AntPack is a Python package / toolkit for antibody numbering, data processing, statistical inference and machine learning for antibody sequences and TCRs. It is currently in active development, so we are adding new features and making more improvements periodically.
For usage for v0.3.9 and later, see the docs at this link.
You can find the docs here for older versions.
v0.4 contains a bug fix for VJ gene assignment for numbering schemes other than IMGT. If perforiming VJ gene assignment with numbering schemes other than IMGT, please prefer v0.4 to prior versions. v0.3.8.6 (open-source) has been updated with this bug fix as well.
The API for the SequenceScoringTool and humanization has been updated and streamlined so if you are scoring sequences for humanness or humanizing them please see the updated section of the docs.
v0.4 also contains capabilities for clustering antibody datasets ranging in size from a few dozen sequences to tens of millions using a variety of algorithms using any CDR or framework region of interest. (see below and see docs). These will be significantly expanded in the next version with further clustering and search capabilities and structure analysis-related tools.
Versions v0.3.9 and afterwards are licensed for academic and noncommercial use only; you must first obtain a free license key to use. You do not have to re-setup the key when upgrading AntPack in an existing venv or conda environment.
Versions prior to 0.3.9 were made available under the GPL, which means they can be used it for your own data analysis in any manner you wish whether you work in academia or industry, but any software built using AntPack and intended for sale or distribution must also be open-source under the GPL license. Therefore if you are currently using e.g. v0.3.8.6 for data analysis, you should feel free to continue to do so under the terms of your existing license. Also note that v0.3.8.6 is still available on PyPi and can be installed using:
pip install antpack==0.3.8.6
Please note that v0.3.8.6 contains a bug fix from v0.3.8.5 and should be preferred to v0.3.8.5.
Starting with v0.3.9, AntPack is only available for noncommercial academic use. To use AntPack versions v0.3.9 or later, you must first obtain a free license key. To install AntPack, run:
pip install antpack
AntPack-setup
then paste in the license key and your email address when prompted.
The only required dependency is numpy. If you want to run the GUI, there are two additional dependencies you'll need to install:
pip install pyside6 qt_material
If you don't plan to use the GUI, you don't need those dependencies.
AntPack is distributed as a wheel precompiled for most platforms and CPython >= 3.8.
Numbering antibody sequences is an important precursor for many statistical inference / machine learning applications. AntPack is orders of magnitude faster for numbering antibody sequences than existing tools in the literature (e.g. ANARCI, AbRSA), while providing >= reliability. AntPack also provides tools for merging a list of numbered sequences into an MSA, for easy extraction of specific CDRs and framework regions, and for TCR numbering.
Identifying the most similar human/mouse/llama/rabbit V / J gene sequences is useful for a variety of purposes. AntPack provides tools for determining which human V and J gene sequences are most similar to the variable region chain provided as input.
Minimizing the risk of immunogenicity is important for selecting clinical candidates. In AntPack v0.1.0, we introduce a simple, fully interpretable generative model for human heavy and light chains that outperforms all comparators in the literature on a large held-out test set for distinguishing human sequences from those of other species. This scoring tool can be used to score sequences for humanness, suggest modifications to make them more human, identify liabilities, and generate highly human sequences that contain selected motifs.
Some sequence motifs are known to be associated with developability issues -- certain motifs are known, for example, to be prone to N-glycosylation or deamidation. AntPack provides a tool for finding these "liability" motifs in an input sequence. Note that that identifying liabilities through finding motifs in this way is known to be prone to false positives (an N-glycosylation motif, for example, will not always be glycosylated). Still, these kinds of alerts can be useful for making yourself aware of potential developability issues.
If using AntPack in research intended for publication, please cite either the preprint:
Jonathan Parkinson and Wei Wang. 2024. For antibody sequence generative modeling, mixture models may be all you need. bioRxiv: https://doi.org/10.1101/2024.01.27.577555
or the final paper in Bioinformatics.
The authors & maintainers gratefully acknowledge the contributions of Japanese artist Yusuke Kamiyamane for the creation of the Fugue icon set.