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---
license: mit
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
base_model:
- westlake-repl/ProTrek_650M_UniRef50
---
# ProTrek_650M_UniRef50_structure_encoder
This model is a SentenceTransformer-compatible version of ProTrek_650M_UniRef50_structure_encoder. It has been converted for use with the sentence-transformers library, enabling easy integration into semantic similarity tasks, such as semantic search, clustering, and feature extraction.

**Github repo: https://github.com/westlake-repl/ProTrek**  
**Hugging Face repo https://huggingface.co/westlake-repl/ProTrek_650M_UniRef50**

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 1026 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 1026, 'do_lower_case': False, 'architecture': 'EsmModel'})
  (1): Pooling({'word_embedding_dimension': 640, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 640, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
protein_encoder = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_protein_encoder")
text_encoder = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_text_encoder")
structure_encoder = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_structure_encoder")
# Run inference
ProTrek_650M_UniRef50_temperature = 0.0186767578
def sim(a, b): return (a @ b.T / ProTrek_650M_UniRef50_temperature).item()
aa_seq = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN"
text = 'Insulin decreases blood glucose concentration. It increases cell permeability to monosaccharides, amino acids and fatty acids. It accelerates glycolysis, the pentose phosphate cycle, and glycogen synthesis in liver.'
foldseek_seq = 'DVVVVVVVVVVVVCVVPPDDPVPPFDFDFDADVVLVVLLCVLLVPLAFDDDDPDPVVVVVVVVDDDPPDDDPPDPDPDPPVVVVVVVVDDCSVVRRVGIDGSVSSNVRGD'.lower()
seq_emb = protein_encoder.encode([aa_seq], convert_to_tensor=True)
text_emb = text_encoder.encode([text], convert_to_tensor=True)
struc_emb = structure_encoder.encode([foldseek_seq], convert_to_tensor=True)
print("Seq-Text similarity:", sim(seq_emb, text_emb))
print("Seq-Structure similarity:", sim(seq_emb, struc_emb))
print("Text-Structure similarity:", sim(text_emb, struc_emb))
```

## Overview
ProTrek is a multimodal model that integrates protein sequence, protein structure, and text information for better 
protein understanding. It adopts contrastive learning to learn the representations of protein sequence and structure. 
During the pre-training phase, we calculate the InfoNCE loss for each two modalities as [CLIP](https://arxiv.org/abs/2103.00020)
does.

## Model architecture
**Protein sequence encoder**:  [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D)

**Protein structure encoder**: foldseek_t30_150M (identical architecture with esm2 except that the vocabulary only contains 3Di tokens)

**Text encoder**:  [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.0.0
- Transformers: 4.53.2
- PyTorch: 2.2.1+cu121
- Accelerate: 
- Datasets: 
- Tokenizers: 0.21.2

## Citation
```
@article{su2024protrek,
  title={ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning},
  author={Su, Jin and Zhou, Xibin and Zhang, Xuting and Yuan, Fajie},
  journal={bioRxiv},
  pages={2024--05},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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	---
	license: mit
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	base_model:
	- westlake-repl/ProTrek_650M_UniRef50
	---

	# ProTrek_650M_UniRef50_structure_encoder
	This model is a SentenceTransformer-compatible version of ProTrek_650M_UniRef50_structure_encoder. It has been converted for use with the sentence-transformers library, enabling easy integration into semantic similarity tasks, such as semantic search, clustering, and feature extraction.

	Github repo: https://github.com/westlake-repl/ProTrek
	Hugging Face repo https://huggingface.co/westlake-repl/ProTrek_650M_UniRef50

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	<!-- - Base model: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: 1026 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 1026, 'do_lower_case': False, 'architecture': 'EsmModel'})
	(1): Pooling({'word_embedding_dimension': 640, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Dense({'in_features': 640, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
	(3): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	protein_encoder = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_protein_encoder")
	text_encoder = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_text_encoder")
	structure_encoder = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_structure_encoder")
	# Run inference
	ProTrek_650M_UniRef50_temperature = 0.0186767578
	def sim(a, b): return (a @ b.T / ProTrek_650M_UniRef50_temperature).item()

	aa_seq = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN"
	text = 'Insulin decreases blood glucose concentration. It increases cell permeability to monosaccharides, amino acids and fatty acids. It accelerates glycolysis, the pentose phosphate cycle, and glycogen synthesis in liver.'
	foldseek_seq = 'DVVVVVVVVVVVVCVVPPDDPVPPFDFDFDADVVLVVLLCVLLVPLAFDDDDPDPVVVVVVVVDDDPPDDDPPDPDPDPPVVVVVVVVDDCSVVRRVGIDGSVSSNVRGD'.lower()

	seq_emb = protein_encoder.encode([aa_seq], convert_to_tensor=True)
	text_emb = text_encoder.encode([text], convert_to_tensor=True)
	struc_emb = structure_encoder.encode([foldseek_seq], convert_to_tensor=True)
	print("Seq-Text similarity:", sim(seq_emb, text_emb))
	print("Seq-Structure similarity:", sim(seq_emb, struc_emb))
	print("Text-Structure similarity:", sim(text_emb, struc_emb))
	```

	## Overview
	ProTrek is a multimodal model that integrates protein sequence, protein structure, and text information for better
	protein understanding. It adopts contrastive learning to learn the representations of protein sequence and structure.
	During the pre-training phase, we calculate the InfoNCE loss for each two modalities as [CLIP](https://arxiv.org/abs/2103.00020)
	does.

	## Model architecture
	Protein sequence encoder: [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D)

	Protein structure encoder: foldseek_t30_150M (identical architecture with esm2 except that the vocabulary only contains 3Di tokens)

	Text encoder: [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Framework Versions
	- Python: 3.11.11
	- Sentence Transformers: 5.0.0
	- Transformers: 4.53.2
	- PyTorch: 2.2.1+cu121
	- Accelerate:
	- Datasets:
	- Tokenizers: 0.21.2

	## Citation
	```
	@article{su2024protrek,
	title={ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning},
	author={Su, Jin and Zhou, Xibin and Zhang, Xuting and Yuan, Fajie},
	journal={bioRxiv},
	pages={2024--05},
	year={2024},
	publisher={Cold Spring Harbor Laboratory}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->