Sapiens is a human antibody language model based on BERT.

Last update: Nov 20, 2022

Overview

Sapiens: Human antibody language model

    ____              _                
   / ___|  __ _ _ __ (_) ___ _ __  ___ 
   \___ \ / _` | '_ \| |/ _ \ '_ \/ __|
    ___| | |_| | |_| | |  __/ | | \__ \
   |____/ \__,_|  __/|_|\___|_| |_|___/
               |_|

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 sapiens

❗️ Python 3.7 or 3.8 is currently required due to fairseq bug in Python 3.9 and above: pytorch/fairseq#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:

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

Sapiens is a human antibody language model based on BERT.

Related tags

Overview

Sapiens: Human antibody language model

Features

Usage

Antibody sequence infilling

Suggesting mutations

Antibody sequence embedding

Notebooks

Acknowledgements

Owner

Merck Sharp & Dohme Corp. a subsidiary of Merck & Co., Inc.

Repositório da disciplina no semestre 2021-2

GPT-3: Language Models are Few-Shot Learners

A Multi-modal Model Chinese Spell Checker Released on ACL2021.

Healthsea is a spaCy pipeline for analyzing user reviews of supplementary products for their effects on health.

Grading tools for Advanced NLP (11-711)Grading tools for Advanced NLP (11-711)

An ultra fast tiny model for lane detection, using onnx_parser, TensorRTAPI, torch2trt to accelerate. our model support for int8, dynamic input and profiling. (Nvidia-Alibaba-TensoRT-hackathon2021)

Using BERT-based models for toxic span detection

Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"

Blackstone is a spaCy model and library for processing long-form, unstructured legal text

Experiments in converting wikidata to ftm

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

NLP Overview

Unofficial Python library for using the Polish Wordnet (plWordNet / Słowosieć)

FireFlyer Record file format, writer and reader for DL training samples.

NumPy String-Indexed is a NumPy extension that allows arrays to be indexed using descriptive string labels

easySpeech is an open-source Python wrapper for google speech to text API that doesn't require PyAudio(So you especially windows user don't have to deal with the errors while installing PyAudio) and also works with hugging face transformers

spaCy plugin for Transformers , Udify, ELmo, etc.

ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.

Türkçe küfürlü içerikleri bulan bir yapay zeka kütüphanesi / An ML library for profanity detection in Turkish sentences

Host your own GPT-3 Discord bot