README.md

---
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)

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### Downstream Usage (Sentence Transformers)

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## 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}
}
```

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