AntPack is a toolkit for antibody numbering, data processing, statistical inference and machine learning for antibody sequences. For usage, see the docs.
In v0.3.5, we've added the Aho numbering scheme. We've made AntPack numbering about 30% faster (it was already the fastest numbering tool, but there's no such thing as too fast!) We've also improved VJ gene assignment. AntPack can now assign v- and j-genes using either percent identity or e-value, and if multiple V- or J-genes have essentially the same similarity to the query sequence, it will return a list of them rather than trying to assign a single V or J gene (which doesn't make much sense in that case).
The API has been slightly redesigned. This may cause some breaking changes, mainly for VJ gene assignment. Our apologies for any inconvenience -- the API should be stable from here on out. Finally, for numbering and VJ gene assignment, AntPack now accepts sequences which contain the letter 'X' (although we suggest using this only if necessary -- an alignment with all AAs will generally be better quality).
Finally, AntPack now has a command line tool for running a quick standard analysis on small-medium size datasets. For more customized analyses and/or larger datasets the Python API can be used as before.
Python3.8 or later is now required.
Starting in v0.4 we're planning to add a GUI -- currently in active development -- which makes it easy to load and view sequences if you only have a few. We're also planning to add additional functionality for antibody analysis -- coming soon!
pip install antpack
AntPack is distributed as a wheel precompiled for most platforms and CPython >= 3.8, so installation should be very straightforward. A source distribution is also available (C++17) in case there is any need to compile from source.
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 and for easy extraction of specific CDRs and framework regions.
Identifying the most similar human 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.
AntPack is licensed under a GPL license, which means that you are free to use it for your own data analysis irrespective of whether you work on academic research or industrial R&D / QC.
If you are writing software intended for sale / distribution, however, any software you create that uses AntPack must also be open-source, must use the GPL license and must acknowledge AntPack appropriately. If you are interested in using AntPack in a closed-source software product, please contact us to obtain a version of AntPack licensed for this use.
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.