From 3c3d80fa36316ab3e7702f7f1e2c53d0a101f368 Mon Sep 17 00:00:00 2001 From: nnguyen19 Date: Fri, 19 Apr 2024 13:43:09 -0500 Subject: [PATCH] add inference scripts --- README.md | 111 +++-- scripts/README.md | 6 + {test => scripts}/__init__.py | 0 .../__pycache__/__init__.cpython-39.pyc | Bin .../__pycache__/predict.cpython-39.pyc | Bin .../predict_batch_seq.cpython-39.pyc | Bin .../predict_batch_seq_msk_inp.py | 131 ++++-- scripts/predict_batch_strc_msk_inp.py | 340 ++++++++++++++ scripts/test_pdb.smiles | 207 +++++++++ scripts/test_sequences_from_pdb.fasta | 414 ++++++++++++++++++ test/README.md | 4 - test/predict.py | 205 --------- test/predict_batch.py | 220 ---------- test/test_cases.fasta | 8 - test/test_predict_batch.sh | 24 - 15 files changed, 1147 insertions(+), 523 deletions(-) create mode 100644 scripts/README.md rename {test => scripts}/__init__.py (100%) rename {test => scripts}/__pycache__/__init__.cpython-39.pyc (100%) rename {test => scripts}/__pycache__/predict.cpython-39.pyc (100%) rename {test => scripts}/__pycache__/predict_batch_seq.cpython-39.pyc (100%) rename test/predict_batch_seq.py => scripts/predict_batch_seq_msk_inp.py (67%) create mode 100644 scripts/predict_batch_strc_msk_inp.py create mode 100644 scripts/test_pdb.smiles create mode 100644 scripts/test_sequences_from_pdb.fasta delete mode 100644 test/README.md delete mode 100644 test/predict.py delete mode 100644 test/predict_batch.py delete mode 100644 test/test_cases.fasta delete mode 100644 test/test_predict_batch.sh diff --git a/README.md b/README.md index f33b306..f19af94 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ conda activate ProteinReDiff Download model parameters: ```bash -gdown --fuzzy --folder https://drive.google.com/drive/u/1/folders/1AAJ4P5EmQtwle9_eSeNMcF-KMWObksxZ +gdown --fuzzy --folder https://drive.google.com/drive/folders/1rPlzMUPgKLFd_Krk8cGqhEeitWByPOMn?usp=sharing ``` Additionally, TMalign is required to align generated structures. @@ -47,50 +47,70 @@ export PATH="/path/to/TMalign:$PATH" ``` ## Sample generation -Generate complex structures with the protein structure-free model (ProteinReDiff): + +Generate single complex structure using ProteinReDiff: + ```bash python generate.py \ - --ckpt_path "checkpoints/ProteinReDiff_v1.ckpt" \ - --output_dir "workdir/generate/example_ProteinReDiff" \ + --ckpt_path "checkpoints/PRD_ver1.ckpt"\ + --output_dir "workdir/inference/example_ProteinReDiff" \ --protein "LSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \ --ligand "Cc1ccc2c(c1c3cc(cc4c3nc([nH]4)C5CC5)c6c(noc6C)C)cccn2" \ - --num_samples 8 + --num_samples 3 \ + --num_steps 1000 ``` -Alternatively, the protein structure-dependent model (ProteinReDiff+S) can be used: +The sequence can be masked prior to input using `X` token (`mask_prob` is the fraction of input protein residues to be masked from 0.0 to 1.0): ```bash -wget https://files.rcsb.org/download/6MOA.pdb python generate.py \ - --ckpt_path "checkpoints/ProteinReDiffS_v1.ckpt" \ - --output_dir "workdir/generate/example_ProteinReDiffS" \ - --protein "6MOA.pdb" \ + --ckpt_path "checkpoints/PRD_ver1.ckpt"\ + --output_dir "workdir/inference/example_ProteinReDiff" \ + --protein "LSEQXXXXNGILKELLSKXXXXYAWPFYKPVDASALGLHDYHDIIKXXXXLSTVKRKMENRDYRDXXXXAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \ --ligand "Cc1ccc2c(c1c3cc(cc4c3nc([nH]4)C5CC5)c6c(noc6C)C)cccn2" \ - --num_samples 8 + --num_samples 4 \ + --num_steps 1000 \ + --mask_prob 0.0 ``` -Note that an input protein structure must be given as a PDB file in this case. -Besides, you can specify a reference protein structure to be used for the alignment of results: +Generate structure ensembles without ligand (use the dummy token `*`): ```bash python generate.py \ - --ckpt_path "checkpoints/ProteinReDiff_v1.ckpt" \ - --output_dir "workdir/generate/example_ProteinReDiff_ref" \ + --ckpt_path "checkpoints/PRD_ver1.ckpt"\ + --output_dir "workdir/inference/example_ProteinReDiff" \ --protein "LSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \ - --ligand "Cc1ccc2c(c1c3cc(cc4c3nc([nH]4)C5CC5)c6c(noc6C)C)cccn2" \ - --num_samples 8 \ - --ref_path "6MOA.pdb" + --ligand "*" \ + --num_samples 3 \ + --num_steps 1000 ``` -This is used only for alignment and does not affect the generation process itself. -The argument num_steps can be modified from the default of 64 to reduce execution time: +Generate multiple complex structures: ```bash -python generate.py \ - --ckpt_path "checkpoints/ProteinReDiff_v1.ckpt" \ - --output_dir "workdir/generate/example_ProteinReDiff_fast" \ - --protein "LSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \ - --ligand "Cc1ccc2c(c1c3cc(cc4c3nc([nH]4)C5CC5)c6c(noc6C)C)cccn2" \ - --num_samples 8 \ - --ref_path "6MOA.pdb" \ - --num_steps 24 +python -m scripts.predict_batch_strc_msk_inp \ + --ckpt_path "checkpoints/PRD_ver1.ckpt" \ + --output_dir "workdir/inference/example_ProteinReDiff" \ + --fasta "./scripts/test_sequences_from_pdb.fasta" \ + --ligand_file './scripts/scripts.smiles' \ + --accelerator "gpu"\ + --num_gpus 1 \ + --batch_size 1 \ + --num_samples 1 \ + --mask_prob 0.5 \ + --num_steps 1000 + ``` + +Alternatively, generate multiple samples per sequences only (`mask_prob` can be adjusted to increase diversity, best sequences are masked below 0.5): +```bash +python -m scripts.predict_batch_seq_msk_inp \ + --ckpt_path "checkpoints/PRD_ver1.ckpt" \ + --output_dir "workdir/inference/example_ProteinReDiff" \ + --fasta "./scripts/test_sequences_from_pdb.fasta" \ + --ligand_file './scripts/test_pdb.smiles' \ + --accelerator "gpu"\ + --num_gpus 1 \ + --batch_size 1 \ + --num_samples 10 \ + --mask_prob 0.3 \ + --num_steps 1000 ``` ## Training @@ -110,7 +130,6 @@ python train.py \ --num_workers 8 \ --batch_size 1 \ --accumulate_grad_batches 8 \ - --no_cb_distogram \ --save_dir "workdir/train/example_ProteinReDiff" \ --single_dim 256 \ --pair_dim 32 \ @@ -121,13 +140,37 @@ where the no_cb_distogram argument makes the model protein structure-free. Please modify the batch_size and accumulate_grad_batches arguments according to your machine(s). Default values can be used to reproduce the settings used in our paper: -``` + +```bash python train.py \ - --num_workers 8 \ - --batch_size 3 \ + --training_mode \ + --num_gpus 1\ + --num_workers 30 \ + --batch_size 2 \ + --accumulate_grad_batches 10 \ + --save_dir "workdir/train/example_ProteinReDiff" \ + --single_dim 512 \ + --mask_prob 0.15 \ + --pair_dim 64 \ + --num_steps 2000 \ + --num_blocks 4 +``` +Due to the limitation of runtime on GPUs, we prepared a `train_from_ckpt.py` script to further train on finished epoch: + +```bash +python train_from_ckpt.py \ + --training_mode \ + --num_gpus 1\ + --num_workers 30 \ + --batch_size 2 \ --accumulate_grad_batches 8 \ - --no_cb_distogram \ - --save_dir "workdir/train/reproduce_ProteinReDiff" + --save_dir "$save_dir" \ + --single_dim 512 \ + --mask_prob 0.15 \ + --pair_dim 64 \ + --num_steps 1000 \ + --num_blocks 4 \ + --trained_ckpt "checkpoints/PRD_ver1.ckpt" ``` ## Acknowledgements diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 0000000..ee4f30a --- /dev/null +++ b/scripts/README.md @@ -0,0 +1,6 @@ +### Inference Scripts: + +- predict_batch_strc_msk_inp.py: predict batch of complex structures. +- predict_batch_seq_msk_inp.py: predict batch of sequences only without TM alignment comparison. + + \ No newline at end of file diff --git a/test/__init__.py b/scripts/__init__.py similarity index 100% rename from test/__init__.py rename to scripts/__init__.py diff --git a/test/__pycache__/__init__.cpython-39.pyc b/scripts/__pycache__/__init__.cpython-39.pyc similarity index 100% rename from test/__pycache__/__init__.cpython-39.pyc rename to scripts/__pycache__/__init__.cpython-39.pyc diff --git a/test/__pycache__/predict.cpython-39.pyc b/scripts/__pycache__/predict.cpython-39.pyc similarity index 100% rename from test/__pycache__/predict.cpython-39.pyc rename to scripts/__pycache__/predict.cpython-39.pyc diff --git a/test/__pycache__/predict_batch_seq.cpython-39.pyc b/scripts/__pycache__/predict_batch_seq.cpython-39.pyc similarity index 100% rename from test/__pycache__/predict_batch_seq.cpython-39.pyc rename to scripts/__pycache__/predict_batch_seq.cpython-39.pyc diff --git a/test/predict_batch_seq.py b/scripts/predict_batch_seq_msk_inp.py similarity index 67% rename from test/predict_batch_seq.py rename to scripts/predict_batch_seq_msk_inp.py index ba25cad..3e58783 100644 --- a/test/predict_batch_seq.py +++ b/scripts/predict_batch_seq_msk_inp.py @@ -5,9 +5,10 @@ from argparse import ArgumentParser from operator import itemgetter from pathlib import Path -from typing import Iterable, List, Union, Tuple +from typing import Iterable, List, Union, Tuple, Any import numpy as np +import random import pytorch_lightning as pl import torch from rdkit import Chem @@ -25,14 +26,50 @@ proteins_to_pdb_file, ) from dpl.tmalign import run_tmalign + +torch.multiprocessing.set_start_method('fork') RESIDUE_TYPES_MASK = RESIDUE_TYPES + [""] -def compute_residue_esm(protein: Protein) -> torch.Tensor: - esm_model, esm_alphabet = torch.hub.load( - "facebookresearch/esm:main", "esm2_t33_650M_UR50D" - ) - esm_model.cuda().eval() - esm_batch_converter = esm_alphabet.get_batch_converter() + + + +esm_model = None +esm_batch_converter = None + +def load_esm_model(accelerator): + global esm_model, esm_batch_converter + if esm_model is None or esm_batch_converter is None: + esm_model, esm_alphabet = torch.hub.load( + "facebookresearch/esm:main", "esm2_t33_650M_UR50D" + ) + + # esm_model.cuda().eval() + if accelerator == "gpu": + esm_model.cuda().eval() + else: + esm_model.eval() + esm_batch_converter = esm_alphabet.get_batch_converter() + +# esm_model, esm_alphabet = torch.hub.load( +# "facebookresearch/esm:main", "esm2_t33_650M_UR50D" +# ) +# esm_model.cuda().eval() +# esm_batch_converter = esm_alphabet.get_batch_converter() + +def compute_residue_esm(protein: Protein, accelerator: str) -> torch.Tensor: + # esm_model, esm_alphabet = torch.hub.load( + # "facebookresearch/esm:main", "esm2_t33_650M_UR50D" + # ) + # esm_model.cuda().eval() + # esm_batch_converter = esm_alphabet.get_batch_converter() + global esm_model, esm_batch_converter + # if esm_model is None or esm_batch_converter is None: + # esm_model, esm_alphabet = torch.hub.load( + # "facebookresearch/esm:main", "esm2_t33_650M_UR50D" + # ) + # esm_model.cuda().eval() + # esm_batch_converter = esm_alphabet.get_batch_converter() + load_esm_model(accelerator) data = [] for chain, _ in itertools.groupby(protein.chain_index): @@ -40,7 +77,11 @@ def compute_residue_esm(protein: Protein) -> torch.Tensor: [RESIDUE_TYPES_MASK[aa] for aa in protein.aatype[protein.chain_index == chain]] ) data.append(("", sequence)) - batch_tokens = esm_batch_converter(data)[2].cuda() + # batch_tokens = esm_batch_converter(data)[2].cuda() + if accelerator == "gpu": + batch_tokens = esm_batch_converter(data)[2].cuda() + else: + batch_tokens = esm_batch_converter(data)[2] with torch.inference_mode(): results = esm_model(batch_tokens, repr_layers=[esm_model.num_layers]) token_representations = results["representations"][esm_model.num_layers].cpu() @@ -66,6 +107,24 @@ def proteins_from_fasta(fasta_file: Union[str, Path]): return proteins, names +def proteins_from_fasta_with_mask(fasta_file: Union[str, Path], mask_percent: float = 0.0): + names = [] + proteins = [] + sequences = [] + with open(fasta_file, "r") as f: + for line in f: + if line.startswith(">"): + name = line.lstrip(">").rstrip("\n").replace(" ","_") + names.append(name) + elif not line in ['\n', '\r\n']: + sequence = line.rstrip("\n") + sequence = mask_sequence_by_percent(sequence, mask_percent) + protein = protein_from_sequence(sequence) + proteins.append(protein) + sequences.append(sequence) + + return proteins, names, sequences + def parse_ligands(ligand_input: Union[str, Path, list]): ligands = [] if isinstance(ligand_input, list): @@ -110,9 +169,17 @@ def update_seq( protein = dataclasses.replace(protein, aatype = aatype) return protein +def mask_sequence_by_percent(seq, percentage=0.2): + aa_to_replace = random.sample(range(len(seq)), int(len(seq)*percentage)) + + output_aa = [char if idx not in aa_to_replace else 'X' for idx, char in enumerate(seq)] + masked_seq = ''.join(output_aa) + + return masked_seq def main(args): - pl.seed_everything(args.seed, workers=True) + pl.seed_everything(np.random.randint(999999999), workers=True) + # Check if the directory exists if os.path.exists(args.output_dir): # Remove the existing directory @@ -123,17 +190,20 @@ def main(args): model = ProteinReDiffModel.load_from_checkpoint( args.ckpt_path, num_steps=args.num_steps ) - ## (NN) model.training_mode = False - args.num_gpus = 1 model.mask_prob = args.mask_prob ## (NN) # Inputs - proteins, names = proteins_from_fasta(args.fasta) - + proteins, names, masked_sequences = proteins_from_fasta_with_mask(args.fasta, args.mask_prob) + + with open(args.output_dir / "masked_sequences.fasta", "w") as f: + for i, (name, seq) in enumerate(zip(names, masked_sequences)): + f.write(">{}_sample_{}\n".format(name,i%args.num_samples)) + f.write("{}\n".format(seq)) + if args.ligand_file is None: - ligand_input = ["*"]*args.num_samples*len(names) + ligand_input = ["*"]*len(names) ligands = parse_ligands(ligand_input) else: @@ -145,23 +215,24 @@ def main(args): # warnings.warn("Too many atoms (> 400). May take a long time for sample generation.") datas = [] - for protein, ligand in zip(proteins, ligands): + for name, protein, ligand in zip(names,proteins, ligands): data = { **ligand_to_data(ligand), - **protein_to_data(protein, residue_esm=compute_residue_esm(protein)), + **protein_to_data(protein, residue_esm=compute_residue_esm(protein, args.accelerator)), } datas.extend([data]*args.num_samples) - # Generate samples - trainer = pl.Trainer.from_argparse_args( - args, - accelerator="auto", - gpus = args.num_gpus, - default_root_dir=args.output_dir, - max_epochs=-1, - ) + + trainer = pl.Trainer( + accelerator=args.accelerator, + devices=args.num_gpus, + default_root_dir=args.output_dir, + max_epochs=-1, + strategy='ddp' + + ) results = trainer.predict( model, dataloaders=DataLoader( @@ -169,11 +240,13 @@ def main(args): batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_fn, - ), + ) ) - torch.save(results,"results.pt") - positions = [p[0] for p in results] ## (NN) + + + #torch.save(results,"results.pt") + # positions = [p[0] for p in results] ## (NN) probabilities = [s[1] for s in results] ## (NN) # positions = torch.cat(positions, dim=0).detach().cpu().numpy() @@ -226,8 +299,10 @@ def main(args): if __name__ == "__main__": parser = ArgumentParser() - parser.add_argument("--seed", type=int, default=1234) + # parser.add_argument("--seed", type=int, default=1234) + parser.add_argument("--accelerator", type=str, default="gpu") parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--num_gpus", type=int, default=1) parser.add_argument("--num_workers", type=int, default=torch.get_num_threads()) parser.add_argument("--num_steps", type=int, default=64) parser.add_argument("--mask_prob", type=float, default=0.3) diff --git a/scripts/predict_batch_strc_msk_inp.py b/scripts/predict_batch_strc_msk_inp.py new file mode 100644 index 0000000..52550ec --- /dev/null +++ b/scripts/predict_batch_strc_msk_inp.py @@ -0,0 +1,340 @@ +import dataclasses +import itertools +import warnings +import os +from argparse import ArgumentParser +from operator import itemgetter +from pathlib import Path +from typing import Iterable, List, Union, Tuple, Any + +import numpy as np +import random +import pytorch_lightning as pl +import torch +from rdkit import Chem +from torch.utils.data import DataLoader + +from dpl.data import InferenceDataset, collate_fn, ligand_to_data, protein_to_data +from dpl.model import ProteinReDiffModel ## (NN) +from dpl.mol import get_mol_positions, mol_from_file, update_mol_positions +from dpl.protein import ( + RESIDUE_TYPES, + RESIDUE_TYPE_INDEX, + Protein, + protein_from_pdb_file, + protein_from_sequence, + proteins_to_pdb_file, + protein_to_pdb_file +) +from dpl.tmalign import run_tmalign + +torch.multiprocessing.set_start_method('fork') + +RESIDUE_TYPES_MASK = RESIDUE_TYPES + [""] + + + +esm_model = None +esm_batch_converter = None + +def load_esm_model(accelerator): + global esm_model, esm_batch_converter + if esm_model is None or esm_batch_converter is None: + esm_model, esm_alphabet = torch.hub.load( + "facebookresearch/esm:main", "esm2_t33_650M_UR50D" + ) + + # esm_model.cuda().eval() + if accelerator == "gpu": + esm_model.cuda().eval() + else: + esm_model.eval() + esm_batch_converter = esm_alphabet.get_batch_converter() + +# esm_model, esm_alphabet = torch.hub.load( +# "facebookresearch/esm:main", "esm2_t33_650M_UR50D" +# ) +# esm_model.cuda().eval() +# esm_batch_converter = esm_alphabet.get_batch_converter() + +def compute_residue_esm(protein: Protein, accelerator: str) -> torch.Tensor: + # esm_model, esm_alphabet = torch.hub.load( + # "facebookresearch/esm:main", "esm2_t33_650M_UR50D" + # ) + # esm_model.cuda().eval() + # esm_batch_converter = esm_alphabet.get_batch_converter() + global esm_model, esm_batch_converter + # if esm_model is None or esm_batch_converter is None: + # esm_model, esm_alphabet = torch.hub.load( + # "facebookresearch/esm:main", "esm2_t33_650M_UR50D" + # ) + # esm_model.cuda().eval() + # esm_batch_converter = esm_alphabet.get_batch_converter() + load_esm_model(accelerator) + + data = [] + for chain, _ in itertools.groupby(protein.chain_index): + sequence = "".join( + [RESIDUE_TYPES_MASK[aa] for aa in protein.aatype[protein.chain_index == chain]] + ) + data.append(("", sequence)) + # batch_tokens = esm_batch_converter(data)[2].cuda() + if accelerator == "gpu": + batch_tokens = esm_batch_converter(data)[2].cuda() + else: + batch_tokens = esm_batch_converter(data)[2] + with torch.inference_mode(): + results = esm_model(batch_tokens, repr_layers=[esm_model.num_layers]) + token_representations = results["representations"][esm_model.num_layers].cpu() + residue_representations = [] + for i, (_, sequence) in enumerate(data): + residue_representations.append(token_representations[i, 1 : len(protein.aatype) + 1]) + residue_esm = torch.cat(residue_representations, dim=0) + assert residue_esm.size(0) == len(protein.aatype) + return residue_esm + +def proteins_from_fasta(fasta_file: Union[str, Path]): + names = [] + proteins = [] + with open(fasta_file, "r") as f: + for line in f: + if line.startswith(">"): + name = line.lstrip(">").rstrip("\n").replace(" ","_") + names.append(name) + elif not line in ['\n', '\r\n']: + sequence = line.rstrip("\n") + protein = protein_from_sequence(sequence) + proteins.append(protein) + + return proteins, names + +def proteins_from_fasta_with_mask(fasta_file: Union[str, Path], mask_percent: float = 0.0): + names = [] + proteins = [] + sequences = [] + with open(fasta_file, "r") as f: + for line in f: + if line.startswith(">"): + name = line.lstrip(">").rstrip("\n").replace(" ","_") + names.append(name) + elif not line in ['\n', '\r\n']: + sequence = line.rstrip("\n") + sequence = mask_sequence_by_percent(sequence, mask_percent) + protein = protein_from_sequence(sequence) + proteins.append(protein) + sequences.append(sequence) + + return proteins, names, sequences + +def parse_ligands(ligand_input: Union[str, Path, list]): + ligands = [] + if isinstance(ligand_input, list): + for lig in ligand_input: + ligand = Chem.MolFromSmiles(lig) + ligand = update_mol_positions(ligand, np.zeros((ligand.GetNumAtoms(), 3))) + ligands.append(ligand) + else: + with open(ligand_input, "r") as f: + for line in f: + ligand = Chem.MolFromSmiles(line.rstrip("\n")) + ligand = update_mol_positions(ligand, np.zeros((ligand.GetNumAtoms(), 3))) + ligands.append(ligand) + + return ligands + +def update_pos( + protein: Protein, ligand: Chem.Mol, pos: np.ndarray +) -> Tuple[Protein, Chem.Mol]: + atom_pos = np.zeros_like(protein.atom_pos) + atom_pos[:, 1] = pos[ligand.GetNumAtoms() :] + atom_mask = np.zeros_like(protein.atom_mask) + atom_mask[:, 1] = 1.0 + protein = dataclasses.replace(protein, atom_pos=atom_pos, atom_mask=atom_mask) + ligand = update_mol_positions(ligand, pos[: ligand.GetNumAtoms()]) + return protein, ligand + +def predict_seq( + proba: torch.Tensor +) -> list : + tokens = torch.argmax(torch.softmax((torch.tensor(proba)), dim = -1), dim = -1) + RESIDUE_TYPES_NEW = ["X"] + RESIDUE_TYPES + return "".join(map(lambda i : RESIDUE_TYPES_NEW[i], tokens)).lstrip("X").rstrip("X") + +def update_seq( + protein: Protein, proba: torch.Tensor +) -> Protein: + tokens = torch.argmax(torch.softmax((torch.tensor(proba)), dim = -1), dim = -1) + RESIDUE_TYPES_NEW = ["X"] + RESIDUE_TYPES + sequence = "".join(map(lambda i : RESIDUE_TYPES_NEW[i], tokens)).lstrip("X").rstrip("X") + aatype = np.array([RESIDUE_TYPES.index(s) for s in sequence], dtype=np.int64) + protein = dataclasses.replace(protein, aatype = aatype) + return protein + +def mask_sequence_by_percent(seq, percentage=0.2): + aa_to_replace = random.sample(range(len(seq)), int(len(seq)*percentage)) + + output_aa = [char if idx not in aa_to_replace else 'X' for idx, char in enumerate(seq)] + masked_seq = ''.join(output_aa) + + return masked_seq + +def main(args): + pl.seed_everything(np.random.randint(999999999), workers=True) + + # Check if the directory exists + if os.path.exists(args.output_dir): + # Remove the existing directory + os.rmdir(args.output_dir) + args.output_dir.mkdir(parents=True) + + # Model + model = ProteinReDiffModel.load_from_checkpoint( + args.ckpt_path, num_steps=args.num_steps + ) + model.training_mode = False + model.mask_prob = args.mask_prob + ## (NN) + + # Inputs + proteins, names, masked_sequences = proteins_from_fasta_with_mask(args.fasta, args.mask_prob) + + with open(args.output_dir / "masked_sequences.fasta", "w") as f: + for i, (name, seq) in enumerate(zip(names, masked_sequences)): + f.write(">{}_sample_{}\n".format(name,i%args.num_samples)) + f.write("{}\n".format(seq)) + + if args.ligand_file is None: + ligand_input = ["*"]*len(names) + + ligands = parse_ligands(ligand_input) + else: + ligands = parse_ligands(args.ligand_file) + + # total_num_atoms = len(protein.aatype) + ligand.GetNumAtoms() + # print(f"Total number of atoms: {total_num_atoms}") + # if total_num_atoms > 400: + # warnings.warn("Too many atoms (> 400). May take a long time for sample generation.") + + datas = [] + for name, protein, ligand in zip(names,proteins, ligands): + data = { + **ligand_to_data(ligand), + **protein_to_data(protein, residue_esm=compute_residue_esm(protein, args.accelerator)), + } + datas.extend([data]*args.num_samples) + + + # Generate samples + + trainer = pl.Trainer( + accelerator=args.accelerator, + devices=args.num_gpus, + default_root_dir=args.output_dir, + max_epochs=-1, + strategy='ddp' + + ) + results = trainer.predict( + model, + dataloaders=DataLoader( + InferenceDataset(datas, args.num_samples * len(names)), + batch_size=args.batch_size, + num_workers=args.num_workers, + collate_fn=collate_fn, + ) + ) + + + + #torch.save(results,"results.pt") + positions = [p[0] for p in results] ## (NN) + probabilities = [s[1] for s in results] ## (NN) + + # positions = torch.cat([p[0] for p in results], dim=-1).detach().cpu().numpy() + # probabilities = torch.cat(probabilities, dim=0).detach().cpu().numpy() #(NN) + + + + # with open(args.output_dir / "sample_sequences.fasta", "w") as f: + # for i, (name, seq_prob) in enumerate(zip(names, probabilities)): + # f.write(">{}_sample_{}\n".format(name,i%args.num_samples)) + # sequence = predict_seq(seq_prob.squeeze()) + # f.write("{}\n".format(sequence)) + + # Save samples + # Repeat samples by num_samples + proteins, ligands, names = [protein for protein in proteins for _ in range(args.num_samples)],\ + [ligand for ligand in ligands for _ in range(args.num_samples)], \ + [name for name in names for _ in range(args.num_samples)] + + + for k, (pos, seq_prob, protein, ligand, name) in enumerate(zip(positions, probabilities, proteins, ligands, names)): + sample_protein, sample_ligand = update_pos(protein, ligand, pos.squeeze()) + sample_protein = update_seq(sample_protein, seq_prob.squeeze()) + # if ref_protein is None: + if k % args.num_samples ==0: + warnings.warn( + "Using the first sample as a reference. The resulting structures may be mirror images." + ) + + ref_protein = sample_protein + sample_proteins, sample_ligands = [], [] + tmscores = [] + + + + + + tmscore, t, R = max( + run_tmalign(sample_protein, ref_protein), + run_tmalign(sample_protein, ref_protein, mirror=True), + key=itemgetter(0), + ) + sample_proteins.append( + dataclasses.replace( + sample_protein, atom_pos=t + sample_protein.atom_pos @ R + ) + ) + sample_ligands.append( + update_mol_positions( + sample_ligand, t + get_mol_positions(sample_ligand) @ R + ) + ) + tmscores.append(tmscore) + + #save every same protein batch + if ((k +1) % (args.num_samples)) ==0: + for i, sample_protein in enumerate(sample_proteins): + protein_to_pdb_file(sample_protein, args.output_dir / "sample_protein_{}_model_{}.pdb".format(name, i)) + + + for i, sample_ligand in enumerate(sample_ligands): + with Chem.SDWriter(str(args.output_dir / "sample_ligand_{}_model_{}.sdf".format(name, i))) as w: + w.write(sample_ligand) + + with open(args.output_dir / "sample_tmscores_{}.txt".format(name), "w") as f: + for tmscore in tmscores: + f.write(str(tmscore) + "\n") + + + + +if __name__ == "__main__": + parser = ArgumentParser() + + # parser.add_argument("--seed", type=int, default=1234) + parser.add_argument("--accelerator", type=str, default="gpu") + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--num_gpus", type=int, default=1) + parser.add_argument("--num_workers", type=int, default=torch.get_num_threads()) + parser.add_argument("--num_steps", type=int, default=64) + parser.add_argument("--mask_prob", type=float, default=0.3) + parser.add_argument("--training_mode", action="store_true") + parser.add_argument("-c", "--ckpt_path", type=Path, required=True) + parser.add_argument("-o", "--output_dir", type=Path, required=True) + parser.add_argument("-p", "--fasta", type=str, required=True) + parser.add_argument("-l", "--ligand_file", type=str, default=None) + parser.add_argument("-n", 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|(69.73,210.716,48.696;68.851,211.384,49.656;69.388,211.914,50.794;68.62,212.565,51.727;67.32,212.703,51.565;66.723,212.17,50.411;67.473,211.498,49.447;66.597,211.104,48.453;65.349,211.511,48.809;65.416,212.166,50.007;64.331,212.797,50.76;63.173,212.348,50.353;62.368,213.613,49.64;61.556,213.018,48.606;61.928,212.064,47.881;60.225,213.547,48.447;59.304,213.048,47.478;57.893,213.017,48.158;57.751,211.883,48.99;63.343,214.423,49.216;62.884,215.795,49.071;64.346,214.334,50.414;63.905,215.062,51.474)| +CCn1c2ccc(C[NH2+]C)cc2c2ccc(Br)cc21 |(93.436,92.705,-41.565;91.913,92.963,-41.585;91.585,94.105,-42.399;91.489,95.403,-42.003;91.688,95.995,-40.741;91.54,97.396,-40.604;91.18,98.206,-41.715;91.029,99.678,-41.577;89.723,100.056,-41.003;89.571,101.511,-40.789;90.992,97.598,-42.977;91.144,96.202,-43.107;91.02,95.34,-44.197;90.702,95.547,-45.557;90.663,94.417,-46.414;90.946,93.117,-45.926;90.905,91.612,-47.031;91.267,92.939,-44.572;91.298,94.055,-43.72)| 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|(-0.5135,3.7454,-34.9094;-1.195,4.307,-34.399;-1.536,3.973,-33.162;-2.424,4.71,-32.506;-0.988,2.904,-32.587;-1.289,2.44,-31.232;-0.353,3.046,-30.194;-0.684,2.576,-28.78;0.164,3.257,-27.707;1.57,2.886,-27.858;-0.329,2.903,-26.305;-0.266,1.751,-25.874;-0.826,3.911,-25.6;-1.23,3.78,-24.208;-0.452,4.79,-23.366;1.041,4.712,-23.605;1.671,3.486,-23.768;3.031,3.407,-23.995;3.776,4.561,-24.066;5.133,4.491,-24.286;3.174,5.789,-23.911;1.813,5.86,-23.689;-2.725,4.014,-24.079;-3.3,4.838,-24.793;-3.347,3.279,-23.163;-4.776,3.401,-22.914;-5.141,4.763,-22.317;-5.603,3.108,-24.164;-6.674,3.699,-24.366;-5.122,2.174,-24.981;-5.849,1.694,-26.154;-6.393,0.301,-25.857;-5.642,-0.604,-25.463;-7.692,0.128,-26.069;-8.35,-1.143,-25.747;-7.924,-2.197,-26.759;-7.846,-1.901,-27.967;-7.643,-3.431,-26.336;-7.392,-3.88,-24.954;-7.235,-5.373,-25.101;-6.914,-5.672,-26.489;-7.425,-4.493,-27.323;-8.728,-4.844,-28.039;-8.826,-4.408,-29.182;-9.516,-5.519,-27.383;-9.864,-0.957,-25.772;-10.568,-2.256,-25.455;-10.311,0.119,-24.785;-4.962,1.697,-27.41;-5.699,1.084,-28.592;-4.516,3.115,-27.745)| 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|(-25.431,9.824,-5.823;-24.366,9.885,-4.775;-23.584,8.759,-4.472;-22.582,8.791,-3.482;-22.318,9.967,-2.738;-21.002,9.976,-1.461;-20.007,8.502,-1.56;-20.426,7.543,-0.628;-19.7,6.266,-0.649;-20.322,5.321,0.356;-21.638,5.079,-0.052;-18.2,6.586,-0.451;-17.981,7.02,0.818;-17.692,7.61,-1.524;-16.264,7.756,-1.489;-15.526,8.311,-0.431;-14.176,8.283,-0.828;-12.955,8.704,-0.115;-12.767,8.582,1.274;-13.774,8.117,1.95;-11.564,8.974,1.962;-11.508,8.817,3.283;-10.426,9.486,1.307;-9.248,9.865,1.957;-10.603,9.583,-0.086;-9.584,10.06,-0.817;-11.813,9.2,-0.751;-11.892,9.324,-2.062;-14.201,7.714,-2.122;-15.387,7.426,-2.496;-18.5,8.912,-1.405;-18.099,9.736,-2.472;-23.101,11.096,-3.035;-24.106,11.049,-4.034)| +CC(C)CN(C[C@@H](O)[C@H](CC1CCCCC1)NC(=O)O[C@H]1CO[C@H]2OCC[C@@H]12)S(=O)(=O)c1ccc([C@@H](O)CO)cc1 |(73.467,54.619,16.19;72.956,54.636,14.753;73.757,53.591,13.923;71.419,54.238,14.71;70.572,55.249,15.415;70.179,56.482,14.661;71.047,57.633,15.158;70.843,58.745,14.36;70.683,57.983,16.624;69.313,58.662,16.713;69.051,59.085,18.18;69.193,60.551,18.589;68.937,60.968,20.026;68.531,59.916,21.079;68.389,58.438,20.67;68.635,58.021,19.241;71.668,58.92,17.07;72.645,58.606,18.168;72.673,57.551,18.747;73.511,59.681,18.445;74.291,59.669,19.653;75.743,60.282,19.208;75.504,61.589,19.065;74.459,61.926,20.16;73.532,62.626,19.682;72.128,61.804,19.846;72.36,60.749,20.588;73.85,60.523,20.572;69.345,54.59,16.443;68.401,55.596,16.937;69.916,53.909,17.609;68.532,53.371,15.349;67.507,53.839,14.36;66.87,52.796,13.437;67.312,51.345,13.579;66.708,50.306,12.634;65.828,50.974,11.8;67.892,49.82,11.833;68.665,50.924,11.449;68.369,50.888,14.563;68.989,51.919,15.48)| +COc1cc(/C=C/C(C)=O)ccc1O |(-21.985,12.777,-17.236;-22.043,11.919,-16.109;-22.276,12.506,-14.853;-22.44,13.881,-14.672;-22.672,14.374,-13.385;-22.859,15.86,-13.118;-22.662,16.773,-14.084;-22.878,18.228,-13.742;-23.361,18.611,-12.362;-22.687,19.079,-14.584;-22.753,13.516,-12.295;-22.591,12.149,-12.466;-22.356,11.658,-13.749;-22.198,10.281,-13.938)| +CCCC[C@H]([NH3+])C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)O)[C@@H](C)O)[C@@H](C)O)[C@@H](C)O |(11.997,10.186,22.676;13.134,10.736,23.521;14.026,9.61,24.039;15.272,10.14,24.744;16.312,9.053,25.027;16.916,8.577,23.785;15.661,7.893,25.773;15.509,6.8,25.226;15.258,8.144,27.013;14.682,7.092,27.834;14.608,7.535,29.295;13.296,6.718,27.341;12.575,7.534,26.759;12.924,5.464,27.577;11.575,4.991,27.329;11.55,3.904,26.245;12.305,2.651,26.66;13.543,2.643,26.512;11.66,1.683,27.125;10.979,4.45,28.622;11.691,4.176,29.594;9.66,4.296,28.621;8.922,3.726,29.739;8.063,2.573,29.234;7.666,2.541,28.066;7.779,1.619,30.123;6.936,0.478,29.793;5.516,0.617,30.341;4.786,-0.378,30.415;5.11,1.824,30.727;3.791,2.124,31.251;3.285,3.417,30.628;4.051,4.143,29.985;1.981,3.736,30.775;0.887,3.028,31.467;-0.201,4.059,31.528;-0.023,4.874,30.301;1.496,4.958,30.118;2.035,6.248,30.736;2.502,7.119,30.005;1.994,6.397,31.956;3.829,2.244,32.786;4.169,0.901,33.427;4.802,3.225,33.166;7.572,-0.82,30.301;9.07,-0.852,29.98;7.385,-0.933,31.72;8.036,4.79,30.427;7.04,5.395,29.43;7.323,4.211,31.534)| +CCC(=O)Nc1nc(-c2ccc(Cl)cc2)cs1 |(-6.186,5.005,-2.562;-5.875,6.456,-2.992;-4.89,7.077,-2.052;-5.086,7.11,-0.859;-3.717,7.57,-2.687;-2.598,8.151,-2.079;-1.451,8.36,-2.703;-0.439,8.754,-1.778;0.891,9.065,-2.33;1.86,9.759,-1.585;3.13,9.973,-2.124;3.424,9.484,-3.414;4.976,9.75,-4.099;2.503,8.799,-4.169;1.238,8.577,-3.63;-0.86,8.864,-0.49;-2.523,8.451,-0.326)| +CNc1c(F)c(F)c(-c2cn([C@@H]3[C@@H](O)[C@H](Sc4ccc(C)cc4)O[C@H](CO)[C@@H]3O)nn2)c(F)c1F |(-9.364,11.186,2.372;-9.633,10.04,1.519;-10.893,9.659,0.885;-11.144,9.846,-0.472;-10.212,10.426,-1.246;-12.318,9.444,-1.072;-12.491,9.648,-2.385;-13.321,8.811,-0.365;-14.578,8.358,-0.99;-15.867,8.353,-0.521;-16.595,7.807,-1.506;-18.04,7.533,-1.544;-18.906,8.813,-1.47;-18.643,9.618,-2.607;-20.326,8.253,-1.55;-21.621,9.501,-1.626;-22.821,8.514,-2.491;-23.54,7.572,-1.767;-24.434,6.736,-2.408;-24.645,6.818,-3.779;-25.606,5.891,-4.474;-23.928,7.774,-4.489;-23.023,8.611,-3.861;-20.587,7.424,-0.441;-19.895,6.186,-0.611;-20.395,5.311,0.484;-21.75,5.057,0.249;-18.409,6.479,-0.508;-18.131,6.97,0.789;-15.817,7.483,-2.561;-14.592,7.822,-2.244;-13.08,8.616,0.981;-14.017,8.021,1.72;-11.921,9.041,1.588;-11.828,8.891,2.909)| +CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]1CO[C@H]2OCC[C@@H]12)S(=O)(=O)c1ccc([C@H](O)CO)cc1 |(73.373,54.692,15.849;72.834,54.732,14.419;73.621,53.748,13.549;71.356,54.354,14.374;70.528,55.33,15.072;70.188,56.574,14.396;71.052,57.713,14.94;70.885,58.85,14.148;70.673,58.062,16.375;69.302,58.734,16.436;69.05,59.142,17.883;68.663,58.098,18.924;68.435,58.53,20.367;68.587,59.992,20.761;68.979,61.027,19.722;69.213,60.6,18.281;71.633,59.024,16.87;72.605,58.712,17.888;72.638,57.66,18.46;73.515,59.743,18.131;74.471,59.632,19.275;75.572,60.008,18.891;75.486,61.533,18.967;74.5,61.803,20.076;73.253,62.709,19.497;72.191,61.974,19.493;72.466,60.848,20.466;74.041,60.693,20.428;69.347,54.662,16.106;69.991,53.971,17.216;68.451,55.669,16.659;68.482,53.471,15.088;67.469,53.952,14.063;66.788,52.912,13.2;67.137,51.441,13.38;66.467,50.41,12.494;66.553,50.901,11.186;65.012,50.229,12.87;64.495,49.172,12.101;68.162,50.977,14.392;68.843,52.011,15.258)| +[NH3+]Cc1ccc(-c2ncc(OCC3CC[NH2+]CC3)nc2-c2ccc(-c3ccoc3)cc2)cc1 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|(-3.141,-10.193,-10.729;-3.966,-10.717,-9.555;-3.993,-9.755,-8.352;-3.027,-9.563,-7.689;-5.224,-9.067,-8.007;-5.619,-8.095,-6.948;-6.944,-7.934,-7.598;-8.051,-8.332,-6.728;-8.239,-7.774,-5.383;-7.495,-6.966,-4.935;-9.439,-8.272,-4.567;-9.612,-7.484,-3.35;-10.412,-6.434,-3.255;-10.33,-5.922,-1.958;-11.117,-4.698,-1.414;-11.323,-4.618,-0.247;-11.63,-3.659,-2.309;-11.402,-3.723,-3.773;-11.637,-2.455,-4.57;-12.912,-1.748,-4.141;-13.28,-1.766,-2.833;-14.473,-1.101,-2.406;-15.253,-0.432,-3.352;-14.871,-0.418,-4.697;-13.705,-1.064,-5.114;-13.288,-1.075,-6.461;-13.819,-0.098,-7.317;-12.402,-2.504,-1.775;-9.416,-6.765,-1.255;-8.909,-6.79,0.067;-7.989,-7.777,0.45;-7.304,-7.807,2.265;-7.559,-8.745,-0.47;-8.053,-8.723,-1.772;-8.99,-7.72,-2.162;-6.612,-9.023,-8.554)| +O=c1ccccc2[nH]c3c(Cl)cccc3c12 |(3.738,11.777,27.936;4.578,11.531,27.024;4.501,12.224,25.858;5.262,12.173,24.739;6.351,11.453,24.345;7.039,10.518,24.979;6.671,10.094,26.333;7.417,9.137,26.896;6.879,8.923,28.126;7.239,8.054,29.146;8.648,6.941,28.954;6.518,8.041,30.329;5.436,8.893,30.499;5.067,9.763,29.493;5.779,9.761,28.324;5.651,10.502,27.181)| +CC1(C)c2[nH]c3cc(Br)c(Cl)cc3c2C[C@@]23C[N@@H+]4CCC[C@]4(C[C@@H]12)C(=O)N3 |(1.71,58.167,114.392;2.605,59.342,114.888;1.694,60.241,115.799;3.335,60.048,113.703;2.796,60.902,112.704;3.838,61.327,111.822;3.862,62.165,110.687;5.056,62.37,110.05;5.159,63.497,108.506;6.24,61.783,110.485;7.686,62.126,109.596;6.247,60.969,111.583;5.051,60.719,112.284;4.671,59.95,113.435;5.571,59.146,114.272;5.16,59.272,115.722;5.364,60.663,116.454;4.584,60.616,117.733;5.308,61.173,118.897;4.67,60.469,120.122;3.897,59.252,119.587;4.296,59.192,118.128;3.243,58.607,117.246;3.719,58.667,115.8;5.612,58.392,117.947;6.227,57.812,118.764;6.017,58.439,116.572)| 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|(-14.886,-28.97,-9.641;-14.545,-30.31,-9.589;-14.455,-30.966,-8.381;-14.752,-30.252,-7.23;-14.635,-30.919,-5.881;-13.833,-31.932,-5.636;-13.918,-32.28,-4.319;-13.286,-33.3,-3.565;-13.578,-33.441,-2.212;-14.486,-32.594,-1.576;-15.11,-31.6,-2.32;-14.822,-31.46,-3.703;-15.287,-30.559,-4.703;-16.325,-29.451,-4.479;-15.732,-28.237,-3.937;-14.436,-27.975,-3.831;-14.328,-26.723,-3.255;-15.565,-26.293,-3.046;-16.413,-27.222,-3.438;-15.065,-28.908,-7.278;-15.15,-28.254,-8.493)| +CC(=O)N1c2ccccc2N(Cc2ccccc2C(=O)O)C[C@@H]1C1C=C1 |(39.183,15.361,5.816;40.505,15.464,6.514;41.31,14.547,6.461;40.792,16.61,7.229;39.948,17.75,7.356;39.204,18.261,6.297;38.411,19.379,6.49;38.37,20.006,7.717;39.117,19.524,8.777;39.925,18.402,8.597;40.704,17.876,9.69;40.895,18.695,10.888;40.11,18.185,12.073;39.067,17.285,11.87;38.334,16.795,12.935;38.629,17.196,14.219;39.659,18.09,14.449;40.408,18.591,13.383;41.509,19.551,13.682;41.532,20.68,13.208;42.433,19.089,14.471;41.877,17.048,9.384;42.1,16.677,7.925;43.102,17.561,7.192;43.998,16.933,6.171;44.558,17.355,7.482)| +[H]/N=C(/N)NCCC[C@@H]1NC(=O)[C@@H]2ON2C(=O)[C@H](CC(=O)O)NC(=O)[C@@H]2CCCN2C(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H]2CSSC[C@H](NC1=O)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCCC[NH3+])C(=O)N[C@@H](CO)C(=O)N[C@@H](CCCC[NH3+])C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N[C@@H]([C@@H](C)CC)C(=O)N2 |(-2.2565,-2.6093,31.7253;-1.242,-2.517,31.776;-0.621,-2.622,32.951;0.706,-2.497,33.009;-1.319,-2.848,34.067;-2.778,-3.005,34.127;-3.214,-4.337,34.782;-2.961,-4.318,36.275;-3.51,-5.473,37.122;-2.582,-5.724,38.223;-2.928,-6.448,39.283;-4.047,-6.944,39.423;-1.858,-6.631,40.344;-0.724,-5.796,39.798;-0.569,-6.05,40.014;-0.174,-4.838,40.345;-0.819,-4.188,41.17;1.272,-4.466,40.009;1.334,-3.093,39.347;0.224,-2.885,38.343;-0.338,-1.77,38.315;-0.085,-3.833,37.582;1.871,-5.476,39.147;2.452,-6.537,39.69;2.48,-6.705,40.915;3.1,-7.575,38.788;4.604,-7.31,38.628;4.931,-7.841,37.296;3.727,-7.545,36.43;2.614,-7.575,37.396;1.331,-7.834,37.135;0.49,-7.925,38.039;0.938,-8.044,35.681;0.812,-6.697,34.952;0.754,-6.83,33.449;1.908,-7.073,32.719;1.864,-7.195,31.334;0.653,-7.088,30.67;-0.508,-6.858,31.388;-0.452,-6.728,32.772;-0.309,-8.786,35.593;-0.443,-9.891,34.873;0.504,-10.456,34.32;-1.855,-10.457,34.788;-2.084,-11.464,35.919;-3.826,-11.903,36.066;-4.579,-10.401,37.25;-5.843,-9.519,36.244;-5.301,-8.377,35.366;-4.972,-7.182,36.145;-3.712,-6.757,36.311;-2.741,-7.39,35.889;-6.339,-7.998,34.319;-7.415,-7.504,34.67;-6.007,-8.212,33.043;-6.964,-8.029,31.957;-6.233,-7.855,30.618;-5.54,-6.517,30.557;-4.306,-6.319,31.175;-3.695,-5.087,31.16;-4.315,-4.024,30.541;-3.707,-2.793,30.517;-5.534,-4.19,29.926;-6.149,-5.427,29.955;-7.903,-9.22,31.92;-7.47,-10.369,32.029;-9.197,-8.942,31.765;-10.237,-9.911,32.108;-11.461,-9.163,32.627;-11.216,-8.565,34.029;-12.284,-7.558,34.458;-11.97,-6.991,35.857;-12.901,-5.882,36.259;-10.6,-10.837,30.952;-11.77,-11.205,30.781;-9.62,-11.216,30.137;-9.828,-12.282,29.174;-8.789,-12.203,28.055;-7.462,-12.237,28.577;-9.766,-13.637,29.882;-9.441,-13.734,31.071;-10.085,-14.691,29.141;-9.98,-16.054,29.658;-11.353,-16.727,29.706;-11.356,-18.076,30.405;-12.768,-18.531,30.735;-12.808,-20.026,31.006;-11.937,-20.412,32.158;-9.019,-16.846,28.786;-9.35,-17.148,27.624;-7.807,-17.18,29.261;-6.832,-17.913,28.434;-5.574,-17.907,29.28;-6.026,-17.721,30.69;-7.251,-16.811,30.566;-6.858,-15.334,30.617;-6.693,-14.7,29.567;-6.765,-14.779,31.819;-7.077,-15.387,33.126;-6.546,-14.365,34.105;-6.784,-13.063,33.412;-6.42,-13.366,31.955;-4.939,-13.132,31.702;-4.118,-14.053,31.71;-4.606,-11.872,31.455;-3.217,-11.433,31.367;-2.937,-10.648,30.078;-1.447,-10.371,29.954;-3.466,-11.405,28.852;-3.716,-10.517,27.623;-2.953,-10.588,32.605;-3.533,-9.506,32.758;-2.091,-11.08,33.496)| 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|(-56.145,24.507,9.2;-57.414,23.962,8.66;-57.504,24.234,7.149;-58.503,23.554,6.404;-58.591,23.691,5.01;-57.693,24.623,4.398;-57.863,24.842,2.924;-58.035,26.155,2.515;-58.201,26.538,1.161;-58.185,25.569,0.188;-58.418,25.961,-1.16;-58.695,24.754,-2.05;-57.461,24.689,-2.979;-56.95,23.281,-3.478;-55.909,23.407,-4.615;-56.445,24.418,-5.641;-56.585,25.776,-5.02;-57.82,25.706,-4.078;-58.015,24.234,0.561;-57.907,23.803,1.95;-57.631,22.305,2.315;-56.791,21.985,3.421;-56.472,20.606,3.698;-57.008,19.552,2.84;-56.584,18.132,3.209;-56.614,17.589,4.543;-56.086,16.304,4.401;-55.722,16.016,3.001;-56.008,17.211,2.265;-57.782,19.852,1.688;-58.079,21.228,1.428;-56.706,25.392,5.103;-56.598,25.202,6.563)| \ No newline at end of file diff --git a/scripts/test_sequences_from_pdb.fasta b/scripts/test_sequences_from_pdb.fasta new file mode 100644 index 0000000..8777f40 --- /dev/null +++ b/scripts/test_sequences_from_pdb.fasta @@ -0,0 +1,414 @@ +>6d08 +GEFVVEKVLDRRVVKGKVEYLLKWKGGSDEDNTWEPEENLDCPDLIAEFLQSQK +>6np2 +TPWSKSELVRQLRDLGVRSGDMVMPHVSLRAVGPLADGPQTLVDALIEAVGPTGNILAFVSWRDSPYEQTLGHDAPPAAIAQSWPAFDPDHAPAYPGFGAINEFIRTYPGCRRSAHPDASMAAIGPDAAWLVAPHEMGAAYGPRSPIARFLAHAGKILSIGAGPDAVTALHYAEAVARIEGKRRVTYSMPLLREGKRVWVTTSDWDSNGILDEYAAPDGPDAVERIARDYLARTRVAQGPVGGAQSRLIDAADIVSFGIEWLEARHAA +>6oxq +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6oio +VKYVDKIHIGNYEIDAWYFSPFPEDYGKQPKLWLCEYCLKYMKYEKSYRFHLGQCQWRQPPGKEIYRKSNISVHEVDGKDHKIYCQNLCLLAKLFLDHTLYFDVEPFVFYILTEVDRQGAHIVGYFSKEKESPDGNNVSCIMILPPYQRRGYGRFLIAFSYELSKLESTVGSPEKPLSDLGKLSYRSYWSWVLLENLRDFRGTLSIKDLSQMTSITQNDIISTLQSLNMVKYWKGQHVICVTPKLVEEHLKSAQYKKPPITVDSVCLKWAPP +>6moa +LSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMP +>6e4c +MAMEDFVRQCFNPMIVELAEKAMKEYGEDPKIETNKFAAICTHLEVCFMYSDFGHRFEIIEGRDRIMAWTVVNSICNTTGVEKPKFLPDLYDYKENRFIEIGVTRREVHIYYLEKANKIKEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRSLWDSFRQSER +>6g24 +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEATKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYID +>6s55 +SMQEEDTFRELRIFLRNVTHRLAIDKRFRVFTKPVDPDEVPDYVTVIKQPMDLSSVISKIDLHKYLTVKDYLRDIDLICSNALEYNPDRDPGDRLIRHRACALRDTAYAIIKEELDEDFEQLCEEIQESR +>6seo +PRSVASSKLWMLEFSAFLEQQQDPDTYNKHLFVHIGPYLEAVDIRQIYDKFPEKKGGLKDLFERGPSNAFFLVKFWADLNTNSSFYGVSSQYESPENMIITCSTKVCSFGKQVVEVETEYARYENGHYSYRIHRSPLCEYMINFIHKLKHLPEKYMMNSVLENFTILQVVTNRDTQETLLCIAYVFEVSASEHGAQHHIYRLVK +>5zk5 +PEHYIKHPLQNRWALWFFKNDKSKTWQANLRLISKFDTVEDFWALYNHIQLSSNLMPGCDYSLFKDGIEPMWEDEKNKRGGRWLITLNKQQRRSDLDRFWLETLLCLIGESFDDYSDDVCGAVVNVRAKGDKIAIWTTECENREAVTHIGRVYKERLGLPPKIVIGYQSHADTATTKNRFVV +>6jid +EAVVISGRKLAQQIKQEVRQEVEEWVASGNKRPHLSVILVGENPASHSYVLNKTRAAAVVGINSETIMKPASISEEELLNLINKLNNDDNVDGLLVQLPLPEHIDERRICNAVSPDKDVDGFHVINVGRMCLDQYSMLPATPWGVWEIIKRTGIPTLGKNVVVAGRSKNVGMPIAMLLHTDGAHERPGGDATVTISHRYTPKEQLKKHTILADIVISAAGIPNLITADMIKEGAAVIDVGINRVHDKPKLVGDVDFEGVRQKAGYITPVPGGVGPMTVAMLMKNTIIAAKKVLR +>6qlu +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6a6k +NKIVSHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLSFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNIKLEGKVPMHKLFLEMLE +>6qgf +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6te6 +LELRLKSPVGAEPAVYPWPLPVYDKHHDAAHEIIETIRWVCEEIPDLKLAMENYVLIDYDTKSFESMQRLCDKYNRAIDSIHQLWKGTNTRPSTGLLRHILQQVYNHSVTDPEKLNNYEPFSPEVYGETSFDLVAQMIDEIKMTDDDLFVDLGSGVGQVVLQVAAATNCKHHYGVEKADIPAKYAETMDREFRKWMKWYGKKHAEYTLERGDFLSEEWRERIANTSVIFVNNFAFGPEVDHQLKERFANMKEGGRIVSSKPFAPLNFRINSRNLSDIGTIMRVVELSPLKSWTGKPVSYYLHTIDRTILENYFSSLKNP +>6g2o +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEQKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYID +>5zxk +GAYNEWKKALFKPADVILDPKTANPILLVSEDQRSVQRAKEPQDLPDNPERFNWHYCVLGCESFISGRHYWEVEVGDRKEWHIGVCSKNVQRKGWVKMTPENGFWTMGLTDGNKYRTLTEPRTNLKLPKPPKKVGVFLDYETGDISFYNAVDGSHIHTFLDVSFSEALYPVFRILTLEPTALTICP +>6qxd +KYRVRKNVLHLTDTEKRDFVRTVLILKEKGIYDRYIAWHGAAGKFHTPPGSDRNAAHMSSAFLPWHREYLLRFERDLQSINPEVTLPYWEWETDAQMQDPSQSQIWSADFMGGNGNPIKDFIVDTGPFAAGRWTTIDEQGNPSGGLKRNFGATKEAPTLPTRDDVLNALKITQYDTPPWDMTSQNSFRNQLEGFINGPQLHNRVHRWVGGQMGVVPTAPNDPVFFLHHANVDRIWAVWQIIHRNQNYQPMKNGPFGQNFRDPMYPWNTTPEDVMNHRKLGYVYDIEL +>6oy1 +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6n96 +AYQYVNVVTINKVAVIEFNYGRKLNALSKVFIDDLMQALSDLNRPEIRCIILRAPSGSKVFSAGHDIHELPSGGRDPLSYDDPLRQITRMIQKFPKPIISMVEGSVWGGAFEMIMSSDLIIAASTSTFSMTPVNLGVPYNLVGIHNLTRDAGFHIVKELIFTASPITAQRALAVGILNHVVEVEELEDFTLQMAHHISEKAPLAIAVIKEELRVLGEAHTMNSDEFERIQGMRRAVYDSEDYQEGMNAFLEKRKPNFVGH +>5zk7 +ANPEHYIKHPLQNRWALWFFKWQANLRLISKFDTVEDFWALYNHIQLSSNLMPGCDYSLFKDGIEPMWEDEKNKRGGRWLITLNKQQRRSDLDRFWLETLLCLIGESFDDYSDDVCGAVVNVRAKGDKIAIWTTECENREAVTHIGRVYKERLGLPPKIVIGYQSHADTATTKNRFVV +>6k3l +RSVARVYANVNEKLGRSWWDYDNLVVQWGVQDNYEIVRKVGRGKYSEVFESIHLPTDSKCIVKVLKPVKKKKIKREIKILQNLAGGPNVVGLLDVVRDSQSKTPSIVTEYVNNTEFKTLYPKFSDFDVRYYIFELLKALDFCHSKGIMHRDVKPHNVMIDHEKRTLRLIDWGLAEFYHPGTEYNVRVASRYFKGPELLVDFQEYDYSLDMWSLGCMFASMIFRKEPFFHGHDNADQLVKIAKVLGTDELYTYLERYDIDLDAQFDDILGRYPRKPWSRFVSSENQRYISSEAIDFLDKLLRYDHQERLTAEEAKEHPYFEPVRQAAAQA +>6cjs +NLYFQGETHEFTAEISQLMSLIINTVYSNKEIFLRELISNASDALDKIRYQALSDPSQLESEPELFIRIIPQKDQKVLEIRDSGIGMTKADLVNNLGTIAKSGTKSFMEALSAGADVSMIGQFGVGFYSLFLVADHVQVISKHNDDEQYVWESNAGGKFTVTLDETNERLGRGTMLRLFLKEDQLEYLEEKRIKEVVKKHSEFVAYPIQLVVTKEVEK +>6gge +SVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKLFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRAEYLDDRNTFRHSVVVPCEPPEVGSDCTTIHYNYMCYSSCMGGMNRRPILTIITLEDSSGNLLGRDSFEVRVCACPGRDRRTEEENLRKK +>6hot +GPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKD +>6e3p +AMEDFVRQCFNPMIVELAEKAMKEYGEDPKIETNKFAAICTHLEVCFMYSDFGHRFEIIEGRDRIMAWTVVNSICNTTGVEKPKFLPDLYDYKENRFIEIGVTRREVHIYYLEKANKIEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRSLWDSFRQSER +>6npp +SNPKFENIAEGLRALLARSHVERTTDEGTWVAGVFVYGGSKTSLYNLRRGTALAIPQCRLTPLSRLPFGMAPGPGPQPGPLRESIVCYFMVFLQTHIFAEVLKDAIKDLVMTKPAPTCNIRVTVCSFDDGVDLP +>6g2f +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEQKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYID +>6mo7 +RVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQEL +>6bqd +DDDQVAFSFILDNIVTQKMMAVPDSWPFHHPVNKKFVPDYYKVIVNPMDLETIRKNISKHKYQSRESFLDDVNLILANSVKYNGPESQYTKTAQEIVNVCYQTLTEYDEHLTQLEKDICTAKEAALEEA +>6nsv +SPSAQELKEQGNRLFVGRKYPEAAACYGRAITRNPLVAVYYTNRALCYLKMQQHEQALADCRRALELDGQSVKAHFFLGQCQLEMESYDEAIANLQRAYSLAKEQRLNFGDDIPSALRIAKKKRWNSI +>6i76 +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6n53 +EPFLIGVSGGTASGKSSVCAKIVQLLGQNQKQVVILSQDSFYRVLTSEQKAKALKGQFNFDHPDAFDNELILKTLKEITEGKTVQIPVYDFVSHSRKEETVTVYPADVVLFEGILAFYSQEVRDLFQMKLFVDTDADTRLSRRVLRDISERGRDLEQILSQYITFVKPAFEEFCLPTKKYADVIIPRGADNLVAINLIVQHIQDILN +>6g2c +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEQKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYIDK +>6eeb +TEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQVNYEEFVQMMT +>6n0m +GLEALMSSGRVDNLAVVMGLHPDYFTSFWRLHYLLLHTDGPLASSWRHYIAIMAAARHQCSYLVGSHMAEFLQTGGDPEWLLGLHRAPEKLRKLSEINKLLAHRPWLITKEHIQALLKTGEHTWSLAELIQALVLLTHCHSLSSFVFGCGILPEGDPPSEQSSPRDVEALMERMQQLQESEEMESRFELEKSESLPDMLCFVEDPTFGYEDFTRRGAQAPPTFRAQDYTWEDHGYSLIQRLYPEGGQLLDEKFQAAYSLTYNTIAMHSGVDTSVLRRAIWNYIHCVFGIRYDDYDYGEVNQLLERNLKVYIKTVACYPEKTTRRMYNLFWRHFRHSEKVHVNLLLLEARMQAALLYALRAITRYMT +>6olx +EVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNASDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEK +>6v5l +MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEK +>6efk +SPSAQELKEQGNRLFVGRKYPEAAACYGRAITRNPLVAVYYTNRALCYLKMQQHEQALADCRRALELDGQSVKAHFFLGQCQLEMESYDEAIANLQRAYSLAKEQRLNFGDDIPSALRIAKKKRWNSIEERR +>6oxt +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6oy0 +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6cyg +EVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKE +>6g27 +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYID +>6ggb +SSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKLFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRAEYLDDRNTFRHSVVVPCEPPEVGSDCTTIHYNYMCYSSCMGGMNRRPILTIITLEDSSGNLLGRDSFEVRVCACPGRDRRTEEENLRKK +>6g3c +FHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCFCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAAAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKAAELANLINNCMDYEPDHRPSFRAIIRDLNSL +>6fcj +EVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVE +>6quv +MTEYKLVVVGADGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHK +>6i74 +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6rnu +SQSNRELVVDFLSYKLSQKGYSWSQFSESEAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYG +>6jib +EAVVISGRKLAQQIKQEVRQEVEEWVASGNKRPHLSVILVGENPASHSYVLNKTRAAAVVGINSETIMKPASISEEELLNLINKLNNDDNVDGLLVQLPLPEHIDERRICNAVSPDKDVDGFHVINVGRMCLDQYSMLPATPWGVWEIIKRTGIPTLGKNVVVAGRSKNVGMPIAMLLHTDGAHERPGGDATVTISHRYTPKEQLKKHTILADIVISAAGIPNLITADMIKEGAAVIDVGINRVHKPKLVGDVDFEGVRQKAGYITPVPGGVGPMTVAMLMKNTIIAAKKVLR +>6izq +SAVQYFQFYGYLSQQQNMMQDYVRTGTYQRAILQNHTDFKDKIVLDVGCGSGILSFFAAQAGARKIYAVEASTMAQHAEVLVKSNNLTDRIVVIPGKVEEVSLPEQVDIIISEPMGYMLFNERMLESYLHAKKYLKPSGNMFPTIGDVHLAPFTDEQLYMEQFTKANFWYQPSFHGVDLSALRGAAVDEYFRQPVVDTFDIRILMAKSVKYTVNFLEAKEGDLHRIEIPFKFHMLHSGLVHGLAFWFDVAFIGSIMTVWLSTAPTEPLTHWYQVRCLFQSPLFAKAGDTLSGTCLLIANKRQSYDISIVAQVDQTGSKSSNLLDLKNPFFRY +>6qw8 +ADVQQKLAELERQSGGRLGVALINTADNSQILYRADERFAMCSTSKVMAAAAVLKKSESEPNLLNQRVEIKKSDLVNYNPIAEKHVNGTMSLAELSAAALQYSDNVAMNKLIAHVGGPASVTAFARQLGDETFRLDRTEPTLNTAIPGDPRDTTSPRAMAQTLRNLTLGKALGDSQRAQLVTWMKGNTTGAASIQAGLPASWVVGDKTGSGGYGTTNDIAVIWPKDRAPLILVTYFTQPQPKAESRRDVLASAAKIVTDGL +>6e5s +TRDQNGTWEMESNENFEGYMKALDIDFATRKIAVRLTQTKVIDQDGDNFKDKTTSTFRNYDVDFTVGVEFDEYTKSLDNRHVKALVTWEGDVLVCVQKGEKENRGWKKWIEGDKLYLELTCGDQVCRQVFKKK +>6e6w +MAMEDFVRQCFNPMIVELAEKAMKEYGEDPKIETNKFAAICTHLEVCFMYSDFGHRFEIIEGRDRIMAWTVVNSICNTTGVEKPKFLPDLYDYKENRFIEIGVTRREVHIYYLEKANKIEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRSLWDSFRQSE +>6cyh +EVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKE +>6gga +SVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKLFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRAEYLDDRNTFRHSVVVPCEPPEVGSDCTTIHYNYMCYSSCMGGMNRRPILTIITLEDSSGNLLGRDSFEVRVCACPGRDRRTEEENLRK +>6pgp +MTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETSLLDILDTAGQEEYSAMRDQYMRTGEGFLLVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKSDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEK +>6qtq +TRYSRLRVIAEIRHNIVSSIEFDRDDELFATAGVSRIKVFDFSSVVNEPQCPIVEMSTRSKLSLSWNKHEKNHIASSDYEGIVTVWDVTTRQSLMEYEEHEKRAWSVDFSRTEPSMLVSGSDDCKVKVWTRQEASVINIDMKANICVKYNPGSSNYIAVGSADHHIHYYDLRNISQPLHVFSGHKKAVSYVKFLSNNELASASTDSTLRLWDVKDNLPVRTFRGHTNEKNFVGLTVNSEYLACGSETNEVYVYHKEITRPVTSHRFGGSYFISAVCWKSDSPTMLTANSQGTIKVLVLAA +>6gj6 +MTEYKLVVVGADGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHK +>6os5 +TTVRDACARTLENTARTLHLGASGTEFVAAFRAMTDHWGAARPHDLPLSDVSPDGSPVEYAVDLGGLAPALQFAMEPLTAGVPARDPLAARAIMPLLAGRYGADATRWSALADRLLPDDAHGPHVSMYGAEVRAGAPIRFKAWFYLNVTGPDGAFNLLYSALERMGTTHLWPVVQAHVHRAGEDVPFLLSLDLSDDPAARVKVYFRHFAADVEEVAAVLKAYPGFEPGEVRAFCKVMMGGRRRFSDQPAVTCVSLLDAQTFDRTAATLYVPLWTYAEHDGEVRQRVHRTLAAWPEALYRYDSVLAGIAHRGLDAGTGIHNYISWQPGRTRPRMKVYLSPEMHDVTPPPLGVSQ +>6i77 +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6oxx +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6hor +GPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKD +>6i68 +SGKVVKFSYMWTINNFSFCREEMGEVIKSSTFSSGANDKLKWCLRVNPKGLDEESKDYLSLYLLLVSCPKSEVRAKFKFSILNAKGEETKAVESQRAYRFVQGKDWGFKKFIRRDFLLDEANGLLPDDKLTLFCEVSVVQD +>6mhb +ARSLGKGSAPPGPVPEGSIRIYSMRFCPFAERTRLVLKAKGIRHEVININLKNKPEWFFKKNPFGLVPVLENSQGQLIYESAITCEYLDEAYPGKKLLPDDPYEKACQKMILELFSKVPSLVGSFIRKEDYAGLKEEFRKEFTKLEEVLTNKKTTFFGGNSISMIDYLIWPWFERLEAMKLNECVDHTPKLKLWMAAMKEDPTVSALLTSEKDWQGFLELYLQNSPEACDYGL +>6i78 +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6oxy +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6mo0 +MADLELERAADVRWEEQAEISGSSLEDGAYRIKQKGILGYSQIGAGVYKEGTFHTMWHVTRGAVLMHKGKRIEPSWADVKKDLISYGGGWKLEGEWKEGEEVQVLALEPGKNPRAVQTKPGLFKTNTGTIGAVSLDFSPGTSGSPIVDKKGKVVGLYGIANTEK +>6ggf +SVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKLFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRAEYLDDRNTFRHSVVVPCEPPEVGSDCTTIHYNYMCYSSCMGGMNRRPILTIITLEDSSGNLLGRDSFEVRVCACPGRDRRTEEENLRK +>6qge +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6cjr +GETHEFTAEISQLMSLIINTVYSNKEIFLRELISNASDALDKIRYQALSDPSQLESEPELFIRIIPQKDQKVLEIRDSGIGMTKADLVNNLGTIAKSGTKSFMEALSAGADVSMIGQFGVGFYSLFLVADHVQVISKHNDDEQYVWESNAGGKFTVTLDETNERLGRGTMLRLFLKEDQLEYLEEKRIKEVVKKHSEFVAYPIQLVVTKEVEK +>6oxp +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6d07 +GEYVVEKVLDRRVVKGKVEYLLKWKGFSDEDNTWEPEENLDCPDLIAEFLQSQK +>6i63 +YNKIVSHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLSFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNIKLEGKVPMHKLFLEMLEAKV +>6ten +LELRLKSPVGAEPAVYPWPLPVYDKHHDAAHEIIETIRWVCEEIPDLKLAMENYVLIDYDTKSFESMQRLCDKYNRAIDSIHQLWKGNTRPSTGLLRHILQQVYNHSVTDPEKLNNYEPFSPEVYGETSFDLVAQMIDEIKMTDDDLFVDLGSGVGQVVLQVAAATNCKHHYGVEKADIPAKYAETMDREFRKWMKWYGKKHAEYTLERGDFLSEEWRERIANTSVIFVNNFAFGPEVDHQLKERFANMKEGGRIVSSKPFAPLNFRINSRNLSDIGTIMRVVELSPLKSWTGKPVSYYLHTIDRTILENYFSSLKNP +>6qlr +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6sen +RSVASSKLWMLEFSAFLEQQQDPDTYNKHLFVHIGQSYLEAVDIRQIYDKFPEKKGGLKDLFERGPSNAFFLVKFWADLNTNSSFYGVSSQYESPENMIITCSTKVCSFGKQVVEVETEYARYENGHYSYRIHRSPLCEYMINFIHKLKHLPEKYMMNSVLENFTILQVVTNRDTQETLLCIAYVFEVSASEHGAQHHIYRLVK +>6oxv +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6g2b +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEAQKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYIDKQ +>5zr3 +MEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVE +>6e6v +MAMEDFVRQCFNPMIVELAEKAMKEYGEDPKIETNKFAAICTHLEVCFMYSDFGHRFEIIEGRDRIMAWTVVNSICNTTGVEKPKFLPDLYDYKENRFIEIGVTRREVHIYYLEKANKIKSEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRSLWDSFRQSERGE +>5zk9 +PEHYIKHPLQNRWALWFFKNDKSKTWQANLRLISKFDTVEDFWALYNHIQLSSNLMPGCDYSLFKDGIEPMWEDEKNKRGGRWLITLNKQQRRSDLDRFWLETLLCLIGESFDDYSDDVCGAVVNVRAKGDKIAIWTTECENREAVTHIGRVYKERLGLPPKIVIGYQSHADTATKTKNRFVV +>6pnn +ESARSLGKGSAPPGPVPEGSIRIYSMRFCPFAERTRLVLKAKGIRHEVININLKNKPEWFFKKNPFGLVPVLENSQGQLIYESAITCEYLDEAYPGKKLLPDDPYEKACQKMILELFSKVPSLVGSFIRSQNKEDYAGLKEEFRKEFTKLEEVLTNKKTTFFGGNSISMIDYLIWPWFERLEAMKLNECVDHTPKLKLWMAAMKEDPTVSALLTSEKDWQGFLELYLQNSPEACDYGL +>6nri +KSKLPKPVQDLIKMIFGGDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFK +>6npi +SNPKFENIAEGLRALLARSHVERTTDEGTWVAGVFVYGGSKTSLYNLRRGTALAIPQCRLTPLSRLPFGMAPGPGPQPGPLRESIVCYFMVFLQTHIFAEVLKDAIKDLVMTKPAPTCNIRVTVCSFDDGVDLP +>6oip +KYVDKIHIGNYEIDAWYFSPFPEDYGKQPKLWLCEYCLKYMKYEKSYRFHLGQCQWRQPPGKEIYRKSNISVHEVDGKDHKIYCQNLCLLAKLFLDHTLYFDVEPFVFYILTEVDRQGAHIVGYFSKEKESPDGNNVSCIMILPPYQRRGYGRFLIAFSYELSKLESTVGSPEKPLSDLGKLSYRSYWSWVLLENLRDFRGTLSIKDLSQMTSITQNDIISTLQSLNMVKYWKGQHVICVTPKLVEEHLKSAQYKKPPITVDSVCLKWAPP +>6s57 +SMQEEDTFRELRIFLRNVTHRLAIDKRFRVFTKPVDPDEVPDYVTVIKQPMDLSSVISKIDLHKYLTVKDYLRDIDLICSNALEYNPDRDPGDRLIRHRACALRDTAYAIIKEELDEDFEQLCEEIQESR +>6p8x +TEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETSLLDILDTAGQEEYSAMRDQYMRTGEGFLLVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKSDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHK +>6hoq +GPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKD +>6qts +TRYSRLRVIAEIRHNIVSSIEFDRDDELFATAGVSRIKVFDFSSVVNEPQPIVEMSTRSKLSLSWNKHEKNHIASSDYEGIVTVWDVTTRQSLMEYEEHEKRAWSVDFSRTEPSMLVSGSDDKVKVWTRQEASVINIDMKANICVKYNPGSSNYIAVGSADHHIHYYDLRNISQPLHVFSGHKKAVSYVKFLSNNELASASTDSTLRLWDVKDNLPVRTFRGHTNEKNFVGLTVNSEYLACGSETNEVYVYHKEITRPVTSHRFGGSYFISAVCWKSDSPTMLTANSQGTIKVLVLAA +>6ggd +SVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKLFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRAEYLDDRNTFRHSVVVPCEPPEVGSDCTTIHYNYMCYSSCMGGMNRRPILTIITLEDSSGNLLGRDSFEVRVCACPGRDRRTEEENLRK +>6pnm +GESARSLGKGSAPPGPVPEGSIRIYSMRFCPFAERTRLVLKAKGIRHEVININLKNKPEWFFKKNPFGLVPVLENSQGQLIYESAITCEYLDEAYPGKKLLPDDPYEKACQKMILELFSKVPSLVGSFIRSQNKEDYAGLKEEFRKEFTKLEEVLTNKKTTFFGGNSISMIDYLIWPWFERLEAMKLNECVDHTPKLKLWMAAMKEDPTVSALLTSEKDWQGFLELYLQNSPEACDYGL +>6oy2 +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6mhd +SNAMSGESARSLGKGSAPPGPVPEGSIRIYSMRFCPFAERTRLVLKAKGIRHEVININLKNKPEWFFKKNPFGLVPVLENSQGQLIYESAITCEYLDEAYPGKKLLPDDPYEKACQKMILELFSKVPSLVGSFIRSQNKEDYAGLKEEFRKEFTKLEEVLTNKKTTFFGGNSISMIDYLIWPWFERLEAMKLNECVDHTPKLKLWMAAMKEDPTVSALLTSEKDWQGFLELYLQNSPEACDYGL +>6hhr +VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEK +>6p8z +GMTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETSLLDILDTAGQAMRDQYMRTGEGFLLVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKSDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKE +>6c85 +SKKRCGVLGATGAVGTRFILLLEQSPLLELVAVGASERSAGKKYRDAVRWKQASPMPASVADLTVRRCAPSEFSDCDIIFSGLDPAGDIEMAFLKANFAVFSNAKNYRLDPIVPLVVPLVNAGHIDVIPAQRKHYGLDKGLIVCNSNCAVVGLVIPAKALIQKFGPIESVSMVTMQAVSGAGGVSSMDIFDNIVPFIPGEEGKIATESRKILGNLNPDLAGFSDQQPLQVSVACNRVPVLDGHTVCASLRFVNRPAPTASQVRDALREYKSEVQLLGCPSAPRQAIHVLDDVDRPQPRLDRDTEAGYACSVGRIREDDSGVFDIQFVALSHNTVLGASGSSILNAESAILKGYI +>6g5u +SWGYREHNGPIHWKEFFPIADGDQQSPIEIKTKEVKYDSSLRPLSIKYDPSSAKIISNSGHSFNVDFDDTENKSVLRGGPLTGSYRLRQVHLHWGSADDHGSEHIVDGVSYAAELHVVHWNSDKYPSFVEAAHEPDGLAVLGVFLQIGEPNSQLQKITDTLDSIKEKGKQTRFTNFDLLSLLPPSWDYWTYPGSLTVPPLLESVTWIVLKQPINISSQQLAKFRSLLCTAEGEAAAFLVSNHRPPQPLKGRKVRASFH +>6j06 +GAYNEWKKALFKPADVILDPKTANPILLVSEDQRSVQRAKEPQDLPDNPERFNWHYCVLGCESFISGRHYWEVEVGDRKEWHIGVCSKNVQRKGWVKMTPENGFWTMGLTDGNKYRTLTEPRTNLKLPKPPKKVGVFLDYETGDISFYNAVDGSHIHTFLDVSFSEALYPVFRILTLEPTALTICPA +>6qsz +SKSSWRQEWLANLKLISVSLVDEFPSELSDSDRQIINEKQLLKDIFANNLKSAISNNFRESDIIILKGEIEDYPSSEIKIYYNELQNKPKARFWSFKTQRFVSNGFDI +>6np5 +TPWSKSELVRQLRDLGVRSGDMVMPHVSLRAVGPLADGPQTLVDALIEAVGPTGNILAFVSWRDSPYEQTLGHDAPPAAIAQSWPAFDPDHAPAYPGFGAINEFIRTYPGCRRSAHPDASMAAIGPDAAWLVAPHEMGAAYGPRSPIARFLAHAGKILSIGAGPDAVTALHYAEAVARIEGKRRVTYSMPLLREGKRVWVTTSDWDSNGILDEYAAPDGPDAVERIARDYLARTRVAQGPVGGAQSRLIDAADIVSFGIEWLEARHAA +>6qlp +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6qls +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6st3 +PALKLALEYIVPCMNKHGICVVDDFLGKETGQQIGDEVRALHDTGKFTDGQLVSQIRGDKITWIEGKEPGCETIGLLMSSMDDLIRHCNGKLGSYKINGRTKAMVACYPGNGTGYVRHVDNPNGDGRCVTCIYYLNKDWDAKVSGGILRIFPEGKAQFADIEPKFDRLLFFWSDRRNPHEVQPAYATRYAITVWYFDADERARAKVKYLT +>6s56 +SMQEEDTFRELRIFLRNVTHRLAIDKRFRVFTKPVDPDEVPDYVTVIKQPMDLSSVISKIDLHKYLTVKDYLRDIDLICSNALEYNPDRDPGDRLIRHRACALRDTAYAIIKEELDEDFEQLCEEIQESR +>6k05 +TNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQE +>6e3m +MAMEDFVRQCFNPMIVELAEKAMKEYGEDPKIETNKFAAICTHLEVCFMYSDFGLKHRFEIIEGRDRIMAWTVVNSICNTTGVEKPKFLPDLYDYKENRFIEIGVTRREVHIYYLEKANKIEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRSLWDSFRQSER +>6qtx +TRYSRLRVIAEIRNIVSSIEFDRDDELFATAGVSRCIKVFDFSSVVNEQCPIVEMSTRSKLSCLSWNKHEKNHIASSDYEGIVTVWDVTTRQSLMEYEEHEKRAWSVDFSRTEPSMLVSGSDDCKVKVWTRQEASVINIDMKANICVKYNPGSSNYIAVGSADHHIHYYDLRNISQPLHVFSGHKKAVSYVKFLSNNELASASTDSTLRLWDVKDNLPVRTFRGHTNEKNFVGLTVNSEYLACGSETNEVYVYHKEITRPVTSHRFEEEAGSYFISAVCWKSDSPTMLTANSQGTIKVLVLAA +>5zjy +HYIKHPLQNRWALWFFKNDKSKTWQANLRLISKFDTVEDFWALYNHIQLSSNLMPGCDYSLFKDGIEPMWEDEKNKRGGRWLITLNKQQRRSDLDRFWLETLLCLIGESFDDYSDDVCGAVVNVRAKGDKIAIWTTECENREAVTHIGRVYKERLGLPPKIVIGYQSHADTATTKNRFVV +>6o3y +MDFQNFVATLESFKDLKSGISGSRIKKLTTYALDHIDIESKIISLIIDYSRLCPDSHKLGSLYIIDSIGRAYLDETRSNSNSSSNKPGTCAHAINTLGEVIQELLSDAIAKSNQDHKEKIRMLLDIWDRSGLFQKSYLNAIRSKCFA +>6rpg +AFTVTVPKDLYVVEYGSNMTIECKFPVEKQLDLAALIVYWEMEDKNIIQFVHGEEDLKVQHSSYRQRARLLKDQLSLGNAALQITDVKLQDAGVYRCMISYGGADYKRITVKVNAPY +>6rr0 +LSKSSWRQEWLANLKLISVSLVDEFPSELSDSDRQIINEKMQLLKDIFANNLKSAISNNFRESDIIILKGEIEDYPMSSEIKIYYNELQNKPDAKKARFWSFMKTQRFVSNMGFDI +>6gzy +QGLAMYLQENGIDCPKCKFSYALARGGCMHFHCTQCRHQFCSGCYNAFYAKNKCPEPNCRVKKSLHGHHPRDCLFYLRDWTALRLQKLLQDNNVMFNTEPPGCRVIEQRDEACGKETPAGYAGLCQAHYKEYLVSLINAHSLDPATLYEVEELETATERYLHVRPQPLAGEDPPAYQARLLQKLTEEVPLGQSIPRRRK +>6qlt +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6o3x +HMDFQNFVATLESFKDLKSGISGSRIKKLTTYALDHIDIESKIISLIIDYSRLCPDSHKLGSLYIIDSIGRAYLDETRSNSNSSSNKPGTCAHAINTLGEVIQELLSDAIAKSNQDHKEKIRMLLDIWDRSGLFQKSYLNAIRSKCFA +>5zjz +EHYIKHPLQNRWALWFFKNDKSKTWQANLRLISKFDTVEDFWALYNHIQLSSNLMPGCDYSLFKDGIEPMWEDEKNKRGGRWLITLNKQQRRSDLDRFWLETLLCLIGESFDDYSDDVCGAVVNVRAKGDKIAIWTTECENREAVTHIGRVYKERLGLPPKIVIGYQSHADTATKTKNRFVV +>6i8t +KSKLAKPIQDLIKMIFDVESMKKAMVEFEIDLQKMPLGKLSKRQIQSAYSILNEVQQAVSDGGSESQILDLSNRFYTLIPHDFGMKKPPLLSNLEYIQAKVQMLDNLLDIEVAYSLLRGGNEDGDKDPIDINYEKLRTDIKVVDKDSEEAKIIKQYVKNTHAATHNAYDLKVVEIFRIEREGESQRYKPFKQLHNRQLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQADPIGLILLGEVALGNMYELKNASHITKLPKGKHSVKGLGKTAPDPTATTTLDGVEVPLGNGISTGINDTCLLYNEYIVYDVAQVNLKYLLKLKFNY +>6oiq +KYVDKIHIGNYEIDAWYFSPFPEDYGKQPKLWLCEYCLKYMKYEKSYRFHLGQCQWRQPPGKEIYRKSNISVHEVDGKDHKIYCQNLCLLAKLFLDHTLYFDVEPFVFYILTEVDRQGAHIVGYFSKEKESPDGNNVSCIMILPPYQRRGYGRFLIAFSYELSKLESTVGSPEKPLSDLGKLSYRSYWSWVLLENLRDFRGTLSIKDLSQMTSITQNDIISTLQSLNMVKYWKGQHVICVTPKLVEEHLKSAQYKKPPITVDSVCLKWAPP +>6nrh +KSKLPKPVQDLIKMIFGDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFK +>6m7h +LTEEQIAEFKEAFSLFDKDGDGTITTKELGTVRSLGQNPTEAELQDINEVDADGNGTIDFPEFLTARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVTNLGEKLTDEEVDEIREADIDGDGQVNYEEFVQTA +>6o5u +TPWSKSELVRQLRDLGVRSGDMVMPHVSLRAVGPLADGPQTLVDALIEAVGPTGNILAFVSWRDSPYEQTLGHDAPPAAIAQSWPAFDPDHAPAYPGFGAINEFIRTYPGCRRSAHPDASMAAIGPDAAWLVAPHEMGAAYGPRSPIARFLAHAGKILSIGAGPDAVTALHYAEAVARIEGKRRVTYSMPLLREGKRVWVTTSDWDSNGILDEYAAPDGPDAVERIARDYLARTRVAQGPVGGAQSRLIDAADIVSFGIEWLEARHAA +>6jwa +SGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVV +>6ny0 +DATFGMGDRVRKKSGAAWQGQIVGWYCTNLTPEGYAVESEAHPGSVQIYPVAALERIN +>6ftf +GIDPEKAKLYQAPYFEKSEDEMNLITKLLTHNVLFSFLNTKDIKVVAGAMQRATFKHDDCIMEAGQTTCNKLYIIQSGHADIIKEGQKVYLKTEGTAVGELELMYDTPVVATVKVCTDELIAWVLDRDTYRNLVMGTAIRRRETYIQFLANVPFLGGLDSYEKLQLADALSSEEFSPGEYIIHYGEEGEWLYIIMEGTVEVIGRDADGEPTKVCEFTQGDHIGELEFLNNHRTVADVVATTHVITAKLNRRHFEMCLGPVIDVLKRCADDPKYEYYQNVLKTGAAQPSYVD +>6oxw +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6jon +HMIMEIPAIKALSRYAQWVIWKKERDTKIPYNPNNGKKASSTDPLAWGDIDEAQAGLVRYGANGLGFVLTKSDPFVFIDLDHVLDENKRVKCEWARQLLKEIKSYTEISPSGDGLHVVVSGKLPDYIKHKTKFDDGSALEVYESGRYMTITGEVFDGRDDIKELDLSILGEFAEHKILDDEAIIDLMKRKGQWPDAPKDGDDWSSLDMSFANRLAFWCGKDIERMDRIFRQSPLMRQKWDRPTAGSTYGRITLKKACDFVDSVYDPALRNESDCPFEPYN +>6mo8 +RVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQEL +>6qln +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6i66 +YNKIVSHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLSFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNIKLEGKVPMHKLFLEMLEA +>6d3z +FDCGKPQVEPKKCPGVVGGCVAHPHSWPWQVSLRTRFGMHFCGGTLISPEWVLTAAHCLEKSPRPSSYKVILGAHQEVNLEPHVQEIEVSRLFLEPTRKDIALLKLSSPAVITDKVIPACLPSPNYVVADRTECFITGWGETQGTFGAGLLKEAQLPVIENKVCNRYEFLNGRVQSTELCAGHLAGGTDSCQGDAGGPLVCFEKDKYILQGVTSWGLGCARPNKPGVYVRVSRFVTWIEGVMRNN +>6oxr +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6hou +GPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKD +>6py0 +RWVQFMKEAGQGSRDMWRAYSDMKKANWKNSDKYFHARGNYDAARRGPGGAWAAKVISDAREAVQKFTGHGDSRADQFANEWGRSGKDPNHFRPAGLPKRY +>6qlq +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6n4b +MVLNPSQQLAIAVLSLTLGTFTVLENLLVLCVILHSRSLRCRPSYHFIGSLAVADLLGSVIFVYSFIDFHVFHRKDSRNVFLFKLGGVTASFTASVGSLFLTAIDRYISIHRPLAYKRIVTRPKAVVAFCLMWTIAIVIAVLPLLGWNCCSDIFPHIDETYLMFWIGVTSVLLLFIVYAYMYILWKAHSHAVRMIQRGTARMDIRLAKTLVLILVVLIICWGPLLAIMVYDVFGKMNKLIKTVFAFCSMLCLLNSTVNPIIYALRSKDLRHAFRSM +>6i8m +KSKLAKPIQDLIKMIFDVESMKKAMVEFEIDLQKMPLGKLSKRQIQSAYSILNEVQQAVSDGGSESQILDLSNRFYTLIPHDFGMKKPPLLSNLEYIQAKVQMLDNLLDIEVAYSLLRGGNEDGDKDPIDINYEKLRTDIKVVDKDSEEAKIIKQYVKNTHAATHNAYDLKVVEIFRIEREGESQRYKPFKQLHNRQLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQADPIGLILLGEVALGNMYELKNASHITKLPKGKHSVKGLGKTAPDPTATTTLDGVEVPLGNGISTGINDTCLLYNEYIVYDVAQVNLKYLLKLKFNY +>6oxs +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6cjj +QGETHEFTAEISQLMSLIINTVYSNKEIFLRELISNASDALDKIRYQALSDPSQLESEPELFIRIIPQKDQKVLEIRDSGIGMTKADLVNNLGTIAKSGTKSFMEALSAGADVSMIGQFGVGFYSLFLVADHVQVISKHNDDEQYVWESNAGGKFTVTLDETNERLGRGTMLRLFLKEDQLEYLEEKRIKEVVKKHSEFVAYPIQLVVTKEVEK +>6ffg +MGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD +>6a73 +ENQYYNSKALKEDDPKAALSSFQKVLELEGEKGEWGFKALKQMIKINFKLTNFPEMMNRYKQLLTYIRSAVTRNYSEKSINSILDYISTSKQMDLLQEFYETTLEALKDAKNDRLWFKTNTKLGKLYLERNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAY +>6a1c +SGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMG +>6oxu +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6qtw +RYSRLRVIAEIRNIVSSIEFDRDDELFATAGVSRCIKVFDFSSVVNEPQCPIVEMSTRSKLSCLSWNKHEKNHIASSDYEGIVTVWDVTTRQSLMEYEEHEKRAWSVDFSRTEPSMLVSGSDDCKVKVWTRQEASVINIDMKANICVKYNPGSSNYIAVGSADHHIHYYDLRNISQPLHVFSGHKKAVSYVKFLSNNELASASTDSTLRLWDVKDNLPVRTFRGHTNEKNFVGLTVNSEYLACGSETNEVYVYHKEITRPVTSHRFSYFISAVCWKSDSPTMLTANSQGTIKVLVLAA +>6np4 +TPWSKSELVRQLRDLGVRSGDMVMPHVSLRAVGPLADGPQTLVDALIEAVGPTGNILAFVSWRDSPYEQTLGHDAPPAAIAQSWPAFDPDHAPAYPGFGAINEFIRTYPGCRRSAHPDASMAAIGPDAAWLVAPHEMGAAYGPRSPIARFLAHAGKILSIGAGPDAVTALHYAEAVARIEGKRRVTYSMPLLREGKRVWVTTSDWDSNGILDEYAAPDGPDAVERIARDYLARTRVAQGPVGGAQSRLIDAADIVSFGIEWLEARHAA +>6hv2 +EYNVRTLKWSKMNLTYRIVNYTPDMTHSEVEKAFKKAFKVWSDVTPLNFTRLHDGIADIMISFGIKEHGDFYPFDGPSGLLAHAFPPGPNYGGDAHFDDDETWTSSSKGYNLFLVAAHEFGHSLGLDHSKDPGALMFPIYTYTGKSHFMLPDDDVQGIQSLYGPGD +>6n55 +GEPFLIGVSGGTASGKSSVCAKIVQLLGQNDYRQKQVVILSQDSFYRVLTSEQKAKALKGQFNFDHPDAFDNELILKTLKEITEGKTVQIPVYDFVSHSRKEETVTVYPADVVLFEGILAFYSQEVRDLFQMKLFVDTDADTRLSRRVLRDISERGRDLEQILSQYITFVKPAFEEFCLPTKKYADVIIPRGADNLVAINLIVQHIQDILNG +>6e3o +AMEDFVRQCFNPMIVELAEKAMKEYGEDPKIETNKFAAICTHLEVCFMYSDFGLKHRFEIIEGRDRIMAWTVVNSICNTTGVEKPKFLPDLYDYKENRFIEIGVTRREVHIYYLEKANKIEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRSLWDSFRQSE +>6sfc +SFKCVNSLKEDHNQPLFGVQFNWHSKEGDPLVFATVGSNRVTLYECHSQGEIRLLQSYVDADADENFYTCAWTYDSNTSHPLLAVAGSRGIIRIINPITMQCIKHYVGHGNAINELKFHPRDPNLLLSVSKDHALRLWNIQTDTLVAIFGGVEGHRDEVLSADYDLLGEKIMSCGMDHSLKLWRINSKRMMNAIKESYDRPFISQKIHFPDFSTRDIHRNYVDCVRWLGDLILSKSCENAIVCWKPGKMEDDIDKIKPSESNVTILGRFDYSQCDIWYMRFSMDFWQKMLALGNQVGKLYVWDLECTTLTHHKCGAAIRQTSFSRDSSILIAVCDDASIWRWDR +>6qi7 +VTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPYRVYVEELKPTGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDENKVLVLDTDYKKYLLFCMENSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI +>6k04 +SEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMP +>6n8x +EVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKE +>6np3 +TPWSKSELVRQLRDLGVRSGDMVMPHVSLRAVGPLADGPQTLVDALIEAVGPTGNILAFVSWRDSPYEQTLGHDAPPAAIAQSWPAFDPDHAPAYPGFGAINEFIRTYPGCRRSAHPDASMAAIGPDAAWLVAPHEMGAAYGPRSPIARFLAHAGKILSIGAGPDAVTALHYAEAVARIEGKRRVTYSMPLLREGKRVWVTTSDWDSNGILDEYAAPDGPDAVERIARDYLARTRVAQGPVGGAQSRLIDAADIVSFGIEWLEARHAA +>6pgo +MTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETSLLDILDTAGRDQYMRTGEGFLLVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKSDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHK +>6i75 +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6i62 +YNKIVSHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLSFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNIKLEGKVPMHKLFLEMLEAK +>6g29 +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYIDKQ +>6mo9 +RVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQEL +>5zml +EVANPEHYIKHPLQNRWALWFFKNDKSKTWQANLRLISKFDTVEDFWALYNHIQLSSNLMPGCDYSLFKDGIEPMWEDEKNKRGGRWLITLNKQQRRSDLDRFWLETLLCLIGESFDDYSDDVCGAVVNVRAKGDKIAIWTTECENREAVTHIGRVYKERLGLPPKIVIGYQSHADTATKSGSTTKNRFVV +>6i65 +YNKIVSHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLSFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNIKLEGKVPMHKLFLEMLEAK +>6e7m +TRDQNGTWEMESNENFEGYMKALDIDFCTRKIAVRLTQTKVIDQDGDNFKDKTTSTFRNYDVDFTVGVEFDEYTKSLDNRHVKALVTWEGDVLVCVQKGEKENRGWKKWIEGDKLYLELTCGDQVCRQVFKKK +>6i61 +YNKIVSHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLSFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNIKLEGKVPMHKLFLEMLEAK +>6qtm +LSKSSWRQEWLANLKLISVSLVDEFPSELSDSDRQIINEKQLLKDIFANNLKSAISNNFRESDIIILKGEIEDYPSSEIKIYYNELQNKKARFWSFKTQRFVSNGFDI +>6qlo +PLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI +>6oie +MDPIPICSFCLGTKESNREKKPEELLSCADCGSSGHPSCLKFCPELTTNVKALRWQCIECKTCSACRVQGRNADNMLFCDSCDRGFHMECCDPPLSRMPKGMWICQVCRPK +>6nrf +KSKLPKPVQDLIKMIFGDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFK +>6gj5 +MTEYKLVVVGADGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKSDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHK +>6d3y +SFDCGKPQVEPKKCPVVGGCVAHPHSWPWQVSLRTRFGMHFCGGTLISPEWVLTAAHCLEKSPRPSSYKVILGAHQEVNLEPHVQEIEVSRLFLEPTRKDIALLKLSSPAVITDKVIPACLPSPNYVVADRTECFITGWGETQGTFGAGLLKEAQLPVIENKVCNRYEFLNGRVQSTELCAGHLAGGTDSCQGDSGGPLVCFEKDKYILQGVTSWGLGCARPNKPGVYVRVSRFVTWIEGVMRNN +>6oir +KYVDKIHIGNYEIDAWYFSPFPEDYGKQPKLWLCEYCLKYMKYEKSYRFHLGQCQWRQPPGKEIYRKSNISVHEVDGKDHKIYCQNLCLLAKLFLDHTLYFDVEPFVFYILTEVDRQGAHIVGYFSKEKESPDGNNVSCIMILPPYQRRGYGRFLIAFSYELSKLESTVGSPEKPLSDLGKLSYRSYWSWVLLENLRDFRGTLSIKDLSQMTSITQNDIISTLQSLNMVKYWKGQHVICVTPKLVEEHLKSAQYKKPPITVDSVCLKWAPP +>6d40 +PSFDCGKPQVEPKKCPVVGGCVAHPHSWPWQVSLRTRFGMHFCGGTLISPEWVLTAAHCLEKSPRPSSYKVILGAHQEVNLEPHVQEIEVSRLFLEPTRKDIALLKLSSPAVITDKVIPACLPSPNYVVADRTECFITGWGETQGTFGAGLLKEAQLPVIENKVCNRYEFLNGRVQSTELCAGHLAGGTDSCQGDSGGPLVCFEKDKYILQGVTSWGLGCARPNKPGVYVRVSRFVTWIEGVMRNN +>6e6j +HMEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMP +>6i7a +KVVKFSYMWTINNFSFCREEMGEVIKSSTFSSNDKLKWCLRVNPKGLDEESKDYLSLYLLLVSCPKSEVRAKFKFSILNAKGEETKAMESQRAYRFVQGKDWGFKKFIRRNFLLDEANGLLPDDKLTLFCEVSVVQD +>6g25 +VKFQVGDLVWSKVGTYPWWPCMVSSDPQLEVHTKINTRGAREYHVQFFSNQPERAWVHEKRVREYKGHKQYEELLAEAQKIRKPRPQRERAQWDIGIAHAEKALKMTREERIEQYTFIYID +>6oin +KYVDKIHIGNYEIDAWYFSPFPEDYGKQPKLWLCEYCLKYMKYEKSYRFHLGQCQWRQPPGKEIYRKSNISVHEVDGKDHKIYCQNLCLLAKLFLDHTLYFDVEPFVFYILTEVDRQGAHIVGYFSKEKESPDGNNVSCIMILPPYQRRGYGRFLIAFSYELSKLESTVGSPEKPLSDLGKLSYRSYWSWVLLENLRDFRGTLSIKDLSQMTSITQNDIISTLQSLNMVKYWKGQHVICVTPKLVEEHLKSAQYKKPPITVDSVCLKWAPP +>6oxz +PQITLWKRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF +>6hop +GPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKD +>6nrj +KSKLPKPVQDLIKMIFGDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFK +>6op9 +VLARIFKETELRKLKVLGSGVFGTVHKGVWIPEGESIKIPVCIKVIEDKSGRQSFQAVTDHMLAIGSLDHAHIVRLLGLCPGSSLQLVTQYLPLGSLLDHVRQHRGALGPQLLLNWGVQIAKGMYYLEEHGMVHRNLAARNVLLKSPSQVQVADFGVADLLPPDDKQAKTPIKWMALESIHFGKYTHQSDVWSYGVTVWELMTFGAEPYAGLRLAEVPDLLEKGERLAQPQICTIDVYMVMVKCWMIDENIRPTFKELANEFTRMARDPPRYLVIK +>6pno +GESARSLGKGSAPPGPVPEGSIRIYSMRFCPFAERTRLVLKAKGIRHEVININLKNKPEWFFKKNPFGLVPVLENSQGQLIYESAITCEYLDEAYPGKKLLPDDPYEKACQKMILELFSKVPSLVGSFIRSQNKEDYAGLKEEFRKEFTKLEEVLTNKKTTFFGGNSISMIDYLIWPWFERLEAMKLNECVDHTPKLKLWMAAMKEDPTVSALLTSEKDWQGFLELYLQNSPEACDYGL +>6a87 +APVPVTKLVCDGDTYKCTAYLDYGDGKWVAQWDTAVFHT +>6oim +GMTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETSLLDILDTAGQEEYSAMRDQYMRTGEGFLLVFAINNTKSFEDIHHYREQIKRVKDVPMVLVGNKSDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEK +>6cjp +LYFQGETHEFTAEISQLMSLIINTVYSNKEIFLRELISNASDALDKIRYQALSDPSQLESEPELFIRIIPQKDQKVLEIRDSGIGMTKADLVNNLGTIAKSGTKSFMEALSAGADVSMIGQFGVGFYSLFLVADHVQVISKHNDDEQYVWESNGKFTVTLDETNERLGRGTMLRLFLKEDQLEYLEEKRIKEVVKKHSEFVAYPIQLVVTKEVE +>6pyb +PAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAKISASAPTSLRSC +>6mhc 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+FDCGKPQVEPKKCPVVGGCVAHPHSWPWQVSLRTRFGMHFCGGTLISPEWVLTAAHCLEKSPRPSSYKVILGAHQEVNLEPHVQEIEVSRLFLEPTRKDIALLKLSSPAVITDKVIPACLPSPNYVVADRTECFITGWGETQGTFGAGLLKEAQLPVIENKVCNRYEFLNGRVQSTELCAGHLAGGTDSCQGDSGGPLVCFEKDKYILQGVTSWGLGCARPNKPGVYVRVSRFVTWIEGVMRNN +>6gj8 +GMTEYKLVVVGADGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKE +>6mo2 +MADLELERAADVRWEEQAEISGSSLEDGAYRIKQKGILGYSQIGAGVYKEGTFHTMWHVTRGAVLMHKGKRIEPSWADVKKDLISYGGGWKLEGEWKEGEEVQVLALEPGKNPRAVQTKPGLFKTNTGTIGAVSLDFSPGTSGSPIVDKKGKVVGLYGIANTEK \ No newline at end of file diff --git a/test/README.md b/test/README.md deleted file mode 100644 index a91bbc4..0000000 --- a/test/README.md +++ /dev/null @@ -1,4 +0,0 @@ -### Test Cases: - -- predict_batch_seq: predict batch of sequences only without TM alignment comparison - \ No newline at end of file diff --git a/test/predict.py b/test/predict.py deleted file mode 100644 index 91fd5ec..0000000 --- a/test/predict.py +++ /dev/null @@ -1,205 +0,0 @@ -import dataclasses -import itertools -import warnings -import os -from argparse import ArgumentParser -from operator import itemgetter -from pathlib import Path -from typing import Tuple - -import numpy as np -import pytorch_lightning as pl -import torch -from rdkit import Chem -from torch.utils.data import DataLoader - -from dpl.data import RepeatDataset, collate_fn, ligand_to_data, protein_to_data -from dpl.model import ProteinReDiffModel ## (NN) -from dpl.mol import get_mol_positions, mol_from_file, update_mol_positions -from dpl.protein import ( - RESIDUE_TYPES, - RESIDUE_TYPE_INDEX, - Protein, - protein_from_pdb_file, - protein_from_sequence, - proteins_to_pdb_file, -) -from dpl.tmalign import run_tmalign - -RESIDUE_TYPES_MASK = RESIDUE_TYPES + [""] -def compute_residue_esm(protein: Protein) -> torch.Tensor: - esm_model, esm_alphabet = torch.hub.load( - "facebookresearch/esm:main", "esm2_t33_650M_UR50D" - ) - esm_model.cuda().eval() - esm_batch_converter = esm_alphabet.get_batch_converter() - - data = [] - for chain, _ in itertools.groupby(protein.chain_index): - sequence = "".join( - [RESIDUE_TYPES_MASK[aa] for aa in protein.aatype[protein.chain_index == chain]] - ) - data.append(("", sequence)) - batch_tokens = esm_batch_converter(data)[2].cuda() - with torch.inference_mode(): - results = esm_model(batch_tokens, repr_layers=[esm_model.num_layers]) - token_representations = results["representations"][esm_model.num_layers].cpu() - residue_representations = [] - for i, (_, sequence) in enumerate(data): - residue_representations.append(token_representations[i, 1 : len(protein.aatype) + 1]) - residue_esm = torch.cat(residue_representations, dim=0) - assert residue_esm.size(0) == len(protein.aatype) - return residue_esm - - -def update_pos( - protein: Protein, ligand: Chem.Mol, pos: np.ndarray -) -> Tuple[Protein, Chem.Mol]: - atom_pos = np.zeros_like(protein.atom_pos) - atom_pos[:, 1] = pos[ligand.GetNumAtoms() :] - atom_mask = np.zeros_like(protein.atom_mask) - atom_mask[:, 1] = 1.0 - protein = dataclasses.replace(protein, atom_pos=atom_pos, atom_mask=atom_mask) - ligand = update_mol_positions(ligand, pos[: ligand.GetNumAtoms()]) - return protein, ligand - -def predict_seq( - proba: torch.Tensor -) -> list : - tokens = torch.argmax(torch.softmax((torch.tensor(proba)), dim = -1), dim = -1) - RESIDUE_TYPES_NEW = ["X"] + RESIDUE_TYPES - return list(map(lambda i : RESIDUE_TYPES_NEW[i], tokens)) - -def update_seq( - protein: Protein, proba: torch.Tensor -) -> Protein: - tokens = torch.argmax(torch.softmax((torch.tensor(proba)), dim = -1), dim = -1) - RESIDUE_TYPES_NEW = ["X"] + RESIDUE_TYPES - sequence = "".join(map(lambda i : RESIDUE_TYPES_NEW[i], tokens)).lstrip("X").rstrip("X") - aatype = np.array([RESIDUE_TYPES.index(s) for s in sequence], dtype=np.int64) - protein = dataclasses.replace(protein, aatype = aatype) - return protein - - -def main(args): - pl.seed_everything(args.seed, workers=True) - # Check if the directory exists - if os.path.exists(args.output_dir): - # Remove the existing directory - os.rmdir(args.output_dir) - args.output_dir.mkdir(parents=True) - - # Model - model = ProteinReDiffModel.load_from_checkpoint( - args.ckpt_path, num_steps=args.num_steps - ) - ## (NN) - model.training_mode = False - args.num_gpus = 1 - model.mask_prob = args.mask_prob - ## (NN) - - # Inputs - if args.protein.endswith(".pdb"): - protein = protein_from_pdb_file(args.protein) - else: - - protein = protein_from_sequence(args.protein) - - if args.ligand.endswith(".sdf") or args.ligand.endswith(".mol2"): - ligand = mol_from_file(args.ligand) - else: - ligand = Chem.MolFromSmiles(args.ligand) - ligand = update_mol_positions(ligand, np.zeros((ligand.GetNumAtoms(), 3))) - - total_num_atoms = len(protein.aatype) + ligand.GetNumAtoms() - print(f"Total number of atoms: {total_num_atoms}") - if total_num_atoms > 384: - warnings.warn("Too many atoms. May take a long time for sample generation.") - - data = { - **ligand_to_data(ligand), - **protein_to_data(protein, residue_esm=compute_residue_esm(protein)), - } - - ref_protein = protein_from_pdb_file(args.ref_path) if args.ref_path else None - - # Generate samples - trainer = pl.Trainer.from_argparse_args( - args, - accelerator="auto", - gpus = args.num_gpus, - default_root_dir=args.output_dir, - max_epochs=-1, - ) - results = trainer.predict( ## (NN) - model, - dataloaders=DataLoader( - RepeatDataset(data, args.num_samples), - batch_size=args.batch_size, - num_workers=args.num_workers, - collate_fn=collate_fn, - ), - ) - - positions = [p[0] for p in results] ## (NN) - sequences = [s[1] for s in results] ## (NN) - - positions = torch.cat(positions, dim=0).detach().cpu().numpy() - probabilities = torch.cat(sequences, dim=0).detach().cpu().numpy() #(NN) - torch.save(probabilities, "sampled_seq_gvp.pt") # (NN) - - # Save samples - sample_proteins, sample_ligands = [], [] - tmscores = [] - for pos, seq_prob in zip(positions, probabilities): - sample_protein, sample_ligand = update_pos(protein, ligand, pos) - sample_protein = update_seq(sample_protein, seq_prob) - if ref_protein is None: - warnings.warn( - "Using the first sample as a reference. The resulting structures may be mirror images." - ) - ref_protein = sample_protein - tmscore, t, R = max( - run_tmalign(sample_protein, ref_protein), - run_tmalign(sample_protein, ref_protein, mirror=True), - key=itemgetter(0), - ) - sample_proteins.append( - dataclasses.replace( - sample_protein, atom_pos=t + sample_protein.atom_pos @ R - ) - ) - sample_ligands.append( - update_mol_positions( - sample_ligand, t + get_mol_positions(sample_ligand) @ R - ) - ) - tmscores.append(tmscore) - proteins_to_pdb_file(sample_proteins, args.output_dir / "sample_protein.pdb") - with Chem.SDWriter(str(args.output_dir / "sample_ligand.sdf")) as w: - for sample_ligand in sample_ligands: - w.write(sample_ligand) - with open(args.output_dir / "sample_tmscores.txt", "w") as f: - for tmscore in tmscores: - f.write(str(tmscore) + "\n") - - -if __name__ == "__main__": - parser = ArgumentParser() - - parser.add_argument("--seed", type=int, default=1234) - parser.add_argument("--batch_size", type=int, default=1) - parser.add_argument("--num_workers", type=int, default=2) - parser.add_argument("--num_steps", type=int, default=64) - parser.add_argument("--mask_prob", type=float, default=0.3) - parser.add_argument("--training_mode", action="store_true") - parser.add_argument("-c", "--ckpt_path", type=Path, required=True) - parser.add_argument("-o", "--output_dir", type=Path, required=True) - parser.add_argument("-p", "--protein", type=str, required=True) - parser.add_argument("-l", "--ligand", type=str, required=True) - parser.add_argument("-n", "--num_samples", type=int, required=True) - parser.add_argument("-r", "--ref_path", type=Path) - args = parser.parse_args() - - main(args) diff --git a/test/predict_batch.py b/test/predict_batch.py deleted file mode 100644 index a44af7a..0000000 --- a/test/predict_batch.py +++ /dev/null @@ -1,220 +0,0 @@ -import dataclasses -import itertools -import warnings -import os -from argparse import ArgumentParser -from operator import itemgetter -from pathlib import Path -from typing import Iterable, List, Union, Tuple - -import numpy as np -import pytorch_lightning as pl -import torch -from rdkit import Chem -from torch.utils.data import DataLoader - -from dpl.data import RepeatDataset, collate_fn, ligand_to_data, protein_to_data -from dpl.model import ProteinReDiffModel ## (NN) -from dpl.mol import get_mol_positions, mol_from_file, update_mol_positions -from dpl.protein import ( - RESIDUE_TYPES, - RESIDUE_TYPE_INDEX, - Protein, - protein_from_pdb_file, - protein_from_sequence, - proteins_to_pdb_file, -) -from dpl.tmalign import run_tmalign - -RESIDUE_TYPES_MASK = RESIDUE_TYPES + [""] -def compute_residue_esm(protein: Protein) -> torch.Tensor: - esm_model, esm_alphabet = torch.hub.load( - "facebookresearch/esm:main", "esm2_t33_650M_UR50D" - ) - esm_model.cuda().eval() - esm_batch_converter = esm_alphabet.get_batch_converter() - - data = [] - for chain, _ in itertools.groupby(protein.chain_index): - sequence = "".join( - [RESIDUE_TYPES_MASK[aa] for aa in protein.aatype[protein.chain_index == chain]] - ) - data.append(("", sequence)) - batch_tokens = esm_batch_converter(data)[2].cuda() - with torch.inference_mode(): - results = esm_model(batch_tokens, repr_layers=[esm_model.num_layers]) - token_representations = results["representations"][esm_model.num_layers].cpu() - residue_representations = [] - for i, (_, sequence) in enumerate(data): - residue_representations.append(token_representations[i, 1 : len(protein.aatype) + 1]) - residue_esm = torch.cat(residue_representations, dim=0) - assert residue_esm.size(0) == len(protein.aatype) - return residue_esm - -def proteins_from_fasta(fasta_file: Union[str, Path]): - names = [] - proteins = [] - with open(fasta_file, "r") as f: - for line in f: - if line.startswith(">"): - name = line.lstrip(">").replace(" ","_") - names.append(name) - else: - sequence = line.rstrip("\n") - protein = protein_from_sequence(sequence) - proteins.append(protein) - - return proteins, names - - -def update_pos( - protein: Protein, ligand: Chem.Mol, pos: np.ndarray -) -> Tuple[Protein, Chem.Mol]: - atom_pos = np.zeros_like(protein.atom_pos) - atom_pos[:, 1] = pos[ligand.GetNumAtoms() :] - atom_mask = np.zeros_like(protein.atom_mask) - atom_mask[:, 1] = 1.0 - protein = dataclasses.replace(protein, atom_pos=atom_pos, atom_mask=atom_mask) - ligand = update_mol_positions(ligand, pos[: ligand.GetNumAtoms()]) - return protein, ligand - -def predict_seq( - proba: torch.Tensor -) -> list : - tokens = torch.argmax(torch.softmax((torch.tensor(proba)), dim = -1), dim = -1) - RESIDUE_TYPES_NEW = ["X"] + RESIDUE_TYPES - return list(map(lambda i : RESIDUE_TYPES_NEW[i], tokens)) - -def update_seq( - protein: Protein, proba: torch.Tensor -) -> Protein: - tokens = torch.argmax(torch.softmax((torch.tensor(proba)), dim = -1), dim = -1) - RESIDUE_TYPES_NEW = ["X"] + RESIDUE_TYPES - sequence = "".join(map(lambda i : RESIDUE_TYPES_NEW[i], tokens)).lstrip("X").rstrip("X") - aatype = np.array([RESIDUE_TYPES.index(s) for s in sequence], dtype=np.int64) - protein = dataclasses.replace(protein, aatype = aatype) - return protein - - -def main(args): - pl.seed_everything(args.seed, workers=True) - # Check if the directory exists - if os.path.exists(args.output_dir): - # Remove the existing directory - os.rmdir(args.output_dir) - args.output_dir.mkdir(parents=True) - - # Model - model = ProteinReDiffModel.load_from_checkpoint( - args.ckpt_path, num_steps=args.num_steps - ) - ## (NN) - model.training_mode = False - args.num_gpus = 1 - model.mask_prob = args.mask_prob - ## (NN) - - # Inputs - if args.protein.endswith(".pdb"): - protein = protein_from_pdb_file(args.protein) - else: - - protein = protein_from_sequence(args.protein) - - if args.ligand.endswith(".sdf") or args.ligand.endswith(".mol2"): - ligand = mol_from_file(args.ligand) - else: - ligand = Chem.MolFromSmiles(args.ligand) - ligand = update_mol_positions(ligand, np.zeros((ligand.GetNumAtoms(), 3))) - - total_num_atoms = len(protein.aatype) + ligand.GetNumAtoms() - print(f"Total number of atoms: {total_num_atoms}") - if total_num_atoms > 384: - warnings.warn("Too many atoms. May take a long time for sample generation.") - - data = { - **ligand_to_data(ligand), - **protein_to_data(protein, residue_esm=compute_residue_esm(protein)), - } - - ref_protein = protein_from_pdb_file(args.ref_path) if args.ref_path else None - - # Generate samples - trainer = pl.Trainer.from_argparse_args( - args, - accelerator="auto", - gpus = args.num_gpus, - default_root_dir=args.output_dir, - max_epochs=-1, - ) - results = trainer.predict( ## (NN) - model, - dataloaders=DataLoader( - RepeatDataset(data, args.num_samples), - batch_size=args.batch_size, - num_workers=args.num_workers, - collate_fn=collate_fn, - ), - ) - - positions = [p[0] for p in results] ## (NN) - sequences = [s[1] for s in results] ## (NN) - - positions = torch.cat(positions, dim=0).detach().cpu().numpy() - probabilities = torch.cat(sequences, dim=0).detach().cpu().numpy() #(NN) - torch.save(probabilities, "sampled_seq_gvp.pt") # (NN) - - # Save samples - sample_proteins, sample_ligands = [], [] - tmscores = [] - for pos, seq_prob in zip(positions, probabilities): - sample_protein, sample_ligand = update_pos(protein, ligand, pos) - sample_protein = update_seq(sample_protein, seq_prob) - if ref_protein is None: - warnings.warn( - "Using the first sample as a reference. The resulting structures may be mirror images." - ) - ref_protein = sample_protein - tmscore, t, R = max( - run_tmalign(sample_protein, ref_protein), - run_tmalign(sample_protein, ref_protein, mirror=True), - key=itemgetter(0), - ) - sample_proteins.append( - dataclasses.replace( - sample_protein, atom_pos=t + sample_protein.atom_pos @ R - ) - ) - sample_ligands.append( - update_mol_positions( - sample_ligand, t + get_mol_positions(sample_ligand) @ R - ) - ) - tmscores.append(tmscore) - proteins_to_pdb_file(sample_proteins, args.output_dir / "sample_protein.pdb") - with Chem.SDWriter(str(args.output_dir / "sample_ligand.sdf")) as w: - for sample_ligand in sample_ligands: - w.write(sample_ligand) - with open(args.output_dir / "sample_tmscores.txt", "w") as f: - for tmscore in tmscores: - f.write(str(tmscore) + "\n") - - -if __name__ == "__main__": - parser = ArgumentParser() - - parser.add_argument("--seed", type=int, default=1234) - parser.add_argument("--batch_size", type=int, default=1) - parser.add_argument("--num_workers", type=int, default=-1) - parser.add_argument("--num_steps", type=int, default=64) - parser.add_argument("--mask_prob", type=float, default=0.3) - parser.add_argument("--training_mode", action="store_true") - parser.add_argument("-c", "--ckpt_path", type=Path, required=True) - parser.add_argument("-o", "--output_dir", type=Path, required=True) - parser.add_argument("-p", "--protein", type=str, required=True) - parser.add_argument("-l", "--ligand", type=str, required=True) - parser.add_argument("-n", "--num_samples", type=int, required=True) - parser.add_argument("-r", "--ref_path", type=Path) - args = parser.parse_args() - - main(args) diff --git a/test/test_cases.fasta b/test/test_cases.fasta deleted file mode 100644 index 928cece..0000000 --- a/test/test_cases.fasta +++ /dev/null @@ -1,8 +0,0 @@ ->seq1 -GMIRVMATGVFDILHLGHIHYLKESKKLGDELVVVVARDSTARNNGKIPIFDENSRLALISELKVVDRAILGHEGDMMKTVIEVKPDIITLGYDQKFDEAELQSKINKLGITVKIVRISKYDGQLNSSSSVRKKIMELIGERY ->seq2 -MEFQRVHQQLLQSHHLFEPLSPVQLQELLASSDLVNLDKGAYVFRQGEPAHAFYYLISGCVKIYRLTPEGQEKILEVTNERNTFAEAMMFMDTPNYVATAQAVVPSQLFRFSNKAYLRQLQDNTPLALALLAKLSTRLHQRIDEIETLSLKNATHRVVRYLLTLAAHAPGENCRVEIPVAKQLVAGHLSIQPETFSRIMHRLGDEGIIHLDGREISILDRERLECFE ->seq3 -MEFQRVHQXLLQSXXLFEPLSPXQLXEXLASSDLVNLDXGXYVFXQXEPAHAFYYLISGCVKXYRLTPEGXEKIXXVXNEXNTFAEAXMFMDXPNXVATAQAVVPSQXXRFSNKAXLRQXQDNTXLALXLLAKLSTRLHQRIDEXETLXLKNXTHRVXRXLLTLXAXAPGENCRVEXXVXKQLVAGXXXIQPETFXRIXHRLGDXGIIXLDGREXSIXDRERLXCFE ->seq4 -AGEDVGAPPDHLWVHQEGIYRDEYQRTWVAVVEEETSFLRARVQQIQVPLGDAARPSHLLTSQLPLMWQLYPEERYMDNNSRLWQIQHHLMVRGVQELLLKLLPDD \ No newline at end of file diff --git a/test/test_predict_batch.sh b/test/test_predict_batch.sh deleted file mode 100644 index da36894..0000000 --- a/test/test_predict_batch.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=generate-dpl # job name -#SBATCH --output=dpl-inference-%N.out # output log file -#SBATCH --time=04:00:00 # 1 hour of wall time -#SBATCH --nodes=1 # 1 GPU node -#SBATCH --ntasks-per-node=10 -#SBATCH --partition=gpu -#SBATCH --account=pi-roux -#SBATCH --gres=gpu:1 # Request 1 GPU per node - - -source ~/.bashrc -conda activate /scratch/midway3/ndn/dpl - - -save_dir="/scratch/beagle3/ndn/DiffusionProteinLigand/inference/example_beta_encoding/${SLURM_JOB_ID}" - -python -m test.predict_batch_seq \ - --ckpt_path "/scratch/beagle3/ndn/DiffusionProteinLigand/beta_train/retrain/10181134/lightning_logs/version_10181134/checkpoints/epoch=328-val_loss=1.20.ckpt"\ - --output_dir "$save_dir" \ - --fasta "./test/test_cases.fasta" \ - --num_samples 2 \ - --num_steps 1000 \ No newline at end of file