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lonelycrab88/
BiooBang-1.0-HEK293T

Name: BiooBang-1.0-HEK293T
Availability: InStock

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BiooBang is an advanced biological language model designed to integrate protein amino acid sequences and mRNA coding sequences (CDS) within a unified framework. Built on a Transformer-based prefix decoder architecture, BiooBang leverages the principles of natural language processing to treat protein and CDS sequences as biological “languages”, enabling self-supervised learning for comprehensive training.

Note

This model was fine-tuned specifically for codon optimization in the HEK293T cell line.

Use Case

The source code corresponding to this model is publicly available on GitHub at: https://github.com/lonelycrab888/BiooBang

After installing BiooBang, you can use:

python

# ========== generate CDS
import torch
from model.tokenization_UniBioseq import UBSLMTokenizer
from model.modeling_UniBioseq import UniBioseqForCausalLM
from model.UBL_utils import CodonLogitsProcessor

from transformers.generation.logits_process import LogitsProcessorList

tokenizer = UBSLMTokenizer.from_pretrained("lonelycrab88/BiooBang-1.0-HEK293T")
model = UniBioseqForCausalLM.from_pretrained("lonelycrab88/BiooBang-1.0-HEK293T", device_map='auto')

protein_prompt = "MASSDKQTSPKPPPSPSPLRNSKFCQSNMRILIS"
input_ids = torch.tensor([tokenizer.encode(input_protein)+[36]]).to(model.device)
max_length = 4*len(input_protein)+6

logits_processor = LogitsProcessorList()
logits_processor.append(CodonLogitsProcessor(input_protein, tokenizer, len(input_protein)))
result = model.generate(input_ids, max_length = max_length, num_beams = 10, logits_processor=logits_processor, low_memory=True, num_return_sequences=1)
result_CDS_tok = tokenizer.decode(result[0][len(input_protein)+3:].tolist()).replace(" ","").upper()

Citing this Work

Please cite our paper:

bibtex

@article {Zhao2024.10.24.620004,
	author = {Zhao, Heng-Rui and Cheng, Meng-Ting and Zhu, Jinhua and Wang, Hao and Yang, Xiang-Rui and Wang, Bo and Sun, Yuan-Xin and Fang, Ming-Hao and Chen, Enhong and Li, Houqiang and Han, Shu-Jing and Chen, Yuxing and Zhou, Cong-Zhao},
	title = {Integration of protein and coding sequences enables mutual augmentation of the language model},
	elocation-id = {2024.10.24.620004},
	year = {2024},
	doi = {10.1101/2024.10.24.620004},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/10/29/2024.10.24.620004},
	eprint = {https://www.biorxiv.org/content/early/2024/10/29/2024.10.24.620004.full.pdf},
	journal = {bioRxiv}
}

Contacts

If you’re interested in other cell lines and open to collaboration, please don’t hesitate to contact us!