sapiens 1.0.0

Sapiens: Human antibody language model based on BERT

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

Usage

Install Sapiens using pip:

pip install git+https://github.com/Merck/Sapiens

# pip install sapiens - coming soon (see ticket https://github.com/pypa/pypi-support/issues/1651)

Antibody 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)
# QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS

Suggesting 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