line_count) + pseudocount) + target_feature = np.concatenate((pssm, pro_hot, pro_property), axis=1) + elif pro_feat == 'foldseek': + # include foldseek token in the feature vector + # foldseek = get_foldseek(target_sequence, pdb_file) + target_feature = np.concatenate((pro_hot, pro_property), axis=1) else: - pseudocount = 0.8 - pssm = (pssm + pseudocount / 4) / (float(line_count) + pseudocount) - - pro_hot, pro_property = target_to_feature(target_sequence) # shapes=Lx21 and Lx12 - target_feature = np.concatenate((pssm, pro_hot, pro_property), axis=1) + raise NotImplementedError(f'Invalid protein feature option: {pro_feat}') return target_feature, edge_index diff --git a/src/utils/config.py b/src/utils/config.py index 85df2c3f..81c5dbd5 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -10,7 +10,7 @@ STRUCT_EDGE_OPT = ['anm', 'af2', 'af2-anm'] # edge options that require structural info (pdbs) EDGE_OPT = ['simple', 'binary'] + STRUCT_EDGE_OPT -PRO_FEAT_OPT = ['nomsa', 'msa', 'shannon'] +PRO_FEAT_OPT = ['nomsa', 'msa', 'shannon', 'foldseek'] LIG_FEAT_OPT = [None, 'original'] LIG_EDGE_OPT = [None, 'binary'] From 0481f8c7ae550a91fa77d4f8e9fdfd431a54304f Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Wed, 8 Nov 2023 20:47:33 -0500 Subject: [PATCH 08/28] Add function to run Foldseek for structural sequence extraction --- src/feature_extraction/protein_nodes.py | 56 +++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/src/feature_extraction/protein_nodes.py b/src/feature_extraction/protein_nodes.py index 76a2f896..fb4d646c 100644 --- a/src/feature_extraction/protein_nodes.py +++ b/src/feature_extraction/protein_nodes.py @@ -72,6 +72,62 @@ def residue_features(residue): ResInfo.hydrophobic_ph7[residue]] return np.array(feats) +def run_foldseek(foldseek, + path, + chains: list = None, + process_id: int = 0, + plddt_path: str = None, + plddt_threshold: float = 70.) -> dict: + """ + + Args: + foldseek: Binary executable file of foldseek + path: Path to pdb file + chains: Chains to be extracted from pdb file. If None, all chains will be extracted. + process_id: Process ID for temporary files. This is used for parallel processing. + plddt_path: Path to plddt file. If None, plddt will not be used. + plddt_threshold: Threshold for plddt. If plddt is lower than this value, the structure will be masked. + + Returns: + seq_dict: A dict of structural seqs. The keys are chain IDs. The values are tuples of + (seq, struc_seq, combined_seq). + """ + assert os.path.exists(foldseek), f"Foldseek not found: {foldseek}" + assert os.path.exists(path), f"Pdb file not found: {path}" + assert plddt_path is None or os.path.exists(plddt_path), f"Plddt file not found: {plddt_path}" + + tmp_save_path = f"get_struc_seq_{process_id}.tsv" + cmd = f"{foldseek} structureto3didescriptor -v 0 --threads 1 --chain-name-mode 1 {path} {tmp_save_path}" + os.system(cmd) + + seq_dict = {} + name = os.path.basename(path) + with open(tmp_save_path, "r") as r: + for i, line in enumerate(r): + desc, seq, struc_seq = line.split("\t")[:3] + + # Mask low plddt + if plddt_path is not None: + with open(plddt_path, "r") as r: + plddts = np.array(json.load(r)["confidenceScore"]) + + # Mask regions with plddt < threshold + indices = np.where(plddts < plddt_threshold)[0] + np_seq = np.array(list(struc_seq)) + np_seq[indices] = "#" + struc_seq = "".join(np_seq) + + name_chain = desc.split(" ")[0] + chain = name_chain.replace(name, "").split("_")[-1] + + if chains is None or chain in chains: + if chain not in seq_dict: + combined_seq = "".join([a + b.lower() for a, b in zip(seq, struc_seq)]) + seq_dict[chain] = (seq, struc_seq, combined_seq) + + os.remove(tmp_save_path) + os.remove(tmp_save_path + ".dbtype") + return seq_dict if __name__ == '__main__': # dir_p = '/home/jyaacoub/projects/data/msa/outputs' From daa282254a1b3b3e209674c4fdd78053fdccdc6e Mon Sep 17 00:00:00 2001 From: Jean Charle Yaacoub Date: Fri, 10 Nov 2023 06:38:39 -0800 Subject: [PATCH 09/28] results: davis edgeW cross val #46 #25 --- docs/reqbasic.txt | 31 + ...D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...omsaF_simpleE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...omsaF_simpleE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...omsaF_simpleE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...omsaF_simpleE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ ...omsaF_simpleE_64B_0.0001LR_0.4D_2000E.json | 4007 +++++++++++++++++ results/model_media/model_stats.csv | 19 +- results/model_media/model_stats_val.csv | 17 +- 18 files changed, 60169 insertions(+), 3 deletions(-) create mode 100644 docs/reqbasic.txt create mode 100644 results/model_media/davis/train_log/DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis0D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis0D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis1D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis1D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis2D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis2D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis3D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis3D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis3D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis4D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E.json create mode 100644 results/model_media/davis/train_log/DGM_davis4D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E.json diff --git a/docs/reqbasic.txt b/docs/reqbasic.txt new file mode 100644 index 00000000..77171a4c --- /dev/null +++ b/docs/reqbasic.txt @@ -0,0 +1,31 @@ +numpy +pandas +tqdm +rdkit +scipy + +# for generating figures: +matplotlib +seaborn +statannotations + +lifelines +#biopython # used for cmap + +# model building +torch +torch-geometric +transformers + +# optional: +torchsummary +tabulate +ipykernel +plotly +requests + +submitit +ProDy + +# for chemgpt +selfies diff 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-30,10 +30,25 @@ DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8293327722196834,0.7023779705 DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8212757240173636,0.6902984939419878,0.6084680827736445,0.4717995428983164,0.3579402144362287,0.6868766577037805 DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.826504184715153,0.6936343607518232,0.6220887747340503,0.4539645759565525,0.3707979739652987,0.6737689336534837 DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8404119087102697,0.724508857426608,0.6426491268424466,0.419492275491693,0.3439134949420069,0.6476822334229132 -DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8234262476976882,0.6961441165080728,0.614144572586071,0.46013070593880473,0.38740075712053473,0.6783293491651416 +DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8234262476976882,0.6961441165080728,0.614144572586071,0.4601307059388047,0.3874007571205347,0.6783293491651416 CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.7370126629894659,0.4531619146470319,0.4447325194344097,0.6708082424596539,0.5559361077674945,0.8190288410426423 DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8262970114209492,0.6902457009938884,0.6196677619083105,0.4657317363498777,0.3800800188713128,0.6824454090620566 DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8387058574888743,0.7253796342930805,0.6397184730308061,0.418395340180838,0.3501044126973091,0.646834863145794 DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8496612461728558,0.7437110771416391,0.6565426331150037,0.391104889452298,0.3245244547831619,0.6253837937237405 DGM_davis1D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8312754273934078,0.6999447666182411,0.6286994684318588,0.448984799382394,0.354222957680865,0.6700632801328499 -DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8164859423418647,0.693536266777011,0.6013830893679215,0.46093123143684783,0.36639131860035223,0.6789191641402147 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+DGM_davis4D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E,0.833555604966058,0.7075712115532424,0.6106078527242438,0.416024967824604,0.3579411593083644,0.6449999750578321 +DGM_davis1D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.824104741709838,0.7084306688335041,0.5946147924132863,0.4590598491548651,0.3759706711801719,0.6775395554171469 +DGM_davis3D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8287041120499408,0.723104668821358,0.6017108142107055,0.4276229629095767,0.3786222305245666,0.6539288668575327 +DGM_davis4D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8402294262498695,0.7577017923877473,0.6206620344619563,0.3825352688666051,0.3474767485330271,0.6184943563740943 +DGM_davis3D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8402352764347354,0.742903788085218,0.622140808735402,0.40079666204968,0.3532069678508795,0.6330850354017855 +DGM_davis2D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8375435163751175,0.7425455867347595,0.6190960795979725,0.4016181179918054,0.3639643710876131,0.6337334755177491 +DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8477280132052173,0.7510742720785348,0.6353948250848559,0.388494297151684,0.3440198883638499,0.6232931069341967 +DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.81192915696136,0.6594676033603766,0.5745429757386536,0.5342586698655253,0.403905052656025,0.7309300033967174 +DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8138948190762828,0.7160223844294532,0.5785957133005388,0.4355331897122032,0.3649488178715008,0.6599493842047307 +DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8289131436553395,0.7240173262031673,0.601819217331164,0.4393346617159498,0.3699217589544043,0.6628232507357824 diff --git a/results/model_media/model_stats_val.csv b/results/model_media/model_stats_val.csv index 49d7b13a..5bcdbff1 100644 --- a/results/model_media/model_stats_val.csv +++ b/results/model_media/model_stats_val.csv @@ -8,10 +8,25 @@ DGM_davis0D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8363462603536834,0.7300972139 DGM_davis1D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.809188899866386,0.7272563430244341,0.5709758219318876,0.4267040368901719,0.3404274728645112,0.6532258697343301 DGM_davis2D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8256781625398019,0.6981605259116868,0.5810792177230069,0.3963907833649285,0.3506354182128197,0.6295957301037933 DGM_davis3D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8226805325700622,0.6879290668862432,0.5683617333205396,0.3934437094963169,0.3283228899705856,0.6272509143048872 -DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8160244295423997,0.7079263006757146,0.5860858700812804,0.4324321829954757,0.36991941054601357,0.6575957595631801 +DGM_davis4D_msaF_binaryE_64B_0.0001LR_0.4D_2000E,0.8160244295423997,0.7079263006757146,0.5860858700812804,0.4324321829954757,0.3699194105460135,0.6575957595631801 CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E,0.7380363162506938,0.4574955465284291,0.4466001977109979,0.6649520759195586,0.551816601340746,0.8154459368465568 DGM_davis4D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.823990429488312,0.7117146794067782,0.600087030044339,0.4287954343848595,0.3553209463269589,0.654824735623861 DGM_davis3D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.827734580656503,0.702739642357159,0.5768866122321109,0.3738840686242872,0.3239032915296037,0.6114606026755013 DGM_davis2D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8292712666904243,0.7228505402132716,0.5833504792222683,0.3717008538318646,0.3222885614393665,0.6096727432253016 DGM_davis1D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8248946179115073,0.7385253536342127,0.598720558202774,0.404841846456497,0.3498974830004453,0.6362718337758612 DGM_davis0D_shannonF_binaryE_64B_0.0001LR_0.4D_2000E,0.8299184455370965,0.7248253045920677,0.6116541529841135,0.4180130896587404,0.3448278840382894,0.6465393179526984 +DGM_davis0D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E,0.8117213114754098,0.6822219470302058,0.5875019528781427,0.4823782414881425,0.3874284030876908,0.6945345502479646 +DGM_davis1D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E,0.8247915665703199,0.7020887305742687,0.6095028913949306,0.4894290348785011,0.4005208385691923,0.6995920488959985 +DGM_davis3D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E,0.8069108484946393,0.6848170816047032,0.5502399810193338,0.4037963721049334,0.347557117418608,0.6354497400305813 +DGM_davis2D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E,0.8183052096252446,0.6863737867749686,0.5609325476177646,0.3778087612483997,0.347804165166967,0.6146615013553393 +DGM_davis0D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.814969570985508,0.6807272254531795,0.5823545712923985,0.4535221833222305,0.365906297422415,0.6734405566360185 +DGM_davis4D_nomsaF_simpleE_64B_0.0001LR_0.4D_2000E,0.8230528910394936,0.7039758563179465,0.5938138144512036,0.4495278647748936,0.3731706338410643,0.6704683920774294 +DGM_davis1D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8047394404762327,0.6404969726871645,0.562987195871845,0.4697447473362023,0.3722002576379215,0.6853792726193304 +DGM_davis3D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8054935661005236,0.6700353742923937,0.5508048205957337,0.4133981724451739,0.3601412983501659,0.6429604750256224 +DGM_davis4D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8233588063101802,0.7351598079916808,0.6025775511524273,0.4227668643344657,0.3545885150718543,0.6502052478521423 +DGM_davis3D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8181502069514659,0.6991622143832241,0.5723269302216606,0.3840700839692461,0.3371186999160069,0.6197338815727652 +DGM_davis2D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8219574855395659,0.704664969489181,0.5844068826605993,0.4147583475898777,0.3724476900146816,0.6440173503795358 +DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.809944687504805,0.6645005585637087,0.571659262806704,0.4373947617682149,0.3581584250693227,0.6613582703559507 +DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8199801099677777,0.6865022508360388,0.5899446103444999,0.4476447637812934,0.3709879400375827,0.6690626007940463 +DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.819908075643167,0.705268399177133,0.5810352756065946,0.4105505300436516,0.3495037155128313,0.640742171269889 +DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8257259216950036,0.7315092424551636,0.6073172544804472,0.4290040576800057,0.36209144985703134,0.6549840133010925 From e4cd5809d0796d52712142f565e06d7d665b1160 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Fri, 10 Nov 2023 10:44:51 -0500 Subject: [PATCH 10/28] feat(DDP): cluster-based configs and SLURM params --- src/utils/config.py | 20 +++++++++++++++++++- train_test_DDP.py | 11 ++++++----- 2 files changed, 25 insertions(+), 6 deletions(-) diff --git a/src/utils/config.py b/src/utils/config.py index 81c5dbd5..1abc10fb 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -29,4 +29,22 @@ # cluster based configs: import socket -ON_H4H = 'uhnh4h' in socket.getfqdn().split('.') \ No newline at end of file +DOMAIN_NAME = socket.getfqdn().split('.') +CLUSTER = DOMAIN_NAME[1] + +SLURM_CONSTRAINT = None +SLURM_PARTITION = None +SLURM_ACCOUNT = None +SLURM_GPU_NAME = 'v100' + +if 'uhnh4h' in DOMAIN_NAME: + CLUSTER = 'h4h' + SLURM_PARTITION = 'gpu' + SLURM_CONSTRAINT = 'gpu32g' + SLURM_ACCOUNT = 'kumargroup_gpu' +elif 'graham' in DOMAIN_NAME: + CLUSTER = 'graham' + SLURM_CONSTRAINT = 'cascade,v100' +elif 'cedar' in DOMAIN_NAME: + CLUSTER = 'cedar' + SLURM_GPU_NAME = 'v100l' \ No newline at end of file diff --git a/train_test_DDP.py b/train_test_DDP.py index 1723b2e8..ec11cfd6 100644 --- a/train_test_DDP.py +++ b/train_test_DDP.py @@ -38,16 +38,17 @@ timeout_min=args.slurm_time, # SBATCH "-time" param nodes=args.slurm_nnodes, - slurm_gpus_per_node=f'v100:{args.slurm_ngpus}', + slurm_gpus_per_node=f'{cfg.SLURM_GPU_NAME}:{args.slurm_ngpus}', tasks_per_node=args.slurm_ngpus, - slurm_job_name=f'DDP-{args.model_opt[0]}_{args.data_opt[0]}{args.fold_selection or ""}_{args.slurm_ngpus*args.slurm_nnodes}v100', - slurm_partition=("gpu" if cfg.ON_H4H else None), - slurm_account=('kumargroup_gpu' if cfg.ON_H4H else None), + slurm_job_name=f'DDP-{args.model_opt[0]}_{args.data_opt[0]}{args.fold_selection or ""}' + + f'_{args.slurm_ngpus*args.slurm_nnodes}{cfg.SLURM_GPU_NAME}', + slurm_partition=cfg.SLURM_PARTITION, + slurm_account=cfg.SLURM_ACCOUNT, slurm_mem=args.slurm_mem, # Might need this since ESM takes up a lot of memory - slurm_constraint=('gpu32g' if cfg.ON_H4H else 'cascade,v100'), + slurm_constraint=cfg.SLURM_CONSTRAINT, # For CC docs see: (https://docs.alliancecan.ca/wiki/Using_GPUs_with_Slurm) slurm_additional_parameters=additional_params From c7bd6a2f68debf1b9bae675e7b8dd470da17b6d2 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Fri, 10 Nov 2023 10:46:41 -0500 Subject: [PATCH 11/28] fix(foldseek): import json --- src/feature_extraction/protein_nodes.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/feature_extraction/protein_nodes.py b/src/feature_extraction/protein_nodes.py index fb4d646c..81a2e892 100644 --- a/src/feature_extraction/protein_nodes.py +++ b/src/feature_extraction/protein_nodes.py @@ -1,3 +1,4 @@ +import json from typing import Tuple import numpy as np import os From 5ad3c52a93ee8b102d4630d68b7b359989b18916 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Fri, 10 Nov 2023 12:39:09 -0500 Subject: [PATCH 12/28] feat: + `fig5_edge_feat_violin` plot #46 #25 --- playground.py | 48 +++++++++++++++---- src/data_analysis/figures.py | 90 +++++++++++++++++++++++++----------- 2 files changed, 100 insertions(+), 38 deletions(-) diff --git a/playground.py b/playground.py index 7613312b..024b3dd5 100644 --- a/playground.py +++ b/playground.py @@ -1,16 +1,44 @@ # %% -import pandas as pd +from src.data_analysis.figures import prepare_df +import seaborn as sns +from statannotations.Annotator import Annotator -df = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/train/XY.csv', index_col=0) -dft = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/test/XY.csv', index_col=0) -dfv = pd.read_csv('/cluster/home/t122995uhn/projects/data/DavisKibaDataset/davis/nomsa_binary_original_binary/val/XY.csv', index_col=0) -trainp = df['prot_id'].drop_duplicates() -testp = dft['prot_id'].drop_duplicates() -valp = dfv['prot_id'].drop_duplicates() +df = prepare_df(csv_p='/home/jyaacoub/projects/MutDTA/results/model_media/model_stats.csv') -overlap_train_test = trainp[trainp.isin(testp)] -overlap_train_val = trainp[trainp.isin(valp)] -overlap_test_val = testp[testp.isin(valp)] +# %% +# Figure 5: violin plot with error bars for Cross-validation results to show significance among edge feats +sel_dataset='davis' +verbose=False +sel_col='mse' +exclude=[] +show=True +add_labels=True +add_stats=True + +# %% +filtered_df = df[(df['feat'] == 'nomsa') & (~df['overlap']) & (df['lig_feat'].isna())] +filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')] + +filtered_df.sort_values(by=['edge'], inplace=True) + +#%% +binary = filtered_df[filtered_df['edge'] == 'binary'][sel_col] +simple = filtered_df[filtered_df['edge'] == 'simple'][sel_col] +anm = filtered_df[filtered_df['edge'] == 'anm'][sel_col] +af2 = filtered_df[filtered_df['edge'] == 'af2'][sel_col] + +# %% +ax = sns.violinplot(data=[binary, simple, anm, af2]) +ax.set_xticklabels(['binary', 'simple', 'anm', 'af2']) +ax.set_ylabel(sel_col) +ax.set_xlabel('Edge type') +ax.set_title(f'Edge type {sel_col} for {sel_dataset}') +if add_stats: + pairs = [('binary', 'simple'), ('binary', 'anm'), ('binary', 'af2'), + ('simple', 'anm'), ('simple', 'af2'), ('anm', 'af2')] + annotator = Annotator(ax, pairs, data=filtered_df, x='edge', y=sel_col, verbose=verbose) + annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', hide_non_significant=True) + annotator.apply_and_annotate() # %% diff --git a/src/data_analysis/figures.py b/src/data_analysis/figures.py index 4ccb3c44..ad9bb5fb 100644 --- a/src/data_analysis/figures.py +++ b/src/data_analysis/figures.py @@ -156,34 +156,6 @@ def fig2_pro_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, ad # reset stylesheet back to defaults mpl.rcParams.update(mpl.rcParamsDefault) -# similar to above but only for one dataset to show significance between ESM and other features -# this will be plotted as a jitter plot with error bars -def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], - show=True, add_labels=True, add_stats=False): - # Extract relevant data - filtered_df = df[(df['edge'] == 'binary') & (~df['overlap']) & (df['lig_feat'].isna())] - - # show all with fold info - filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')] - nomsa = filtered_df[(filtered_df['feat'] == 'nomsa')][sel_col] - msa = filtered_df[(filtered_df['feat'] == 'msa')][sel_col] - shannon = filtered_df[(filtered_df['feat'] == 'shannon')][sel_col] - esm = filtered_df[(filtered_df['feat'] == 'ESM')][sel_col] - - # Might have different length for each feature - ax = sns.violinplot(data=[nomsa, msa, shannon, esm]) - ax.set_xticklabels(['nomsa', 'msa', 'shannon', 'esm']) - ax.set_ylabel(sel_col) - ax.set_xlabel('Features') - ax.set_title(f'Feature {sel_col} for {sel_dataset}') - - # %% Annotation for stats - if add_stats: - pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon')] - annotator = Annotator(ax, pairs, data=filtered_df, x='feat', y=sel_col, verbose=verbose) - annotator.configure(test='Mann-Whitney', text_format='star', loc='outside') - annotator.apply_and_annotate() - # Figure 3 - Edge type cindex difference # Edges -> binary, simple, anm, af2 def fig3_edge_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, add_labels=True): @@ -275,6 +247,65 @@ def fig3_edge_feat(df, verbose=False, sel_col='cindex', exclude=[], show=True, a # reset stylesheet back to defaults mpl.rcParams.update(mpl.rcParamsDefault) +# Figure 4: violin plot with error bars for Cross-validation results to show significance among pro feats +def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], + show=True, add_labels=True, add_stats=False): + # Extract relevant data + filtered_df = df[(df['edge'] == 'binary') & (~df['overlap']) & (df['lig_feat'].isna())] + + # show all with fold info + filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')] + nomsa = filtered_df[(filtered_df['feat'] == 'nomsa')][sel_col] + msa = filtered_df[(filtered_df['feat'] == 'msa')][sel_col] + shannon = filtered_df[(filtered_df['feat'] == 'shannon')][sel_col] + esm = filtered_df[(filtered_df['feat'] == 'ESM')][sel_col] + + # Get values for each node feature + ax = sns.violinplot(data=[nomsa, msa, shannon, esm]) + ax.set_xticklabels(['nomsa', 'msa', 'shannon', 'esm']) + ax.set_ylabel(sel_col) + ax.set_xlabel('Features') + ax.set_title(f'Feature {sel_col} for {sel_dataset}') + + # Annotation for stats + if add_stats: + pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon')] + annotator = Annotator(ax, pairs, data=filtered_df, x='feat', y=sel_col, verbose=verbose) + annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', + hide_non_significant=True) + annotator.apply_and_annotate() + + +# Figure 5: violin plot with error bars for Cross-validation results to show significance among edge feats +def fig5_edge_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], + show=True, add_labels=True, add_stats=False): + filtered_df = df[(df['feat'] == 'nomsa') & (~df['overlap']) & (df['lig_feat'].isna())] + filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')] + + filtered_df.sort_values(by=['edge'], inplace=True) + + # Get values for each edge type + binary = filtered_df[filtered_df['edge'] == 'binary'][sel_col] + simple = filtered_df[filtered_df['edge'] == 'simple'][sel_col] + anm = filtered_df[filtered_df['edge'] == 'anm'][sel_col] + af2 = filtered_df[filtered_df['edge'] == 'af2'][sel_col] + + # plot violin plot with annotations + ax = sns.violinplot(data=[binary, simple, anm, af2]) + ax.set_xticklabels(['binary', 'simple', 'anm', 'af2']) + ax.set_ylabel(sel_col) + ax.set_xlabel('Edge type') + ax.set_title(f'Edge type {sel_col} for {sel_dataset}') + + if add_stats: + pairs = [('binary', 'simple'), ('binary', 'anm'), ('binary', 'af2'), + ('simple', 'anm'), ('simple', 'af2'), ('anm', 'af2')] + annotator = Annotator(ax, pairs, data=filtered_df, x='edge', y=sel_col, verbose=verbose) + annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', hide_non_significant=True) + annotator.apply_and_annotate() + + + def prepare_df(csv_p:str, old_csv_p='results/model_media/old_model_stats.csv') -> pd.DataFrame: df = pd.read_csv(csv_p) @@ -304,6 +335,9 @@ def prepare_df(csv_p:str, old_csv_p='results/model_media/old_model_stats.csv') - return df + + + if __name__ == '__main__': df = prepare_df('results/model_media/model_stats.csv') From 68174dc93c370e2d407ceed9764524e0deeb690a Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Sun, 12 Nov 2023 17:08:00 -0500 Subject: [PATCH 13/28] fix: plt.show in fig4 and fig5 --- playground.py | 36 +++--------------------------------- src/data_analysis/figures.py | 5 +++++ 2 files changed, 8 insertions(+), 33 deletions(-) diff --git a/playground.py b/playground.py index 024b3dd5..eae7a0b0 100644 --- a/playground.py +++ b/playground.py @@ -1,5 +1,5 @@ # %% -from src.data_analysis.figures import prepare_df +from src.data_analysis.figures import prepare_df, fig2_pro_feat, fig5_edge_feat_violin import seaborn as sns from statannotations.Annotator import Annotator @@ -7,38 +7,8 @@ df = prepare_df(csv_p='/home/jyaacoub/projects/MutDTA/results/model_media/model_stats.csv') # %% -# Figure 5: violin plot with error bars for Cross-validation results to show significance among edge feats -sel_dataset='davis' -verbose=False -sel_col='mse' -exclude=[] -show=True -add_labels=True -add_stats=True +fig2_pro_feat(df) # %% -filtered_df = df[(df['feat'] == 'nomsa') & (~df['overlap']) & (df['lig_feat'].isna())] -filtered_df = filtered_df[(filtered_df['data'] == sel_dataset) & (filtered_df['fold'] != '')] - -filtered_df.sort_values(by=['edge'], inplace=True) - -#%% -binary = filtered_df[filtered_df['edge'] == 'binary'][sel_col] -simple = filtered_df[filtered_df['edge'] == 'simple'][sel_col] -anm = filtered_df[filtered_df['edge'] == 'anm'][sel_col] -af2 = filtered_df[filtered_df['edge'] == 'af2'][sel_col] - -# %% -ax = sns.violinplot(data=[binary, simple, anm, af2]) -ax.set_xticklabels(['binary', 'simple', 'anm', 'af2']) -ax.set_ylabel(sel_col) -ax.set_xlabel('Edge type') -ax.set_title(f'Edge type {sel_col} for {sel_dataset}') - -if add_stats: - pairs = [('binary', 'simple'), ('binary', 'anm'), ('binary', 'af2'), - ('simple', 'anm'), ('simple', 'af2'), ('anm', 'af2')] - annotator = Annotator(ax, pairs, data=filtered_df, x='edge', y=sel_col, verbose=verbose) - annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', hide_non_significant=True) - annotator.apply_and_annotate() +fig5_edge_feat_violin(df, sel_col='pearson') # %% diff --git a/src/data_analysis/figures.py b/src/data_analysis/figures.py index ad9bb5fb..859d2ccb 100644 --- a/src/data_analysis/figures.py +++ b/src/data_analysis/figures.py @@ -275,6 +275,8 @@ def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex hide_non_significant=True) annotator.apply_and_annotate() + if show: + plt.show() # Figure 5: violin plot with error bars for Cross-validation results to show significance among edge feats def fig5_edge_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex', exclude=[], @@ -303,6 +305,9 @@ def fig5_edge_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cinde annotator = Annotator(ax, pairs, data=filtered_df, x='edge', y=sel_col, verbose=verbose) annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', hide_non_significant=True) annotator.apply_and_annotate() + + if show: + plt.show() From 4c91cd2d982a1c362270b238c7e5ab5750734498 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Sun, 12 Nov 2023 17:08:45 -0500 Subject: [PATCH 14/28] refactor: new load_datasets fn in loader cls --- src/utils/loader.py | 62 +++++++++++++++++++++++++-------------------- train_test.py | 11 +------- 2 files changed, 35 insertions(+), 38 deletions(-) diff --git a/src/utils/loader.py b/src/utils/loader.py index 439ea4f9..9d3f146b 100644 --- a/src/utils/loader.py +++ b/src/utils/loader.py @@ -149,12 +149,11 @@ def load_dataset(data:str, pro_feature:str, edge_opt:str, subset:str=None, path: @staticmethod @validate_args({'data': data_opt, 'pro_feature': pro_feature_opt, 'edge_opt': edge_opt, 'ligand_feature':cfg.LIG_FEAT_OPT, 'ligand_edge':cfg.LIG_EDGE_OPT}) - def load_DataLoaders(data:str, pro_feature:str, edge_opt:str, path:str=cfg.DATA_ROOT, - batch_train:int=64, datasets:Iterable[str]=['train', 'test', 'val'], + def load_datasets(data:str, pro_feature:str, edge_opt:str, path:str=cfg.DATA_ROOT, + datasets:Iterable[str]=['train', 'test', 'val'], training_fold:int=None, # for cross-val. None for no cross-val protein_overlap:bool=False, ligand_feature:str=None, ligand_edge:str=None): - loaders = {} # no overlap or cross-val subsets = datasets @@ -172,15 +171,37 @@ def load_DataLoaders(data:str, pro_feature:str, edge_opt:str, path:str=cfg.DATA_ if protein_overlap: subsets = [d+'-overlap' for d in subsets] - + loaded_datasets = {} for d, s in zip(datasets, subsets): dataset = Loader.load_dataset(data, pro_feature, edge_opt, subset=s, path=path, ligand_feature=ligand_feature, ligand_edge=ligand_edge) - + loaded_datasets[d] = dataset + return loaded_datasets + + @staticmethod + @validate_args({'data': data_opt, 'pro_feature': pro_feature_opt, 'edge_opt': edge_opt, + 'ligand_feature':cfg.LIG_FEAT_OPT, 'ligand_edge':cfg.LIG_EDGE_OPT}) + def load_DataLoaders(data:str, pro_feature:str, edge_opt:str, path:str=cfg.DATA_ROOT, + batch_train:int=64, datasets:Iterable[str]=['train', 'test', 'val'], + training_fold:int=None, # for cross-val. None for no cross-val + protein_overlap:bool=False, + ligand_feature:str=None, ligand_edge:str=None, + loaded_datasets=None): + # loaded_datasets is used to avoid loading the same dataset multiple times when we just want + # to create a new dataloader (e.g.: for testing with different batch size) + if loaded_datasets is None: + loaded_datasets = Loader.load_datasets(data=data, pro_feature=pro_feature, edge_opt=edge_opt, + path=path, datasets=datasets, training_fold=training_fold, + protein_overlap=protein_overlap, ligand_feature=ligand_feature, + ligand_edge=ligand_edge) + + loaders = {} + for d in loaded_datasets: bs = 1 if d == 'test' else batch_train - loader = DataLoader(dataset=dataset, batch_size=bs, + loader = DataLoader(dataset=loaded_datasets[d], + batch_size=bs, shuffle=False) loaders[d] = loader @@ -199,30 +220,15 @@ def load_distributed_DataLoaders(num_replicas:int, rank:int, seed:int, data:str, ligand_feature:str=None, ligand_edge:str=None, num_workers:int=4): - loaders = {} - # no overlap or cross-val - subsets = datasets - # training folds are identified by train1, train2, etc. - # (see model_key fn above) - if training_fold is not None: - subsets = [d+str(training_fold) for d in subsets] - try: - # making sure test set is not renamed - subsets[datasets.index('test')] = 'test' - except ValueError: - pass - - # Overlap is identified by adding '-overlap' to the subset name (after cross-val) - if protein_overlap: - subsets = [d+'-overlap' for d in subsets] + loaded_datasets = Loader.load_datasets(data=data, pro_feature=pro_feature, edge_opt=edge_opt, + path=path, datasets=datasets, training_fold=training_fold, + protein_overlap=protein_overlap, ligand_feature=ligand_feature, + ligand_edge=ligand_edge) - - for d, s in zip(datasets, subsets): - dataset = Loader.load_dataset(data, pro_feature, edge_opt, - subset=s, path=path, - ligand_feature=ligand_feature, - ligand_edge=ligand_edge) + loaders = {} + for d in loaded_datasets: + dataset = loaded_datasets[d] sampler = DistributedSampler(dataset, shuffle=True, num_replicas=num_replicas, rank=rank, seed=seed) diff --git a/train_test.py b/train_test.py index beca4ba8..7826333e 100644 --- a/train_test.py +++ b/train_test.py @@ -73,16 +73,7 @@ os.makedirs(f'{media_save_p}/train_log/', exist_ok=True) - # ==== LOAD DATA ==== - # WARNING: Deprecating use of split to ensure all models train on same dataset splits. - # dataset = Loader.load_dataset(DATA, FEATURE, EDGEW, subset='full') - # print(f'# Number of samples: {len(dataset)}') - # train_loader, val_loader, test_loader = train_val_test_split(dataset, - # train_split=TRAIN_SPLIT, val_split=VAL_SPLIT, - # shuffle_dataset=True, random_seed=args.rand_seed, - # batch_train=BATCH_SIZE, use_refined=False, - # split_by_prot=not args.protein_overlap) - + # ==== LOAD DATA ==== loaders = Loader.load_DataLoaders(data=DATA, pro_feature=FEATURE, edge_opt=EDGEW, path=cfg.DATA_ROOT, ligand_feature=ligand_feature, ligand_edge=ligand_edge, batch_train=BATCH_SIZE, From 86da7307a307847a0f99d55c5b1f3371adb47d20 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 10:15:26 -0500 Subject: [PATCH 15/28] results: fold 0 and 1 for davis-EDI --- results/model_media/model_stats.csv | 2 ++ results/model_media/model_stats_val.csv | 4 +++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/results/model_media/model_stats.csv b/results/model_media/model_stats.csv index 15a23d29..614d4064 100644 --- a/results/model_media/model_stats.csv +++ b/results/model_media/model_stats.csv @@ -52,3 +52,5 @@ DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8477280132052173,0.75107427207 DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.81192915696136,0.6594676033603766,0.5745429757386536,0.5342586698655253,0.403905052656025,0.7309300033967174 DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8138948190762828,0.7160223844294532,0.5785957133005388,0.4355331897122032,0.3649488178715008,0.6599493842047307 DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8289131436553395,0.7240173262031673,0.601819217331164,0.4393346617159498,0.3699217589544043,0.6628232507357824 +EDIM_davis0D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8421343521980615,0.7442023086590929,0.6221393745119952,0.3652477400883954,0.3289030617467355,0.6043572950568193 +EDIM_davis1D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8421023614186662,0.7223580257923967,0.6225670625924141,0.3758262699500786,0.3181417466188423,0.6130467110670105 diff --git a/results/model_media/model_stats_val.csv b/results/model_media/model_stats_val.csv index 5bcdbff1..afe77908 100644 --- a/results/model_media/model_stats_val.csv +++ b/results/model_media/model_stats_val.csv @@ -29,4 +29,6 @@ DGM_davis2D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8219574855395659,0.70466496948 DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.809944687504805,0.6645005585637087,0.571659262806704,0.4373947617682149,0.3581584250693227,0.6613582703559507 DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8199801099677777,0.6865022508360388,0.5899446103444999,0.4476447637812934,0.3709879400375827,0.6690626007940463 DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.819908075643167,0.705268399177133,0.5810352756065946,0.4105505300436516,0.3495037155128313,0.640742171269889 -DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8257259216950036,0.7315092424551636,0.6073172544804472,0.4290040576800057,0.36209144985703134,0.6549840133010925 +DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8257259216950036,0.7315092424551636,0.6073172544804472,0.4290040576800057,0.3620914498570313,0.6549840133010925 +EDIM_davis0D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8289380911435942,0.7355891133257101,0.6157672298130831,0.4139988652707585,0.3623009506861369,0.6434274359014842 +EDIM_davis1D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8526864235317578,0.7780004172307035,0.6560782026672488,0.37383884772108467,0.3317426473954145,0.6114236237839398 From 5fa65af9ec8fcf4b9261b954dd76a496c006537f Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 10:25:37 -0500 Subject: [PATCH 16/28] refactor: training.py and add train_tune function to tuning.py --- src/train_test/training.py | 79 +------------------------------------- src/utils/tuning.py | 38 ++++++++++++++++++ 2 files changed, 40 insertions(+), 77 deletions(-) create mode 100644 src/utils/tuning.py diff --git a/src/train_test/training.py b/src/train_test/training.py index 49f0861c..cd12f3f2 100644 --- a/src/train_test/training.py +++ b/src/train_test/training.py @@ -1,5 +1,3 @@ -import itertools -import gc from typing import Tuple from tqdm import tqdm @@ -10,14 +8,8 @@ from torch_geometric.loader import DataLoader from src.data_analysis.metrics import concordance_index -from src.data_processing.datasets import BaseDataset - from src.models.utils import BaseModel -from src.models.prior_work import DGraphDTA - -from src.train_test.utils import train_val_test_split, CheckpointSaver - -from src.utils.loader import Loader +from src.train_test.utils import CheckpointSaver def train(model: BaseModel, train_loader:DataLoader, val_loader:DataLoader, @@ -189,71 +181,4 @@ def test(model, test_loader, device, CRITERION=None) -> Tuple[float, np.ndarray, # Compute average test loss test_loss /= len(test_loader) - return test_loss, pred, actual - - -def train_tune(config, model:str, pro_feature:str, train_dataset:BaseDataset, val_dataset:BaseDataset): - from ray.air import session - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - model = Loader.init_model(model, pro_feature, config['edge'], config['dropout']) - model.to(device) - - train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], - shuffle=True, - num_workers=2) - val_loader = DataLoader(val_dataset, batch_size=config['batch_size'], - shuffle=True, - num_workers=2) - - saver = CheckpointSaver(model, debug=True) - for i in range(10): # 10 epochs - logs = train(model, train_loader, val_loader, device, epochs=1, - lr_0=config['lr'], silent=True, saver=saver) - val_loss = logs['val_loss'][0] - - # Send the current training result back to Tune - session.report({"val_loss": val_loss}) - - if i % 5 == 0: - # This saves the model to the trial directory - torch.save(model.state_dict(), "./model.pth") - - - -def grid_search(pdb_dataset, TRAIN_SPLIT=0.8, VAL_SPLIT=0.1, RAND_SEED=42, - epoch_opt = [5], - weight_opt = ['kiba', 'davis', 'random'], - batch_size_opt = [32, 64, 128], - lr_opt = [0.0001, 0.001, 0.01], - dropout_opt = [0.1, 0.2, 0.3]): - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - print(f'Device: {device}') - model_results = {} - for WEIGHTS, BATCH_SIZE, \ - LEARNING_RATE, DROPOUT, NUM_EPOCHS in \ - itertools.product(weight_opt, batch_size_opt, \ - lr_opt, dropout_opt, epoch_opt): - MODEL_KEY = f'{WEIGHTS}W_{BATCH_SIZE}B_{LEARNING_RATE}LR_{DROPOUT}DO_{NUM_EPOCHS}E' - print(f'\n\n{MODEL_KEY}') - - - model = DGraphDTA(dropout=DROPOUT) - model.to(device) - assert WEIGHTS in weight_opt, 'WEIGHTS must be one of: kiba, davis, random' - if WEIGHTS != 'random': - model_file_name = f'results/model_checkpoints/prior_work/DGraphDTA_{WEIGHTS}_t2.model' - model.safe_load_state_dict(torch.load(model_file_name, map_location=device)) - - train_loader, val_loader, test_loader = train_val_test_split(pdb_dataset, - train_split=TRAIN_SPLIT, val_split=VAL_SPLIT, - shuffle_dataset=True, random_seed=RAND_SEED, - batch_size=BATCH_SIZE) - logs = train(model, train_loader, val_loader, device, - epochs=NUM_EPOCHS, lr_0=LEARNING_RATE) - - loss, pred, actual = test(model, test_loader, device) - model_results[MODEL_KEY] = {'test_loss': loss, - 'train_loss': logs['train_loss'][-1], - 'val_loss': logs['val_loss'][-1]} - return model_results - + return test_loss, pred, actual \ No newline at end of file diff --git a/src/utils/tuning.py b/src/utils/tuning.py new file mode 100644 index 00000000..eeb9fcb7 --- /dev/null +++ b/src/utils/tuning.py @@ -0,0 +1,38 @@ +from ray.air import session + +import torch +from torch_geometric.loader import DataLoader + +from src.data_processing.datasets import BaseDataset +from src.train_test.utils import CheckpointSaver +from src.utils.loader import Loader +from src.train_test.training import train + + +def train_tune(config, model:str, pro_feature:str, train_dataset:BaseDataset, val_dataset:BaseDataset): + device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + model = Loader.init_model(model, pro_feature, config['edge'], config['dropout']) + model.to(device) + + train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], + shuffle=True, + num_workers=2) + val_loader = DataLoader(val_dataset, batch_size=config['batch_size'], + shuffle=True, + num_workers=2) + + saver = CheckpointSaver(model, debug=True) + for i in range(10): # 10 epochs + logs = train(model, train_loader, val_loader, device, epochs=1, + lr_0=config['lr'], silent=True, saver=saver) + val_loss = logs['val_loss'][0] + + # Send the current training result back to Tune + session.report({"val_loss": val_loss}) + + if i % 5 == 0: + # This saves the model to the trial directory + torch.save(model.state_dict(), "./model.pth") + + + From e25baf9c2564e907c359b9f6aee9ef730c2c4229 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 10:26:23 -0500 Subject: [PATCH 17/28] refactor: remove versions from requirements.txt --- docs/reqbasic.txt | 31 ------------------------ docs/requirements.txt | 43 +++++++++++++++++----------------- docs/requirements_versions.txt | 32 +++++++++++++++++++++++++ 3 files changed, 53 insertions(+), 53 deletions(-) delete mode 100644 docs/reqbasic.txt create mode 100644 docs/requirements_versions.txt diff --git a/docs/reqbasic.txt b/docs/reqbasic.txt deleted file mode 100644 index 77171a4c..00000000 --- a/docs/reqbasic.txt +++ /dev/null @@ -1,31 +0,0 @@ -numpy -pandas -tqdm -rdkit -scipy - -# for generating figures: -matplotlib -seaborn -statannotations - -lifelines -#biopython # used for cmap - -# model building -torch -torch-geometric -transformers - -# optional: -torchsummary -tabulate -ipykernel -plotly -requests - -submitit -ProDy - -# for chemgpt -selfies diff --git a/docs/requirements.txt b/docs/requirements.txt index c4ac33f4..e7b6cbf8 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,32 +1,31 @@ -numpy==1.23.5 -pandas==1.5.3 -tqdm==4.65.0 -rdkit==2023.3.1 -scipy==1.10.1 +numpy +pandas +tqdm +rdkit +scipy # for generating figures: -matplotlib==3.7.1 -seaborn==0.11.2 -statannotations==0.6.0 +matplotlib +seaborn +statannotations -lifelines==0.27.7 # used for concordance index calc -#biopython # used for cmap +lifelines # model building -torch==2.0.1 -torch-geometric==2.3.1 -transformers==4.31.0 # huggingface needed for esm +torch +torch-geometric +transformers # optional: -torchsummary==1.5.1 -tabulate==0.9.0 # for torch_geometric.nn.summary -ipykernel==6.23.1 -plotly==5.14.1 -requests==2.31.0 -#ray[tune] +torchsummary +tabulate +ipykernel +plotly +requests +ray[tune] -submitit==1.4.5 -ProDy==2.4.1 +submitit +ProDy # for chemgpt -selfies==1.0.4 +selfies diff --git a/docs/requirements_versions.txt b/docs/requirements_versions.txt new file mode 100644 index 00000000..c4ac33f4 --- /dev/null +++ b/docs/requirements_versions.txt @@ -0,0 +1,32 @@ +numpy==1.23.5 +pandas==1.5.3 +tqdm==4.65.0 +rdkit==2023.3.1 +scipy==1.10.1 + +# for generating figures: +matplotlib==3.7.1 +seaborn==0.11.2 +statannotations==0.6.0 + +lifelines==0.27.7 # used for concordance index calc +#biopython # used for cmap + +# model building +torch==2.0.1 +torch-geometric==2.3.1 +transformers==4.31.0 # huggingface needed for esm + +# optional: +torchsummary==1.5.1 +tabulate==0.9.0 # for torch_geometric.nn.summary +ipykernel==6.23.1 +plotly==5.14.1 +requests==2.31.0 +#ray[tune] + +submitit==1.4.5 +ProDy==2.4.1 + +# for chemgpt +selfies==1.0.4 From 2e2f9249ddb6fe6c889649ddf8ec35f28280e318 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 11:53:26 -0500 Subject: [PATCH 18/28] feat(datasets): feature extraction for `foldseek` --- src/data_processing/datasets.py | 22 ++++++++-- src/feature_extraction/protein.py | 27 +++++++++--- src/feature_extraction/protein_nodes.py | 57 +++++++++++++++---------- src/utils/config.py | 7 ++- src/utils/residue.py | 15 +++++-- 5 files changed, 88 insertions(+), 40 deletions(-) diff --git a/src/data_processing/datasets.py b/src/data_processing/datasets.py index 6dba0ee1..fd2b20d3 100644 --- a/src/data_processing/datasets.py +++ b/src/data_processing/datasets.py @@ -150,6 +150,10 @@ def pdb_p(self, code) -> str: """path to pdbfile for a particular protein""" raise NotImplementedError + def pddlt_p(self, code) -> str: + """path to plddt file for a particular protein""" + return None + @abc.abstractmethod def cmap_p(self, code) -> str: raise NotImplementedError @@ -332,10 +336,12 @@ def process(self): # extra_feat is Lx54 or Lx34 (if shannon=True) try: pro_cmap = np.load(self.cmap_p(code)) - extra_feat, edge_idx = target_to_graph(pro_seq, pro_cmap, - threshold=self.cmap_threshold, - aln_file=self.aln_p(code), - shannon=self.shannon) + extra_feat, edge_idx = target_to_graph(target_sequence=pro_seq, contact_map=pro_cmap, + threshold=self.cmap_threshold, pro_feat=self.feature_opt, + aln_file=self.aln_p(code), + # for foldseek feats + pdb_fp=self.pdb_p(code), + pddlt_fp=self.pddlt_p(code)) except Exception as e: raise Exception(f"error on protein graph creation for code {code}") from e @@ -598,6 +604,14 @@ def pdb_p(self, code, safe=True): assert not safe or len(file) == 1, f'Incorrect pdb pathing, {len(file)}# of structures for {code}.' return file[0] if len(file) >= 1 else None + def pddlt_p(self, code, safe=True): + # this contains confidence scores for each predicted residue position in the protein + pdb_p = self.pdb_p(code, safe=safe) + if pdb_p is None: return None + # from O00141_unrelaxed_rank_001_alphafold2_ptm_model_1_seed_000.pdb + # to O00141_scores_rank_001_alphafold2_ptm_model_1_seed_000.json + return pdb_p.replace('unrelaxed', 'scores').replace('.pdb', '.json') + def cmap_p(self, code): return os.path.join(self.data_root, 'pconsc4', f'{code}.npy') diff --git a/src/feature_extraction/protein.py b/src/feature_extraction/protein.py index 8d07392b..f2056da8 100644 --- a/src/feature_extraction/protein.py +++ b/src/feature_extraction/protein.py @@ -1,14 +1,13 @@ from multiprocessing import Pool from typing import Callable, Iterable import numpy as np -import matplotlib.pyplot as plt from tqdm import tqdm import os, math import pandas as pd from src.utils import config as cfg from src.utils.residue import ResInfo, Chain -from src.feature_extraction.protein_nodes import get_pfm, target_to_feature +from src.feature_extraction.protein_nodes import get_pfm, target_to_feature, get_foldseek_onehot, run_foldseek from src.feature_extraction.protein_edges import get_target_edge ######################################################################## @@ -16,7 +15,7 @@ ######################################################################## def target_to_graph(target_sequence:str, contact_map:str or np.array, threshold=10.5, pro_feat='nomsa', aln_file:str=None, - pdb_fp:str=None) -> tuple[np.array,np.array]: + pdb_fp:str=None, pddlt_fp:str=None) -> tuple[np.array,np.array]: """ Feature extraction for protein sequence using contact map to generate edge index and node features. @@ -39,6 +38,8 @@ def target_to_graph(target_sequence:str, contact_map:str or np.array, Path to alignment file for PSSM matrix, by default None `pdb_fp` : str, optional Path to pdb file for foldseek feature, by default None + `pddlt_fp` : str, optional + Path to pddlt (confidence) file for foldseek if pdb is a predicted structure, by default None Returns ------- @@ -77,13 +78,25 @@ def entropy(col): pssm = np.apply_along_axis(entropy, axis=1, arr=pssm) pssm = pssm.reshape((len(target_sequence),1)) else: # normal pssm - pseudocount = 0.8 + pseudocount = 0.8 # pseudocount to avoid divide by 0 pssm = (pssm + pseudocount / 4) / (float(line_count) + pseudocount) target_feature = np.concatenate((pssm, pro_hot, pro_property), axis=1) elif pro_feat == 'foldseek': - # include foldseek token in the feature vector - # foldseek = get_foldseek(target_sequence, pdb_file) - target_feature = np.concatenate((pro_hot, pro_property), axis=1) + # returns {chain: [seq, struct_seq, combined_seq]} dict + seq_dict = run_foldseek(pdb_fp, plddt_fp=pddlt_fp) + + # use matching sequence from foldseek + combined_seq = None + for chain in seq_dict: + seq = seq_dict[chain][0] + if seq == target_sequence: + combined_seq = seq_dict[seq][2] + break + assert combined_seq is not None, f'Could not find matching foldseek 3Di sequence for {pdb_fp}' + + # input sequences should now include 3di tokens + pro_hot_3di = get_foldseek_onehot(combined_seq) + target_feature = np.concatenate((pro_hot, pro_hot_3di), axis=1) else: raise NotImplementedError(f'Invalid protein feature option: {pro_feat}') diff --git a/src/feature_extraction/protein_nodes.py b/src/feature_extraction/protein_nodes.py index 81a2e892..31c78d6d 100644 --- a/src/feature_extraction/protein_nodes.py +++ b/src/feature_extraction/protein_nodes.py @@ -6,6 +6,7 @@ from multiprocessing import Pool from src.utils.residue import ResInfo, one_hot +from src.utils import config as cfg def get_pfm(aln_file: str, target_seq: str=None, overwrite=False) -> Tuple[np.array, int]: """ Returns position frequency matrix of amino acids based on MSA for each node in sequence""" @@ -55,8 +56,6 @@ def target_to_feature(target_seq): pro_hot = np.zeros((len(target_seq), len(ResInfo.amino_acids))) pro_property = np.zeros((len(target_seq), 12)) for i in range(len(target_seq)): - # if 'X' in pro_seq: - # print(pro_seq) pro_hot[i,] = one_hot(target_seq[i], ResInfo.amino_acids) pro_property[i,] = residue_features(target_seq[i]) @@ -73,44 +72,58 @@ def residue_features(residue): ResInfo.hydrophobic_ph7[residue]] return np.array(feats) -def run_foldseek(foldseek, - path, - chains: list = None, - process_id: int = 0, - plddt_path: str = None, - plddt_threshold: float = 70.) -> dict: +def get_foldseek_onehot(combined_seq): + # the target sequence includes 3Di foldseek tokens alternating with the actual sequence + # so we divide by 2 to get the length of the actual sequence + fld_hot = np.zeros((len(combined_seq)//2, len(ResInfo.foldseek_tokens))) + for i in range(1, len(combined_seq), step=2): + fld_hot[i // 2,] = one_hot(combined_seq[i], ResInfo.foldseek_tokens) + return fld_hot + +def run_foldseek(pdb_fp:str, foldseek_bin:str=cfg.FOLDSEEK_BIN, + chains: list = None, + plddt_fp: str = None, + plddt_threshold: float = 70.) -> dict: """ + Adapted from https://github.com/westlake-repl/SaProt/blob/main/utils/foldseek_util.py Args: - foldseek: Binary executable file of foldseek path: Path to pdb file + foldseek: Binary executable file of foldseek chains: Chains to be extracted from pdb file. If None, all chains will be extracted. - process_id: Process ID for temporary files. This is used for parallel processing. plddt_path: Path to plddt file. If None, plddt will not be used. + Example: colabfold/kiba_af2_out/highQ/O00141_scores_rank_001_alphafold2_ptm_model_1_seed_000.json plddt_threshold: Threshold for plddt. If plddt is lower than this value, the structure will be masked. Returns: seq_dict: A dict of structural seqs. The keys are chain IDs. The values are tuples of (seq, struc_seq, combined_seq). """ - assert os.path.exists(foldseek), f"Foldseek not found: {foldseek}" - assert os.path.exists(path), f"Pdb file not found: {path}" - assert plddt_path is None or os.path.exists(plddt_path), f"Plddt file not found: {plddt_path}" + assert os.path.exists(foldseek_bin), f"Foldseek not found: {foldseek_bin}" + assert os.path.exists(pdb_fp), f"Pdb file not found: {pdb_fp}" + assert plddt_fp is None or os.path.exists(plddt_fp), f"Plddt file not found: {plddt_fp}" - tmp_save_path = f"get_struc_seq_{process_id}.tsv" - cmd = f"{foldseek} structureto3didescriptor -v 0 --threads 1 --chain-name-mode 1 {path} {tmp_save_path}" - os.system(cmd) + # save in same location as pdb file + tmp_save_path = f"{pdb_fp}.foldseek.txt" + + # run foldseek only if the output file doesn't already exist + if not os.path.exists(tmp_save_path): + cmd = f"{foldseek_bin} structureto3didescriptor -v 0 --threads 1 --chain-name-mode 1 {pdb_fp} {tmp_save_path}" + os.system(cmd) # this is a blocking call + # remove dbtype file which is created by foldseek for some reason #TODO: why? + os.remove(tmp_save_path + ".dbtype") + # extract seqs from foldseek output seq_dict = {} - name = os.path.basename(path) + name = os.path.basename(pdb_fp) with open(tmp_save_path, "r") as r: for i, line in enumerate(r): desc, seq, struc_seq = line.split("\t")[:3] # Mask low plddt - if plddt_path is not None: - with open(plddt_path, "r") as r: - plddts = np.array(json.load(r)["confidenceScore"]) + if plddt_fp is not None: + with open(plddt_fp, "r") as r: + plddts = np.array(json.load(r)["plddt"]) # NOTE: updated from "confidenceScore" # Mask regions with plddt < threshold indices = np.where(plddts < plddt_threshold)[0] @@ -125,9 +138,7 @@ def run_foldseek(foldseek, if chain not in seq_dict: combined_seq = "".join([a + b.lower() for a, b in zip(seq, struc_seq)]) seq_dict[chain] = (seq, struc_seq, combined_seq) - - os.remove(tmp_save_path) - os.remove(tmp_save_path + ".dbtype") + return seq_dict if __name__ == '__main__': diff --git a/src/utils/config.py b/src/utils/config.py index 1abc10fb..0854a316 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -5,7 +5,7 @@ from prody import confProDy confProDy(verbosity='none') # stop printouts from prody - +# model and data options MODEL_OPT = ['DG', 'DGI', 'ED', 'EDA', 'EDI', 'EDAI', 'EAT', 'CD', 'CED'] STRUCT_EDGE_OPT = ['anm', 'af2', 'af2-anm'] # edge options that require structural info (pdbs) @@ -47,4 +47,7 @@ SLURM_CONSTRAINT = 'cascade,v100' elif 'cedar' in DOMAIN_NAME: CLUSTER = 'cedar' - SLURM_GPU_NAME = 'v100l' \ No newline at end of file + SLURM_GPU_NAME = 'v100l' + +# bin paths +FOLDSEEK_BIN = '~/lib/foldseek/bin/foldseek' \ No newline at end of file diff --git a/src/utils/residue.py b/src/utils/residue.py index a58deceb..255e9cd2 100644 --- a/src/utils/residue.py +++ b/src/utils/residue.py @@ -6,10 +6,11 @@ def one_hot(x, allowable_set, cap=False): """Return the one-hot encoding of x as a numpy array.""" if x not in allowable_set: - if not cap: - raise Exception('input {0} not in allowable set{1}:'.format(x, allowable_set)) - else: + if cap: # last element is the catch all/unknown x = allowable_set[-1] + else: + raise Exception('input {0} not in allowable set{1}:'.format(x, allowable_set)) + return np.eye(len(allowable_set))[allowable_set.index(x)] @@ -36,7 +37,12 @@ class ResInfo(): amino_acids = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', - 'R', 'S', 'T', 'V', 'W', 'Y', 'X'] + 'R', 'S', 'T', 'V', 'W', 'Y', 'X'] # X is unknown + + foldseek_tokens = ['a', 'c', 'd', 'e', 'f', 'g', 'h', + 'i', 'k', 'l', 'm', 'n', 'p', 'q', + 'r', 's', 't', 'v', 'w', 'y', '#'] # '#' is mask/unknown + res_to_i = {k: i for i, k in enumerate(amino_acids)} @@ -103,6 +109,7 @@ def normalize_dict(dictionary): # TODO: why not this instead? pl = normalize_add_x(pl) hydrophobic_ph2 = normalize_add_x(hydrophobic_ph2) hydrophobic_ph7 = normalize_add_x(hydrophobic_ph7) + from collections import OrderedDict From 88aa0292827048d0ed40b38ad3f11594410159fa Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 12:20:48 -0500 Subject: [PATCH 19/28] fix(datasets): update sequence to include 3Di --- src/data_processing/datasets.py | 17 ++++++++++------- src/feature_extraction/protein.py | 7 +++++-- 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/src/data_processing/datasets.py b/src/data_processing/datasets.py index fd2b20d3..ab4f3026 100644 --- a/src/data_processing/datasets.py +++ b/src/data_processing/datasets.py @@ -336,12 +336,15 @@ def process(self): # extra_feat is Lx54 or Lx34 (if shannon=True) try: pro_cmap = np.load(self.cmap_p(code)) - extra_feat, edge_idx = target_to_graph(target_sequence=pro_seq, contact_map=pro_cmap, - threshold=self.cmap_threshold, pro_feat=self.feature_opt, - aln_file=self.aln_p(code), - # for foldseek feats - pdb_fp=self.pdb_p(code), - pddlt_fp=self.pddlt_p(code)) + # updated_seq is for updated foldseek 3di combined seq + updated_seq, extra_feat, edge_idx = target_to_graph(target_sequence=pro_seq, + contact_map=pro_cmap, + threshold=self.cmap_threshold, + pro_feat=self.feature_opt, + aln_file=self.aln_p(code), + # for foldseek feats + pdb_fp=self.pdb_p(code), + pddlt_fp=self.pddlt_p(code)) except Exception as e: raise Exception(f"error on protein graph creation for code {code}") from e @@ -370,7 +373,7 @@ def process(self): pro = torchg.data.Data(x=torch.Tensor(pro_feat), edge_index=torch.LongTensor(edge_idx), - pro_seq=pro_seq, # protein sequence for downstream esm model + pro_seq=updated_seq, # Protein sequence for downstream esm model prot_id=prot_id, edge_weight=pro_edge_weight) processed_prots[prot_id] = pro diff --git a/src/feature_extraction/protein.py b/src/feature_extraction/protein.py index f2056da8..275ba373 100644 --- a/src/feature_extraction/protein.py +++ b/src/feature_extraction/protein.py @@ -44,7 +44,7 @@ def target_to_graph(target_sequence:str, contact_map:str or np.array, Returns ------- Tuple[np.array] - tuple of (target_feature, target_edge_index) + tuple of (target_sequence, target_feature, target_edge_index) """ assert pro_feat in cfg.PRO_FEAT_OPT, \ f'Invalid protein feature option: {pro_feat}, must be one of {cfg.PRO_FEAT_OPT}' @@ -97,10 +97,13 @@ def entropy(col): # input sequences should now include 3di tokens pro_hot_3di = get_foldseek_onehot(combined_seq) target_feature = np.concatenate((pro_hot, pro_hot_3di), axis=1) + + # updating target sequence to include 3di tokens + target_sequence = combined_seq else: raise NotImplementedError(f'Invalid protein feature option: {pro_feat}') - return target_feature, edge_index + return target_sequence, target_feature, edge_index ###################################################################### From 0eadb2aeae5029861968ab1937b8ebb9731d7d14 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 12:29:14 -0500 Subject: [PATCH 20/28] fix: deprecate self.shannon attribute --- src/data_processing/datasets.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/src/data_processing/datasets.py b/src/data_processing/datasets.py index ab4f3026..45d2eab5 100644 --- a/src/data_processing/datasets.py +++ b/src/data_processing/datasets.py @@ -100,15 +100,10 @@ def __init__(self, save_root:str, data_root:str, aln_dir:str, assert feature_opt in self.FEATURE_OPTIONS, \ f"Invalid feature_opt '{feature_opt}', choose from {self.FEATURE_OPTIONS}" - self.shannon = False self.feature_opt = feature_opt - if feature_opt == 'nomsa': - self.aln_dir = None # none treats it as np.zeros - elif feature_opt == 'msa': + self.aln_dir = None # none treats it as np.zeros + if feature_opt in ['msa', 'shannon']: self.aln_dir = aln_dir # path to sequence alignments - elif feature_opt == 'shannon': - self.aln_dir = aln_dir - self.shannon = True assert edge_opt in self.EDGE_OPTIONS, \ f"Invalid edge_opt '{edge_opt}', choose from {self.EDGE_OPTIONS}" From bfabacf435660552a924bfc11d6d5587a65e1bfa Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 12:35:01 -0500 Subject: [PATCH 21/28] fix(config): use path.home to get home dir #53 --- playground.py | 21 ++++++++------------- src/utils/config.py | 3 ++- 2 files changed, 10 insertions(+), 14 deletions(-) diff --git a/playground.py b/playground.py index eae7a0b0..5bc24243 100644 --- a/playground.py +++ b/playground.py @@ -1,14 +1,9 @@ # %% -from src.data_analysis.figures import prepare_df, fig2_pro_feat, fig5_edge_feat_violin -import seaborn as sns -from statannotations.Annotator import Annotator - - -df = prepare_df(csv_p='/home/jyaacoub/projects/MutDTA/results/model_media/model_stats.csv') - -# %% -fig2_pro_feat(df) - -# %% -fig5_edge_feat_violin(df, sel_col='pearson') -# %% +from src.data_processing.init_dataset import create_datasets + +create_datasets( + data_opt=['davis'], + feat_opt=['foldseek'], + edge_opt=['binary'] +) +# %% \ No newline at end of file diff --git a/src/utils/config.py b/src/utils/config.py index 0854a316..378a4af5 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -50,4 +50,5 @@ SLURM_GPU_NAME = 'v100l' # bin paths -FOLDSEEK_BIN = '~/lib/foldseek/bin/foldseek' \ No newline at end of file +from pathlib import Path +FOLDSEEK_BIN = f'{Path.home()}/lib/foldseek/bin/foldseek' \ No newline at end of file From 29d758672115177248edbfcc211d428263dce21a Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 12:44:16 -0500 Subject: [PATCH 22/28] fix: structural info check for DavisKibaDataset --- src/data_processing/datasets.py | 25 +++++++++++++++---------- src/utils/config.py | 4 +++- 2 files changed, 18 insertions(+), 11 deletions(-) diff --git a/src/data_processing/datasets.py b/src/data_processing/datasets.py index 45d2eab5..9ace735b 100644 --- a/src/data_processing/datasets.py +++ b/src/data_processing/datasets.py @@ -338,7 +338,7 @@ def process(self): pro_feat=self.feature_opt, aln_file=self.aln_p(code), # for foldseek feats - pdb_fp=self.pdb_p(code), + pdb_fp=self.pdb_p(code), pddlt_fp=self.pddlt_p(code)) except Exception as e: raise Exception(f"error on protein graph creation for code {code}") from e @@ -595,7 +595,8 @@ def pdb_p(self, code, safe=True): code = re.sub(r'[()]', '_', code) # davis and kiba dont have their own structures so this must be made using # af or some other method beforehand. - if self.edge_opt not in cfg.STRUCT_EDGE_OPT: return None + if (self.edge_opt not in cfg.STRUCT_EDGE_OPT) and \ + (self.feature_opt not in cfg.STRUCT_PRO_FEAT_OPT): return None file = glob(os.path.join(self.af_conf_dir, f'highQ/{code}_unrelaxed_rank_001*.pdb')) # should only be one file @@ -730,17 +731,21 @@ def pre_process(self): no_cmap = [c for c in codes if not os.path.isfile(self.cmap_p(c))] print(f'Number of codes without cmap files: {len(no_cmap)} out of {len(codes)}') - # Checking that structure and af_confs files are present if edgeW is anm or af2 + # Checking that structure and af_confs files are present if required: no_confs = [] - if self.edge_opt in cfg.STRUCT_EDGE_OPT: - no_confs = [c for c in codes if ( - (self.pdb_p(c, safe=False) is None) or # no highQ structure - (len(self.af_conf_files(c)) < 2))] # not enough af confirmations. + if self.edge_opt in cfg.STRUCT_EDGE_OPT or self.feature_opt in cfg.STRUCT_PRO_FEAT_OPT: + if self.feature_opt == 'foldseek': + # we only need HighQ structures for foldseek + no_confs = [c for c in codes if (self.pdb_p(c, safe=False) is None)] + else: + no_confs = [c for c in codes if ( + (self.pdb_p(c, safe=False) is None) or # no highQ structure + (len(self.af_conf_files(c)) < 2))] # only if not for foldseek - # WARNING: TEMPORARY FIX FOR DAVIS (TESK1 highQ structure is mismatched...) - no_confs.append('TESK1') + # WARNING: TEMPORARY FIX FOR DAVIS (TESK1 highQ structure is mismatched...) + no_confs.append('TESK1') - print(f'Number of codes missing af2 configurations: {len(no_confs)} / {len(codes)}') + print(f'Number of codes missing af2 configurations: {len(no_confs)} / {len(codes)}') invalid_codes = set(no_aln + no_cmap + no_confs) # filtering out invalid codes: diff --git a/src/utils/config.py b/src/utils/config.py index 378a4af5..a42cb257 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -10,7 +10,9 @@ STRUCT_EDGE_OPT = ['anm', 'af2', 'af2-anm'] # edge options that require structural info (pdbs) EDGE_OPT = ['simple', 'binary'] + STRUCT_EDGE_OPT -PRO_FEAT_OPT = ['nomsa', 'msa', 'shannon', 'foldseek'] + +STRUCT_PRO_FEAT_OPT = ['foldseek'] # requires structural info (pdbs) +PRO_FEAT_OPT = ['nomsa', 'msa', 'shannon'] + STRUCT_PRO_FEAT_OPT LIG_FEAT_OPT = [None, 'original'] LIG_EDGE_OPT = [None, 'binary'] From 6fc396a5e39b008d52caeacd1c99c21c12c7036c Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 12:48:42 -0500 Subject: [PATCH 23/28] fix: KeyError in protein feature extraction --- src/feature_extraction/protein.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/feature_extraction/protein.py b/src/feature_extraction/protein.py index 275ba373..3067376e 100644 --- a/src/feature_extraction/protein.py +++ b/src/feature_extraction/protein.py @@ -87,10 +87,9 @@ def entropy(col): # use matching sequence from foldseek combined_seq = None - for chain in seq_dict: - seq = seq_dict[chain][0] - if seq == target_sequence: - combined_seq = seq_dict[seq][2] + for c in seq_dict: + if seq_dict[c][0] == target_sequence: + combined_seq = seq_dict[c][2] break assert combined_seq is not None, f'Could not find matching foldseek 3Di sequence for {pdb_fp}' From 9dc24c4f81cda0e5677da98e1f9f54b15cdc48c3 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 12:52:25 -0500 Subject: [PATCH 24/28] fix(foldseek): dataset created successfully #53 --- src/feature_extraction/protein_nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/feature_extraction/protein_nodes.py b/src/feature_extraction/protein_nodes.py index 31c78d6d..eda4ae82 100644 --- a/src/feature_extraction/protein_nodes.py +++ b/src/feature_extraction/protein_nodes.py @@ -76,7 +76,7 @@ def get_foldseek_onehot(combined_seq): # the target sequence includes 3Di foldseek tokens alternating with the actual sequence # so we divide by 2 to get the length of the actual sequence fld_hot = np.zeros((len(combined_seq)//2, len(ResInfo.foldseek_tokens))) - for i in range(1, len(combined_seq), step=2): + for i in range(1, len(combined_seq), 2): fld_hot[i // 2,] = one_hot(combined_seq[i], ResInfo.foldseek_tokens) return fld_hot From e9f4be525da45d28b9ee0d843e5b3981ebbfccf2 Mon Sep 17 00:00:00 2001 From: Jean Charle Yaacoub Date: Mon, 13 Nov 2023 10:45:02 -0800 Subject: [PATCH 25/28] results: davis-EDI folds 2-4 --- results/model_media/model_stats.csv | 3 +++ results/model_media/model_stats_val.csv | 5 ++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/results/model_media/model_stats.csv b/results/model_media/model_stats.csv index 15a23d29..c6c3f78a 100644 --- a/results/model_media/model_stats.csv +++ b/results/model_media/model_stats.csv @@ -52,3 +52,6 @@ DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8477280132052173,0.75107427207 DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.81192915696136,0.6594676033603766,0.5745429757386536,0.5342586698655253,0.403905052656025,0.7309300033967174 DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8138948190762828,0.7160223844294532,0.5785957133005388,0.4355331897122032,0.3649488178715008,0.6599493842047307 DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8289131436553395,0.7240173262031673,0.601819217331164,0.4393346617159498,0.3699217589544043,0.6628232507357824 +EDIM_davis2D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8554472810106364,0.7147960047167372,0.6480432041534627,0.3808700198664558,0.3378217575814264,0.6171466761366019 +EDIM_davis3D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8432469960431263,0.7538742839566113,0.6228109253699023,0.3432475435749076,0.3027376194169844,0.5858733170019843 +EDIM_davis4D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8554137020365195,0.7430289570015053,0.6449800902544639,0.3552904408465783,0.2941160608088106,0.5960624471031356 diff --git a/results/model_media/model_stats_val.csv b/results/model_media/model_stats_val.csv index 5bcdbff1..ac26b4a5 100644 --- a/results/model_media/model_stats_val.csv +++ b/results/model_media/model_stats_val.csv @@ -29,4 +29,7 @@ DGM_davis2D_nomsaF_anmE_64B_0.0001LR_0.4D_2000E,0.8219574855395659,0.70466496948 DGM_davis1D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.809944687504805,0.6645005585637087,0.571659262806704,0.4373947617682149,0.3581584250693227,0.6613582703559507 DGM_davis0D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8199801099677777,0.6865022508360388,0.5899446103444999,0.4476447637812934,0.3709879400375827,0.6690626007940463 DGM_davis2D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.819908075643167,0.705268399177133,0.5810352756065946,0.4105505300436516,0.3495037155128313,0.640742171269889 -DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8257259216950036,0.7315092424551636,0.6073172544804472,0.4290040576800057,0.36209144985703134,0.6549840133010925 +DGM_davis4D_nomsaF_af2E_64B_0.0001LR_0.4D_2000E,0.8257259216950036,0.7315092424551636,0.6073172544804472,0.4290040576800057,0.3620914498570313,0.6549840133010925 +EDIM_davis3D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8366344407519283,0.7604833093809331,0.5981203556939766,0.3109887031416362,0.2938859237605942,0.5576636110969015 +EDIM_davis2D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8371103489279001,0.7312114712420225,0.5906166464692194,0.3223543817280097,0.3226479165694292,0.5677626103645869 +EDIM_davis4D_nomsaF_binaryE_48B_0.0001LR_0.4D_2000E,0.8333774894628959,0.759476307205861,0.6095430076677542,0.3768294961878906,0.31885009590497027,0.6138643956020666 From 70521614d60c18098ac42c759893881ae0a32778 Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 15:24:18 -0500 Subject: [PATCH 26/28] fix: added ESM to fig4 violin #46 --- playground.py | 26 +++++++++++++++++++------- src/data_analysis/figures.py | 5 +++-- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/playground.py b/playground.py index 5bc24243..014252d7 100644 --- a/playground.py +++ b/playground.py @@ -1,9 +1,21 @@ +# # %% +# from src.data_processing.init_dataset import create_datasets + +# create_datasets( +# data_opt=['davis'], +# feat_opt=['foldseek'], +# edge_opt=['binary'] +# ) +# # %% +# from src.utils.loader import Loader +# d2 = Loader.load_dataset(data='davis', pro_feature='nomsa', +# edge_opt='anm') # %% -from src.data_processing.init_dataset import create_datasets +from src.data_analysis.figures import fig4_pro_feat_violin, prepare_df + +df = prepare_df('results/model_media/model_stats.csv') -create_datasets( - data_opt=['davis'], - feat_opt=['foldseek'], - edge_opt=['binary'] -) -# %% \ No newline at end of file +#%% +fig4_pro_feat_violin(df, sel_dataset='davis', sel_col='mse', + add_stats=True, verbose=False) +# %% diff --git a/src/data_analysis/figures.py b/src/data_analysis/figures.py index 859d2ccb..3d55f25a 100644 --- a/src/data_analysis/figures.py +++ b/src/data_analysis/figures.py @@ -269,10 +269,11 @@ def fig4_pro_feat_violin(df, sel_dataset='davis', verbose=False, sel_col='cindex # Annotation for stats if add_stats: - pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon')] + pairs=[('nomsa', 'msa'), ('nomsa', 'shannon'), ('msa', 'shannon'), + ('ESM', 'nomsa'), ('ESM', 'msa'), ('ESM', 'shannon')] annotator = Annotator(ax, pairs, data=filtered_df, x='feat', y=sel_col, verbose=verbose) annotator.configure(test='Mann-Whitney', text_format='star', loc='inside', - hide_non_significant=True) + hide_non_significant=not verbose) annotator.apply_and_annotate() if show: From 5a93b8597a65e8372ed9a7f86dd56c62ef5bf0aa Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 17:34:05 -0500 Subject: [PATCH 27/28] feat(Loader): added SaProt option 'SPD' --- src/utils/config.py | 4 ++-- src/utils/loader.py | 8 ++++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/src/utils/config.py b/src/utils/config.py index a42cb257..bd92a438 100644 --- a/src/utils/config.py +++ b/src/utils/config.py @@ -6,7 +6,7 @@ confProDy(verbosity='none') # stop printouts from prody # model and data options -MODEL_OPT = ['DG', 'DGI', 'ED', 'EDA', 'EDI', 'EDAI', 'EAT', 'CD', 'CED'] +MODEL_OPT = ['DG', 'DGI', 'ED', 'EDA', 'EDI', 'EDAI', 'EAT', 'CD', 'CED', 'SPD'] STRUCT_EDGE_OPT = ['anm', 'af2', 'af2-anm'] # edge options that require structural info (pdbs) EDGE_OPT = ['simple', 'binary'] + STRUCT_EDGE_OPT @@ -53,4 +53,4 @@ # bin paths from pathlib import Path -FOLDSEEK_BIN = f'{Path.home()}/lib/foldseek/bin/foldseek' \ No newline at end of file +FOLDSEEK_BIN = f'{Path.home()}/lib/foldseek/bin/foldseek' diff --git a/src/utils/loader.py b/src/utils/loader.py index 9d3f146b..b697eb6e 100644 --- a/src/utils/loader.py +++ b/src/utils/loader.py @@ -85,6 +85,14 @@ def init_model(model:str, pro_feature:str, pro_edge:str, dropout:float, dropout=dropout, pro_feat='esm_only', edge_weight_opt=pro_edge) + elif model == 'SPD': + #SaProt + model = EsmDTA(esm_head='westlake-repl/SaProt_35M_AF2', + num_features_pro=320, + pro_emb_dim=512, # increase embedding size + dropout=dropout, + pro_feat='esm_only', + edge_weight_opt=pro_edge) elif model == 'EDAI': model = EsmDTA(esm_head='facebook/esm2_t6_8M_UR50D', num_features_pro=320 + num_feat_pro, From 2d71560d0a3c61f796dea83f93a181869441a53c Mon Sep 17 00:00:00 2001 From: jyaacoub Date: Mon, 13 Nov 2023 22:09:16 -0500 Subject: [PATCH 28/28] feat: protein stratification analysis for Davis#57 --- playground.py | 32 ++++---- src/data_analysis/stratify_protein.py | 111 ++++++++++++++++++++++++++ 2 files changed, 127 insertions(+), 16 deletions(-) create mode 100644 src/data_analysis/stratify_protein.py diff --git a/playground.py b/playground.py index 014252d7..50d7b870 100644 --- a/playground.py +++ b/playground.py @@ -1,21 +1,21 @@ -# # %% -# from src.data_processing.init_dataset import create_datasets +#%% +from src.data_analysis.stratify_protein import check_davis_names, kinbase_to_df -# create_datasets( -# data_opt=['davis'], -# feat_opt=['foldseek'], -# edge_opt=['binary'] -# ) -# # %% -# from src.utils.loader import Loader -# d2 = Loader.load_dataset(data='davis', pro_feature='nomsa', -# edge_opt='anm') +df = kinbase_to_df() +# %% +import json +prot_dict = json.load(open('/home/jyaacoub/projects/data/davis/proteins.txt', 'r')) # %% -from src.data_analysis.figures import fig4_pro_feat_violin, prepare_df +# returns a dictionary of davis protein names (keys) and a truple of the protein name, main family, and subgroup (values) +prots = check_davis_names(prot_dict, df) + +# %% plot histogram of main families and their counts +import seaborn as sns +import pandas as pd + +main_families = [v[1] for v in prots.values()] +main_families = pd.Series(main_families) +sns.histplot(main_families) -df = prepare_df('results/model_media/model_stats.csv') -#%% -fig4_pro_feat_violin(df, sel_dataset='davis', sel_col='mse', - add_stats=True, verbose=False) # %% diff --git a/src/data_analysis/stratify_protein.py b/src/data_analysis/stratify_protein.py new file mode 100644 index 00000000..4ededacb --- /dev/null +++ b/src/data_analysis/stratify_protein.py @@ -0,0 +1,111 @@ +# To resolve https://github.com/jyaacoub/MutDTA/issues/57 +# list of protein names for davis is in ../data/davis/proteins.txt +# or downloaded from https://staff.cs.utu.fi/~aatapa/data/DrugTarget/target_gene_names.txt + +from typing import Iterable +from src.utils import config as cfg +import pandas as pd +import re + +def kinbase_to_df(fasta_fp:str=f'{cfg.DATA_ROOT}/misc/Human_kinase_domain.fasta'): + """ + Converts the KinBase fasta file containing all Human Kinase Domains into a + dataframe for easy parsing. + + The Human_kinase_domain can be retrieved from: + https://web.archive.org/web/20230517032418/http://kinase.com/kinbase/FastaFiles/Human_kinase_domain.fasta + Sample of the FASTA headers: + >TTBK2_Hsap (CK1/TTBK) * + >TTBK1_Hsap (CK1/TTBK) + >TSSK4_Hsap (CAMK/TSSK) + >TSSK3_Hsap (CAMK/TSSK) + - The first couple characters before the underscore are the protein names (*e.g.: TTBK2). + - The characters after the underscore are the species (*e.g.: Hsap == homo sapiens). + - Most importantly, the characters between the parenthesis are the protein family and + subgroups in that order (*e.g.: CK1/TTBK). + + Parameters + ---------- + `fasta_fp` : str, optional + The path to the downloaded fasta file path, by default f'{cfg.DATA_ROOT}/misc/Human_kinase_domain.fasta' + """ + prots = {} + with open(fasta_fp, 'r') as f: + lines = f.readlines() + for i in range(len(lines)): + line = lines[i] + if line[0] == '>': # header + seq = lines[i+1] + name = re.search(r'^>(.+?)_Hsap', line).group(1) + # all in the fasta has a protein family discriptor with at least 2 elements + protein_family = re.search(r'\((.*)\)', line).group(1) + main_family, subgroup = protein_family.split('/')[:2] + + prots[name] = (main_family, subgroup, seq) + + i+=2 + else: + i+=1 + # convert to dataframe + df = pd.DataFrame.from_dict(prots, orient='index', columns=['main_family', 'subgroup', 'seq']) + df.index.name = 'protein_name' + return df + +def check_davis_names(davis_prots:dict, df:pd.DataFrame) -> list: + """ + Checks davis protein names against fasta file containing human kinase domains. + Returns list of proteins in davis that are also found in the fasta. + + NOTE: that for some in davis they have mutation information in brackets after + the protein name, but we only need the protein name for this function. + Example: + ABL1(F317I)p -> ABL1 + + There are also some with "-alpha?" (or "beta" etc..) where ? is a number, we + can't ignore these and must include them by appending to the protein name "a?". + + Parameters + ---------- + `davis_prots` : dict + Dictionary of davis protein names (keys) and their associated sequence (values). + `df` : pd.DataFrame + The dataframe containing the human kinase domains (see kinbase_to_df()). + """ + + df = kinbase_to_df() if df is None else df + + greek = {'alpha', 'beta', 'gamma', 'delta'} # for checking if protein name has greek letter + + found_prots = {} + for k in davis_prots.keys(): + name = k.split('(')[0] + alpha = '' + if '-' in name: + name, alpha = name.split('-') + + # removing any 'p' at the end of the name which indicates phosphorylation + if name[-1] == 'p': + name = name[:-1] + + # getting alpha info if it exists + if len(alpha) >= 1: + alpha_name, alpha_num = re.search(r'([a-z]*)(\d*)', alpha).groups() + if alpha_name in greek: + name += f'{alpha_name[0]}{alpha_num}' + + # checking if name is in the dataframe + if name in df.index: + found_prots[k] = (name, df.loc[name, 'main_family'], df.loc[name, 'subgroup']) + else: + print(f'MISSING: {k}-{name}') + + return found_prots + +if __name__ == '__main__': + import json + from src.data_analysis.stratify_protein import check_davis_names, kinbase_to_df + + prot_dict = json.load(open('/home/jyaacoub/projects/data/davis/proteins.txt', 'r')) + + df = kinbase_to_df() + prots = check_davis_names(prot_dict, df)