Sapiens: Human antibody language model
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Sapiens is a human antibody language model based on BERT.
Learn more in the Sapiens, OASis and BioPhi in our publication:
David Prihoda, Jad Maamary, Andrew Waight, Veronica Juan, Laurence Fayadat-Dilman, Daniel Svozil & Danny A. Bitton (2022) BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning, mAbs, 14:1, DOI: https://doi.org/10.1080/19420862.2021.2020203
For more information about BioPhi, see the BioPhi repository
Features
- Infilling missing residues in human antibody sequences
- Suggesting mutations (in frameworks as well as CDRs)
- Creating vector representations (embeddings) of residues or sequences
Usage
Install Sapiens using pip:
# Recommended: Create dedicated conda environment
conda create -n sapiens python=3.8
conda activate sapiens
# Install Sapiens
pip install sapiensAntibody sequence infilling
Positions marked with * or X will be infilled with the most likely human residues, given the rest of the sequence
import sapiens
best = sapiens.predict_masked(
'**QLV*SGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS',
'H'
)
print(best)
# QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSSSuggesting mutations
Return residue scores for a given sequence:
import sapiens
scores = sapiens.predict_scores(
'**QLV*SGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS',
'H'
)
scores.head()
# A C D E ...
# 0 0.003272 0.004147 0.004011 0.004590 ... <- based on masked input
# 1 0.012038 0.003854 0.006803 0.008174 ... <- based on masked input
# 2 0.003384 0.003895 0.003726 0.004068 ... <- based on Q input
# 3 0.004612 0.005325 0.004443 0.004641 ... <- based on L input
# 4 0.005519 0.003664 0.003555 0.005269 ... <- based on V input
#
# Scores are given both for residues that are masked and that are present.
# When inputting a non-human antibody sequence, the output scores can be used for humanization.Antibody sequence embedding
Get a vector representation of each position in a sequence
import sapiens
residue_embed = sapiens.predict_residue_embedding(
'QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS',
'H',
layer=None
)
residue_embed.shape
# (layer, position in sequence, features)
# (5, 119, 128)Get a single vector for each sequence
seq_embed = sapiens.predict_sequence_embedding(
'QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS',
'H',
layer=None
)
seq_embed.shape
# (layer, features)
# (5, 128)Notebooks
Try out Sapiens in your browser using these example notebooks:
| Links | Notebook | Description |
|---|---|---|
|
01_sapiens_antibody_infilling | Predict missing positions in an antibody sequence |
|
|
02_sapiens_antibody_embedding | Get vector representations and visualize them using t-SNE |
Acknowledgements
Sapiens is based on antibody repertoires from the Observed Antibody Space:
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018). Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires. The Journal of Immunology, 201(8), 2502–2509. https://doi.org/10.4049/jimmunol.1800708
