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.

This is an incredibly powerful calculator that is capable of many useful day-to-day functions.

Some embedding layer implementation using ivy library

COVID-19 Chatbot with Rasa 2.0: open source conversational AI

Chinese named entity recognization (bert/roberta/macbert/bert_wwm with Keras)

Twitter-Sentiment-Analysis - Twitter sentiment analysis for india's top online retailers(2019 to 2022)

A PyTorch implementation of VIOLET

Estimation of the CEFR complexity score of a given word, sentence or text.

Code for Findings at EMNLP 2021 paper: "Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning"

Paradigm Shift in NLP - "Paradigm Shift in Natural Language Processing".

Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP

This repository contains the code for "Generating Datasets with Pretrained Language Models".

STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums.

The SVO-Probes Dataset for Verb Understanding

A highly sophisticated sequence-to-sequence model for code generation

NLP Core Library and Model Zoo based on PaddlePaddle 2.0

Implementation of ProteinBERT in Pytorch

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

ConferencingSpeech2022; Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge

A Python script that compares files in directories

AEC_DeepModel - Deep learning based acoustic echo cancellation baseline code