SolvNet 1.0.14

Protein solubility prediction toolkit

# SolvNet

**Protein Solubility Prediction Toolkit**

**SolvNet** is a Python toolkit for predicting protein solubility from amino acid sequences. It provides easy-to-use functions to generate embeddings and predict solubility with confidence estimates.

---

## Installation

```bash
pip install SolvNet
```

### Step 0: Prepare Data

* Prepare your dataset or load a test dataset from pbdsolv.

```python
from SolvNet import load_pdb
df = load_pdb()
```

* The dataset should be a pandas DataFrame with the following columns:

| Column  | Description                                           |
|---------|-------------------------------------------------------|
| name    | Protein name                                         |
| aa_seq  | Amino acid sequence                                  |
| label   | Binary solubility label (`0`: insoluble, `1`: soluble) |


### Step 1: Generate ESMC Embeddings

Generate ESMC embeddings for the protein sequences:

```python
from SolvNet import esmc_embeddings
data = esmc_embeddings(df_test)
```

Parameters:

* `df_test`: Dataframe from step 0.

### Step 2: Predict Solubility

Use the generated embeddings to predict protein solubility:

```python
from SolvNet import Predict

labels, preds, confs, probs = Predict(
    data=data,
    percentage=0.85,
    batch_size=64
)
```

Parameters:

* `data:` ESMC embeddings.
* `percentage:` The top percentage of most confident predictions to return (default 0.85).
* `batch_size:` Batch size for prediction (default 64).

Returns:

* `labels:` True labels for the top percentage% most confident proteins.
* `preds:` Predicted labels for the top percentage%.
* `confs:` Confidence scores for predictions.
* `probs:` Predicted probabilities.

## Example Workflow
```python
from SolvNet import *

# Step 0: Load pdb dataset:
df = load_pdb()

# Step 1: Generate embeddings
data = esmc_embeddings(df)

# Step 2: Make predictions
labels, preds, confs, probs = Predict(
    data_pkl_path=data,
    percentage=0.85,
    batch_size=64
)
```
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## License
MIT License © 2025 Thao Nguyen et al.