sceptr

Tests

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.

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},
}

Development Status
- 5 - Production/Stable
Intended Audience
- Science/Research
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3
Topic
- Scientific/Engineering

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 Nov 12, 2025 Source

Built Distribution

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

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

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

Uploaded Nov 12, 2025 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: Nov 12, 2025
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.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: sceptr-1.2.0.tar.gz
- Subject digest: 0f9e0b5630e0d032c95ecb316d6431374ef51e83be3d16b190139c87e3df5551
- Sigstore transparency entry: 698549707
- Sigstore integration time: Nov 12, 2025
Source repository:
- Permalink: yutanagano/sceptr@ea7f68cdce74c15bc8a2f776a4688f58eb9b2eb7
- Branch / Tag: refs/tags/v1.2.0
- Owner: https://github.com/yutanagano
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish_to_pypi.yaml@ea7f68cdce74c15bc8a2f776a4688f58eb9b2eb7
- Trigger Event: release