netam 0.0.0

Neural network models for antibody affinity maturation

netam

Neural NETworks for antibody Affinity Maturation.

pip installation

Netam is available on PyPI, and works with Python 3.9 through 3.11.

pip install netam

This will allow you to use the models.

However, if you wish to interact with the models on a more detailed level, you will want to do a developer installation (see below).

Models

Thrifty model of somatic hypermutation

This model is described in:

Sung, Johnson, Dumm, Simon, Haddox, Fukuyama, Matsen IV. Thrifty wide-context models of B cell receptor somatic hypermutation. eLife. 2025 Mar. doi: 10.7554/elife.105471.1

The corresponding reproducible experiments are at matsengrp/thrifty-experiments-1; see that repo's README for additional dependencies.

Deep Natural Selection Model (DNSM)

This model is described in:

Matsen IV, Sung, Johnson, Dumm, Rich, Starr, Song, Bradley, Fukuyama, Haddox. A sitewise model of natural selection on individual antibodies via a transformer-encoder. Mol Biol Evol. 2025 Jul;42(8):msaf186. doi: 10.1093/molbev/msaf186

The corresponding reproducible experiments are at matsengrp/dnsm-experiments-mbe; see that repo's README for additional dependencies.

Pretrained models

Pretrained models will be downloaded on demand, so you will not need to install them separately.

The models are named according to the following convention:

ModeltypeSpeciesVXX-YY

where:

• Modeltype is the type of model, such as Thrifty for the "thrifty" SHM model or DNSM for Deep Natural Selection Models
• Species is the species, such as Hum for human
• XX is the version of the model
• YY is any model-specific information, such as the number of parameters

Available Models

Thrifty Models:

• ThriftyHumV0.2-20, ThriftyHumV0.2-45, ThriftyHumV0.2-59: SHM models trained on human data

DNSM Models:

• DNSMHumV1.0-1M: 1M parameter Deep Natural Selection Model trained on human data
• DNSMHumV1.0-4M: 4M parameter Deep Natural Selection Model trained on human data

If you need to clear out the cache of pretrained models, you can use the command-line call:

netam clear_model_cache

Usage

See the examples in the notebooks directory.

Developer installation

From a clone of this repository, install using:

python3.11 -m venv .venv
source .venv/bin/activate
make install

Note that you should be fine with an earlier version of Python. We target Python 3.9, but 3.11 is faster.

Troubleshooting

• On some machines, pip may install a version of numpy that is too new for the available version of pytorch, returning an error such as A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. The solution is to downgrade to numpy<2:

  pip install --force-reinstall "numpy<2"