Awesome

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

Sapiens Antibody t-SNE Example

Usage

Install Sapiens using pip:

# Recommended: Create dedicated conda environment
conda create -n sapiens python=3.8
conda activate sapiens
# Install Sapiens
pip install sapiens

❗️ Python 3.7 or 3.8 is currently required due to fairseq bug in Python 3.9 and above: https://github.com/pytorch/fairseq/issues/3535

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:

<table> <tr><th>Links</th><th>Notebook</th><th>Description</th></tr> <tr> <td> <a href="https://mybinder.org/v2/gh/Merck/Sapiens/main?labpath=notebooks%2F01_sapiens_antibody_infilling.ipynb"><img src="https://mybinder.org/badge_logo.svg" /></a> </td> <td><a href="notebooks/01_sapiens_antibody_infilling.ipynb">01_sapiens_antibody_infilling</a></td> <td>Predict missing positions in an antibody sequence</td> </tr> <tr> <td> <a href="https://mybinder.org/v2/gh/Merck/Sapiens/main?labpath=notebooks%2F02_sapiens_antibody_embedding.ipynb"><img src="https://mybinder.org/badge_logo.svg" /></a> </td> <td><a href="notebooks/02_sapiens_antibody_embedding.ipynb">02_sapiens_antibody_embedding</a></td> <td>Get vector representations and visualize them using t-SNE</td> </tr> </table>

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