Fast and performant TCR representation model

Navigation

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for yutanagano from gravatar.com yutanagano

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License
  • Author: Yuta Nagano
  • Maintainer: Yuta Nagano
  • Tags TCR , TR , T cell , transformer , bert , MLM , immunology , bioinformatics
  • Requires: Python >=3.9
  • Provides-Extra: dev , docs
Classifiers
Report project as malware

Project description

Latest release Tests Documentation Status License DOI

Check out the documentation page.
Graphical abstract. Traditional protein language models that are trained purely on masked-language modelling underperform sequence alignment models on TCR specificity prediction. In contrast, our model SCEPTR is jointly trained on masked-language modelling and contrastive learning, allowing it to outperform other language models as well as the best sequence alignment models to achieve state-of-the-art performance.

SCEPTR (Simple Contrastive Embedding of the Primary sequence of T cell Receptors) is a small, fast, and informative TCR representation model that can be used for alignment-free TCR analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery). Our manuscript demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with improved accuracy over previous methods.

SCEPTR is a BERT-like transformer-based neural network implemented in Pytorch. With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! And if your computer does have a CUDA-enabled GPU, the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle.

sceptr's API exposes four intuitive functions: calc_cdist_matrix, calc_pdist_vector, calc_vector_representations, and calc_residue_representations -- and it's all you need to make full use of the SCEPTR models. What's even better is that they are fully compliant with pyrepseq's tcr_metric API, so sceptr will fit snugly into the rest of your repertoire analysis workflow.

Installation

pip install sceptr

Citing SCEPTR

Please cite our manuscript.

BibTex

@article{nagano_contrastive_2025,
	title = {Contrastive learning of {T} cell receptor representations},
	volume = {16},
	issn = {2405-4712, 2405-4720},
	url = {https://www.cell.com/cell-systems/abstract/S2405-4712(24)00369-7},
	doi = {10.1016/j.cels.2024.12.006},
	language = {English},
	number = {1},
	urldate = {2025-01-19},
	journal = {Cell Systems},
	author = {Nagano, Yuta and Pyo, Andrew G. T. and Milighetti, Martina and Henderson, James and Shawe-Taylor, John and Chain, Benny and Tiffeau-Mayer, Andreas},
	month = jan,
	year = {2025},
	pmid = {39778580},
	note = {Publisher: Elsevier},
	keywords = {contrastive learning, protein language models, representation learning, T cell receptor, T cell specificity, TCR, TCR repertoire},
}

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for yutanagano from gravatar.com yutanagano

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License
  • Author: Yuta Nagano
  • Maintainer: Yuta Nagano
  • Tags TCR , TR , T cell , transformer , bert , MLM , immunology , bioinformatics
  • Requires: Python >=3.9
  • Provides-Extra: dev , docs
Classifiers

Release history Release notifications | RSS feed

This version

1.2.0

1.1.1

1.1.0.post2

1.1.0.post1

1.1.0

1.1.0b1 pre-release

1.0.1

1.0.0

1.0.0b1 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sceptr-1.2.0.tar.gz (11.1 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sceptr-1.2.0-py3-none-any.whl (10.9 MB view details)

Uploaded Python 3

File details

Details for the file sceptr-1.2.0.tar.gz.

File metadata

  • Download URL: sceptr-1.2.0.tar.gz
  • Upload date:
  • Size: 11.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for sceptr-1.2.0.tar.gz
Algorithm Hash digest
SHA256 0f9e0b5630e0d032c95ecb316d6431374ef51e83be3d16b190139c87e3df5551
MD5 dcbc6e257d657e35c966e3f84be0d937
BLAKE2b-256 bda42f534e3cab4a0336c7d8646140e4ba2cdc4717cd9cb5f76aa5cd77d9e708

See more details on using hashes here.

Provenance

The following attestation bundles were made for sceptr-1.2.0.tar.gz:

Publisher: publish_to_pypi.yaml on yutanagano/sceptr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sceptr-1.2.0-py3-none-any.whl.

File metadata

  • Download URL: sceptr-1.2.0-py3-none-any.whl
  • Upload date:
  • Size: 10.9 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for sceptr-1.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 aba652a9fb53861d5170cc1ad99a284852da8899328f4b4fd80e78f595d250d5
MD5 8ac19301bc55d972e00cf233d8ad3af7
BLAKE2b-256 8d8ffbca69c18292a8ce8fa1ee4bf9b2ccaf6b10bf93b025a0307d6c669c0355

See more details on using hashes here.

Provenance

The following attestation bundles were made for sceptr-1.2.0-py3-none-any.whl:

Publisher: publish_to_pypi.yaml on yutanagano/sceptr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page