FlashABB: modelling antibody structures at the speed of language
Installation:
pip install flash-abb
Or from source:
git clone git@github.com:oxpig/FlashABB.git
cd FlashABB
pip install .
Structure prediction
The following is also in example.py and can be used to create the structures in sample_preds.
from flash_abb import pretrained
import torch
flabb = pretrained(device='cuda')
# Sequences in heavy|light format
seqs = [
'EVQLLESGGEVKKPGASVKVSCRASGYTFRNYGLTWVRQAPGQGLEWMGWISAYNGNTNYAQKFQGRVTLTTDTSTSTAYMELRSLRSDDTAVYFCARDVPGHGAAFMDVWGTGTTVTVSS|DIQLTQSPLSLPVTLGQPASISCRSSQSLEASDTNIYLSWFQQRPGQSPRRLIYKISNRDSGVPDRFSGSGSGTHFTLRISRVEADDVAVYYCMQGTHWPPAFGQGTKVDIK',
'EVQLLESGGEVKKPGASVKVSCRASGYTFRNYGLTWVRQAPGQGLEWMGWISAYNGNTNYAQKFQGRVTLTTDTSTSTAYMELRSLRSDDTAVYFCARDVPGHGAAFMDVWGTGTTVTVS|DIQLTQSPLSLPVTLGQPASISCRSSQSLEASDTNIYLSWFQQRPGQSPRRLIYKISNRDSGVPDRFSGSGSGTHFTLRISRVEADDVAVYYCMQGTHWPPAFGQGTKVDIK',
]
with torch.no_grad():
result = flabb(seqs)
print(result.coords.shape) # (2, n_residues, 37, 3)
print(result.bb_coords.shape) # (2, n_residues, 4, 3)
result.to_pdbs(['ab1', 'ab2'], pdb_dir='sample_preds')
Developability scoring (FlashTAP)
FlashTAP predicts four TAP developability scores: PSH, PPC, PNC, and SFvCSP.
from flash_abb import pretrained_tap
tap = pretrained_tap(device='cuda')
seqs = [
'EVQLLESGGEVKKPGASVKVSCRASGYTFRNYGLTWVRQAPGQGLEWMGWISAYNGNTNYAQKFQGRVTLTTDTSTSTAYMELRSLRSDDTAVYFCARDVPGHGAAFMDVWGTGTTVTVSS|DIQLTQSPLSLPVTLGQPASISCRSSQSLEASDTNIYLSWFQQRPGQSPRRLIYKISNRDSGVPDRFSGSGSGTHFTLRISRVEADDVAVYYCMQGTHWPPAFGQGTKVDIK',
]
result = tap(seqs)
print(result.scores) # [{'PSH': ..., 'PPC': ..., 'PNC': ..., 'SFvCSP': ...}]
print(result.tensor) # (1, 4) raw score tensor
print(result.flag_probs) # [{'PSH': 0.12, 'PPC': 0.03, 'PNC': 0.05, 'SFvCSP': 0.41}]
print(result.any_flag_prob) # [0.47]
Structure-aware embeddings (FlashABB-SSS)
FlashABB-SSS (seq2struct2seq) produces per-residue embeddings that combine sequence and predicted 3D structure. These can be used as features for downstream tasks.
from flash_abb import pretrained_sss
sss = pretrained_sss(device='cuda')
seqs = [
'EVQLLESGGEVKKPGASVKVSCRASGYTFRNYGLTWVRQAPGQGLEWMGWISAYNGNTNYAQKFQGRVTLTTDTSTSTAYMELRSLRSDDTAVYFCARDVPGHGAAFMDVWGTGTTVTVSS|DIQLTQSPLSLPVTLGQPASISCRSSQSLEASDTNIYLSWFQQRPGQSPRRLIYKISNRDSGVPDRFSGSGSGTHFTLRISRVEADDVAVYYCMQGTHWPPAFGQGTKVDIK',
]
result = sss(seqs)
print(result.embeddings.shape) # (1, n_residues, 128)
print(result.mask.shape) # (1, n_residues)
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