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---
tags:
- drug-discovery
- ibm
- mammal
- pytorch
- TCR
- epitope
- affinity
- safetensors
- biomed-multi-alignment
license: apache-2.0
library_name: biomed-multi-alignment
base_model:
- ibm/biomed.omics.bl.sm.ma-ted-458m
---
T-cell receptor (TCR) binding to immunogenic peptides (epitopes) presented by major
histocompatibility complex (MHC) molecules is a critical mechanism in the adaptive
immune system, essential for antigen recognition and triggering immune responses.
The T-cell receptor (TCR) repertoire exhibits considerable diversity, consisting of an
α-chain and a β-chain that function together to enable T cells to recognize a wide
array of epitopes. The β-chain is especially significant, as it is crucial for the early
stages of T-cell development and possesses greater variability, which enhances the
TCR’s capacity to identify diverse pathogens effectively. However, understanding the
specific interactions between TCRs and epitopes remains a significant challenge due
to the vast variability in TCR sequences. Accurate prediction of TCR-peptide binding
from sequence data could revolutionize immunology by offering deeper insights
into a patient’s immune status and disease history. This capability holds potential
applications in personalized immunotherapy, early diagnosis, and the treatment of
diseases such as cancer and autoimmune disorders. In silico tools designed to model
TCR-peptide interactions could also facilitate the study of therapeutic T-cell efficacy
and assess cross-reactivity risks, presenting a transformative opportunity for precision
medicine.
The benchmark defined in: https://academic.oup.com/bioinformatics/article/37/Supplement_1/i237/6319659?login=false
Data retrieved from: https://tdcommons.ai/multi_pred_tasks/tcrepitope
## Model Summary
- **Developers:** IBM Research
- **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment
- **Paper:** https://arxiv.org/abs/2410.22367
- **Release Date**: Oct 28th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## Usage
Using `ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind` requires installing https://github.com/BiomedSciAI/biomed-multi-alignment
```
pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
```
A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind`:
from mammal.examples.tcr_epitope_binding.main_infer import load_model, task_infer
tcr_beta_seq = "NAGVTQTPKFQVLKTGQSMTLQCAQDMNHEYMSWYRQDPGMGLRLIHYSVGAGITDQGEVPNGYNVSRSTTEDFPLRLLSAAPSQTSVYFCASSYSWDRVLEQYFGPGTRLTVT"
epitope_seq = "LLQTGIHVRVSQPSL"
model_inst, tokenizer_op = load_model(device="cpu")
result = task_infer(
model=model_inst,
tokenizer_op=tokenizer_op,
tcr_beta_seq=tcr_beta_seq,
epitope_seq=epitope_seq,
)
print(f"The prediction for {epitope_seq} and {tcr_beta_seq} is {result}")
See our detailed example at: on `https://github.com/BiomedSciAI/biomed-multi-alignment`
## Citation
If you found our work useful, please consider giving a star to the repo and cite our paper:
```
@misc{shoshan2024mammalmolecularaligned,
title={MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language},
author={Yoel Shoshan and Moshiko Raboh and Michal Ozery-Flato and Vadim Ratner and Alex Golts and Jeffrey K. Weber and Ella Barkan and Simona Rabinovici-Cohen and Sagi Polaczek and Ido Amos and Ben Shapira and Liam Hazan and Matan Ninio and Sivan Ravid and Michael M. Danziger and Joseph A. Morrone and Parthasarathy Suryanarayanan and Michal Rosen-Zvi and Efrat Hexter},
year={2024},
eprint={2410.22367},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2410.22367},
}
``` |